Host Based Intrusion Detection System Using Python — Complete Documentation & Simulation

Overview & System Context

Host Based Intrusion Detection System Using Python: Comprehensive Web-Sourced Technical Investigation Exhaustive Research Dossier · Pure Web-Scraped Content: ~9579 Words · Real Software Screenshots: 1 1. Executive Overview & Technical Background OSSEC+ provides additional capabilities to the basic OSSEC version such as the Machine Learning System for those that simply register. The cost is still free but OSSEC+ does more! OSSEC+ provides additional capabilities to the basic OSSEC version such as the Machine Learning System for those that simply register. The cost is still free but OSSEC+ does more! OSSEC+ provides additional capabilities to the basic OSSEC version such as the Machine Learning System for those that simply register.

Machine Learning * ELK stack * OpenSearch * Real Time Threat Intel * 1000s of new rules * Wazuh Alternative Figure 1: Video Demonstration & Practical Interface Overview Welcome to the official OSSEC website. Here you can find free OSSEC open-source host-based intrusion detection system (HIDS) downloads and community support . Atomicorp manages the OSSEC project and actively develops OSSEC. See OSSEC Development Timeline . The foundational OSSEC HIDS provides multi–platform log-based intrusion detection, file integrity monitoring (FIM) , malware detection, active response, compliance auditing, and more.

You can download the official version of OSSEC directly from the OSSEC downloads page . Our site is the trusted authority for OSSEC, and we provide both the free source code and ready-to-use binaries so you don’t have to compile it yourself. Yes. OSSEC is a free, open-source host-based intrusion detection system (HIDS). It has been trusted by security teams worldwide for decades, and you can use it at no cost. Learn more or download the rules .

OSSEC is the free, open-source version. Atomic OSSEC is a commercial-grade enhancement of OSSEC that includes enterprise features including centralized endpoint firewall management, vulnerability management, compliance scanning, SIEM, EDR, antivirus, advanced HIDS, real-time file integrity monitoring (FIM) and the reduction of false positives and false negatives through machine learning (ML). Atomic OSSEC also provides a graphical interface (GUI), advanced reporting, and professional support. Explore Atomic OSSEC .

OSSEC+ is the enhanced version of OSSEC that includes additional features such as threat intelligence and hundreds of more rules than basic OSSEC. By registering for OSSEC+ on the OSSEC downloads page , you gain access to OSSEC+ at no cost, giving you more capabilities than the standard open-source release. Yes. Atomic OSSEC includes OSSEC+ and more, including hundreds of additional out-of-the-box rules, machine learning, AV, endpoint firewall management, vulnerability management, compliance scanning, built-in software integrations, a GUI, reporting tools and more, making Atomic OSSEC the most advanced version of OSSEC.

Atomic OSSEC is the most advanced version of OSSEC. Atomic OSSEC contains 10X the security and compliance rules of free OSSEC, plus real-time file integrity monitoring (FIM) and malware detection, endpoint firewall management, antivirus, extended vulnerability detection and remediation, machine learning and automated response, full SIEM, dashboard GUI and more. Visit the webpage or get a demo .

No. While the source code is freely available for those who want it, OSSEC.net also provides pre-compiled binaries for popular platforms. You can find them on the OSSEC downloads page . Atomicorp.com also provides commercially supported pre-compiled binaries for a wide range of platforms including signed Microsoft and macOS binaries and for end of life and legacy systems including Solaris, AIX, older versions of Linux and more.

OSSEC is a good fit for individuals and small teams looking for a basic HIDS framework and core ruleset; OSSEC+ (free with registration) enhances the core with hundreds of additional rules and threat intelligence; and Atomic OSSEC delivers enterprise-grade detection, advanced features—including thousands of additional rules, antivirus, endpoint firewall policy management and microsegmentation, EDR, SIEM, machine learning, compliance tools, vulnerability management, reporting, management GUI, and professional support.

OSSEC.net is the official home and authority for the OSSEC project. Downloading directly from the OSSEC downloads page ensures you are getting the latest, verified, and most secure version of OSSEC. With the downloads, you get the binary for your platform instead of just the source code to compile. Although if you want the source code, ossec.net has that too. OSSEC runs on all major operating systems, including Windows, Linux, FreeBSD, OpenBSD, Unix, and macOS. Atomic OSSEC provides additional capabilities for these platforms that the free OSSEC does not, as well as advanced support for AIX, Solaris, HP-UX, and end of life systems. These extra features include AV, SIEM, real-time FIM, endpoint firewall management, WAF, vulnerability management, compliance reporting, and more. Learn more or download OSSEC .

The Atomic OSSEC EDR supports a wider variety of operating systems and platforms than OSSEC. These include AIX, Solaris, Linux, Windows, and macOS as well as cloud platforms such as AWS, Azure, GCP, and more. Atomic OSSEC supports every major architecture these platforms run on, including SPARC, x86/x86-64 (Intel/AMD), ARM, and POWER and binary downloads are available for them as well. View the full list here .

Atomic OSSEC is Atomicorp’s extended detection and response (XDR) and compliance solution. Atomic OSSEC expands OSSEC with thousands of advanced security and compliance rules, real-time FIM, a management dashboard GUI, daily updates, new platform integrations, and expert support. It delivers change detection and intrusion prevention for cloud, server, container and OT environments, plus compliance controls for PCI DSS, HIPAA, NIST 800-53, NIST 800-171, NIST 800-82, FedRAMP, and other standards—all in one versatile solution. It’s an effective, affordable alternative to open source and Wazuh OSSEC deployments. Visit the Atomic OSSEC page .

The Atomic OSSEC option is available for just under $5 per device per month. Ask about our agentless deployments or volume discounts. Atomic OSSEC offers comprehensive host-based intrusion detection and protection across multiple platforms including Linux, Solaris, AIX, HP-UX, Windows, Mac and VMware ESX. Atomic OSSEC expands and deepens HIDS into extended detection and response (XDR), with endpoint and cloud workload protection that inspects software components from the open source and commercial software supply chain.

Atomic OSSEC FIM empowers organizations and agencies to monitor and automatically examine file and system changes, to alert rapid response rules and security administrators to anomalies and malicious signatures. Atomic OSSEC makes continuous and real-time file and system integrity monitoring easy so you can take OSSEC FIM beyond NIST and PCI compliance into proactive threat response.

Get PCI DSS compliant with Atomic OSSEC. We help organizations meet 100s of PCI DSS security controls. Atomic OSSEC detects and alerts on unauthorized file system modifications and malicious behaviors that could make you non-compliant. Manage your OSSEC PCI compliance in a GUI. Need an OSSEC dashboard GUI? Atomicorp provides Atomic Inspector, which enables you to collect, categorize, and understand SIEM data from multi-platform logs. Atomic Inspector, which is included in Atomic OSSEC, is powered by machine learning analysis and the GUI’s visualization and OpenSearch technology, so you can drill into the things that matter most.

Atomic OSSEC supports current and legacy and end of life (EOL) versions of Linux and Windows, AIX, Solaris, and more. Be able to modernize security on your OSSEC legacy systems. We support RHEL 5-7, Windows XP, Windows 2003, and Windows 7, Ubuntu 16-22, Solaris 10 and 11, HP-UX 10 and 11i, CentOS 5-7, and AIX 7.1, 7.2 and 7.3. Get more out of your OSSEC host-based intrusion detection system (HIDS) with answers to FAQs, video demonstrations, documentation, and professional support and services.

Atomic OSSEC extends detection, response, and vulnerability management, with dashboards for compliance and more. Watch the overview video. Get a demonstration of commerical-grade Atomic OSSEC now. Schedule your demonstration with sales now. Take the next step and begin a free 14-day trial period using Atomic OSSEC Software as a Service (SaaS) to protect up to 10 endpoints. No credit card required.

Get a pricing quote for Atomic OSSEC before starting a trial period. (Atomic OSSEC is available as an on-premise deployment or SaaS offering.) Once you install Atomic OSSEC, there is no need to upgrade to another ruleset or pay additionally for support. Prices start as low as $5 per agent per month. Atomic OSSEC is also available in a SaaS version for enterprises and as an integrated, white-label XDR solution for managed service security providers (MSSPs).

Atomicorp extends the power of OSSEC through extended security features that enable both detection and protection; with an easy-to-use, powerful OSSEC GUI; and full product support. Visit Atomicorp to learn more about Atomicorp Enterprise OSSEC . A comprehensive HIDS setup guide for cybersecurity professionals. Learn to install, configure, and maintain Host-Based Intrusion Detection systems for robust endpoint security.

Implementing Host-Based Intrusion Detection (HIDS) involves deploying software agents on individual endpoints to monitor system logs, file integrity, process execution, and other host-specific activities, forwarding this data to a central manager for analysis and alerting on suspicious behavior. This guide details the essential steps for setting up a robust HIDS solution, focusing on practical implementation and configuration.

HIDS provides a critical layer of defense by focusing on the internal activities of a host, offering deep visibility into potential compromises that network-based systems might miss. Unlike Network-Based Intrusion Detection Systems (NIDS) that monitor network traffic, HIDS operates directly on the operating system, observing changes to critical system files, registry entries, user activity, and application logs. This granular visibility is crucial for detecting unauthorized access, malware infections, privilege escalation attempts, and policy violations.

A typical HIDS architecture comprises several interconnected components working in concert: The market offers various HIDS solutions, ranging from open-source options to commercial enterprise platforms. The choice depends on your organization's specific needs, budget, scale, and integration requirements. Before initiating the HIDS setup, thorough planning is essential to ensure a successful deployment and optimal performance.

This section provides a generic, yet detailed, step-by-step guide for setting up a HIDS. While specific commands may vary slightly between solutions, the underlying principles remain consistent. We will reference Wazuh/OSSEC as a common open-source example. 2. System Architecture & Core Working Principles As outlined in the pre-setup considerations, this initial phase is critical. Document your chosen HIDS solution, the number and type of agents, network topology, and security objectives. Before deploying HIDS agents, it is prudent to conduct an internal network scan using tools similar to what ScanSearch offers to map out your asset landscape and identify potential blind spots or misconfigurations. This helps ensure comprehensive HIDS coverage.

The HIDS manager is the central hub. Its installation typically involves setting up an operating system, installing the HIDS software, and configuring its core services. For Wazuh, this often involves installing the Wazuh manager, Elasticsearch (or OpenSearch) for data storage, and Kibana (or OpenSearch Dashboards) for the UI.

Agents need to be registered with the manager to establish a secure communication channel and exchange encryption keys. Familiarize yourself with the default rules provided by your HIDS. These cover common operating system events, known attack patterns, and compliance requirements. Develop custom rules to detect specific threats relevant to your environment, applications, or business logic. Custom rules are often placed in a separate file (e.g., `/var/ossec/etc/rules/local_rules.xml` for Wazuh) to prevent them from being overwritten during updates.

For centralized security operations, integrate your HIDS with a Security Information and Event Management (SIEM) or Security Orchestration, Automation, and Response (SOAR) platform. Ensure that relevant personnel are notified of critical security events and that compliance reports can be generated. After initial setup, rigorously test your HIDS to ensure it is functioning correctly and detecting expected threats.

Beyond the core setup, consider leveraging advanced capabilities and adhering to best practices to maximize your HIDS's effectiveness. It is important to understand that HIDS and NIDS are not mutually exclusive; they are complementary. A NIDS monitors network traffic for suspicious patterns, while a HIDS monitors the internal activities of an endpoint. Together, they provide a much stronger defense posture. NIDS might detect an external scanning attempt, while HIDS would detect if that scan successfully led to a compromise and subsequent internal activity on the host.

Implementing a Host-Based Intrusion Detection System is a fundamental step towards enhancing your organization's cybersecurity posture. By providing deep visibility into endpoint activities, HIDS enables proactive detection of threats, aids in compliance, and supports robust incident response. The setup process, while requiring careful planning and execution, yields significant security dividends.

Use ScanSearch to discover exposed services, open ports, and security vulnerabilities across your infrastructure. XDR (Extended Detection and Response) unifies security data across endpoints, network, cloud, and identity to provide holistic threat detection, investigation, and automated response. It addresses the limitations of siloed tools, offering superior context and faster incident resolution for next-generation monitoring.

Zero Trust Security operates on "never trust, always verify," mandating strict identity verification for every access attempt, regardless of network location. This model counters traditional perimeter flaws by eliminating implicit trust, focusing on granular, identity-centric defense for modern threats. An Intrusion Detection System (IDS) monitors network or system activities for malicious behavior and policy violations, alerting administrators to potential security breaches. This article details the types of IDS, their operational methodologies, and best practices for deployment and management.

Endpoint security protects end-user devices like laptops, servers, and mobile phones from cyber threats, acting as the last line of defense beyond the traditional network perimeter. It integrates technologies such as EDR, XDR, and DLP to detect, prevent, investigate, and respond to advanced attacks. Wazuh is an open-source, free, and enterprise-ready security monitoring platform that unifies SIEM, EDR, and XDR capabilities. It provides comprehensive threat detection, visibility, and compliance for various environments, offering a cost-effective solution for robust cybersecurity.

A technical comparison of Cloudflare, Google, Quad9, AdGuard, and OpenDNS. Learn about latency, DoH/DoT support, and how to detect DNS hijacking. This is the first of some posts that’ll walk you through setting up, tuning and maintaining your very own host-based intrusion detection system. This will provide you with a powerful security monitoring platform to help identify and protect your servers and networks from potential threats. These could include an external attacker trying to brute force a valid set of credentials on one of your Internet facing servers, or a colleague creating a domain account with excessive privileges.

Let’s start off by defining what I mean by a host-based intrusion detection system or the more commonly known acronym, HIDS. A HIDS is an application that monitors the internals of a computer system, such as configuration files and log files, constantly looking for malicious activity or policy violations and if it detects such an event then it will fire an alert, such as sending an email.

In this guide we’ll be looking at the software OSSEC. This freely available IDS currently maintained by Trend Micro can be rolled out across a heterogeneous network, scales well, and provides both passive and active monitoring in the following key areas: While an IDS is usually known to just watch passively and if it detects an attack it will produce an associated alert, OSSEC can also work actively, so when an attack is detected it can take action such as blocking that particular IP address. This type of reactive security technology is often classed as an Intrusion Prevention System (IPS) and can be very useful in thwarting a potential attack.

The OSSEC architecture in its basic form consists of a OSSEC server that is used to manage as well as collate and analyse data received from the connected agents. An agent is any computer system (e.g. Windows 2008) that has the OSSEC agent software installed and configured to communicate back to the central OSSEC server. The server component of this platform needs to run on a UNIX based OS that includes gnu make, gcc and libc to compile the software. We are choosing to compile OSSEC from source for maximum flexibility as we’ll discover later in the blog series.

In this instance to keep things simple I am going to use the well-known Linux distribution known as Ubuntu. In this tutorial I’ll be using Ubuntu Server 14.04.4 LTS which is freely available and well supported. Once you’ve got a minimal Ubuntu server up and running with a static IP address configured and Internet access (this should be restricted to just the Ubuntu update servers once installation is complete), we can install the pre-requisites needed to compile OSSEC. To do this, run the following from the command line: Once completed we can download and extract the latest version of OSSEC (make sure it’s the latest version): You should now be able to change to the newly expanded installation directory and run the installation script: You’ll now be guided through the installation process. Select the defaults values where possible (these are shown in the square brackets), and choose “server” when prompted and provide a suitable e-mail address for server alerts to be sent. Note that you will need to allow the OSSEC server to relay email via the designated email server.

After the script completes you should have a working OSSEC server, so let’s now start the OSSEC server with the following command: At this point we should have a working OSSEC server, however it won’t be able to detect activity on other systems unless we install an agent (that acts a remote sensor).

The OSSEC agent software is available for both Windows and UNIX systems, and is fairly straight forward to configure. It is worth noting that the agent will communicate with the server over UDP port 1514, so make sure communication is permitted at the network level. First let’s download the Windows binary onto the server you wish to monitor using the link below: https://bintray.com/artifact/download/ossec/ossec-hids/ossec-agent-win32-2.8.3.exe On running this executable, you’ll be guided through the installation where you can accept the default values.

As the message above suggests, we need to configure an authentication key. To do this, hop back to the OSSEC server and run the following command: 3. Software Implementation, Frameworks & Protocols Let’s add the details for the agent we just installed by selecting ‘A’. You’ll then be prompted for an arbitrary server name, and then an IP address. Assuming it’s a static IP, add that, otherwise you can add an IP range such as ‘192.168.0.0/24’. This just configures where OSSEC data for that agent is expected to originate.

Finally use the default agent ID by hitting the ‘return’ key and select the ‘y’ key to confirm adding. Now you’ll be back at the main menu, and you can extract the authentication key that is needed by the Windows server by selecting ‘E’. Then type in the ID, for example ‘001’ to extract the base64 encoded authentication key. You’ll need to copy and paste into the “OSSEC Agent Manager” window on the Windows server as well as typing in the OSSEC server’s IP address, and then click ‘Save’. You’ll get a confirmation response like this: After first installing the OSSEC agent, it won’t be running, so let’s sort that out by clicking the ‘Manage’ menu, then ‘Start OSSEC’. It’s worth mentioning that I found the ‘Status’ of the agent didn’t update immediately! Now jump back to the server, and let’s review the status OSSEC agent using the following command: You should now see the Windows server is showing “Active” that means the OSSEC agent was able to successfully authenticate and communicate with the OSSEC server: Let’s test that everything works by purposely generating an alert. One of the many rules enabled by default is generating an alert when a user logs into a system for the very first time. So let’s do just that by RDPing onto the Windows server.

As you’ll discover with your new OSSEC platform, there’s a large number of predefined rules enabled with the default configuration that we’ll need to tune. Next time we’ll dive into OSSEC rules, looking at how they work and how we can tweak the default ones to filter out the stuff we don’t care about and prioritise the alerts we do, and even look at creating our own.

configuring-host-based-intrusion-detection guide for setting up HIDS with Wazuh, OSSEC, or AIDE to monitor file integrity, system changes, and compliance-focused endpoint security for Security Audit workflows. This skill scores 78/100, which means it is a solid listing candidate for Agent Skills Finder. Directory users can reasonably expect a real, install-worthy HIDS workflow for Wazuh/OSSEC/AIDE configuration and alert handling, though they should note that the repo is stronger on guided procedures than on turnkey installation automation.

The configuring-host-based-intrusion-detection skill helps you set up host-based intrusion detection on endpoints so you can track file integrity, system changes, suspicious behavior, and policy violations. It is aimed at people deploying or tuning Wazuh, OSSEC, or AIDE, especially when the goal is compliance-grade monitoring rather than generic security hardening. Use this configuring-host-based-intrusion-detection guide if you need a practical path for endpoint security, centralized alerting, or file integrity monitoring for Security Audit work. It is a strong fit for security engineers, SOC analysts, and admins who need a repeatable HIDS setup across Linux or Windows systems.

This skill is not just about installing an agent. It is centered on deployment choices that affect detection quality: what to monitor, what to exclude, how to establish a baseline, and when to suppress noisy alerts. That matters because HIDS projects usually fail from bad scoping, not from missing tooling. Install with npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill configuring-host-based-intrusion-detection . Then read SKILL.md first, followed by references/standards.md , references/workflows.md , and references/api-reference.md . Use assets/template.md when you need a deployment worksheet or an implementation checklist.

For best configuring-host-based-intrusion-detection usage, don’t ask for “set up HIDS” in the abstract. Tell it the platform, endpoint mix, compliance goal, and rollout scope. A stronger prompt looks like: “Configure Wazuh FIM for 25 Linux servers and 12 Windows workstations, keep /etc and C:\Windows\System32 , exclude log rotation paths, and align with PCI DSS 11.5.” A useful configuring-host-based-intrusion-detection install and usage flow is: define assets and compliance target, choose Wazuh/OSSEC/AIDE, establish a baseline window, tune exclusions, then connect alerts to your SIEM or review process. If you skip baseline and exclusions, the first output will usually be too noisy to trust.

Look at scripts/agent.py if you need API-driven agent management, and scripts/process.py if you want alert parsing or reporting logic. The references also show the practical shape of the skill: Wazuh API endpoints, osquery tables, OSSEC rule ranges, and standards mappings. That helps you judge whether the skill matches your environment before you adopt it. No. Wazuh is the most explicit path in the repository, but the skill also covers OSSEC and AIDE. If your stack is another HIDS or EDR product, the skill may still help with file integrity monitoring concepts, but the implementation details will not transfer cleanly.

Do not use configuring-host-based-intrusion-detection if you are really looking for network IDS, perimeter packet inspection, or full EDR deployment. It is also a poor fit if you have no endpoint admin access, no manager/server ready, or no plan for tuning false positives after rollout. Yes. The skill is especially relevant for Security Audit and compliance work because it maps to file integrity monitoring, event logging, and endpoint change detection. If you need evidence for PCI DSS, NIST, HIPAA, or ISO 27001-style controls, this skill gives you a more direct path than a generic prompt.

Yes, if the goal is guided deployment rather than deep product engineering. Beginners should start with the workflow and template files, then ask for a narrow scope such as one platform, one endpoint group, and one monitoring objective. Broad, mixed-environment prompts create avoidable confusion. The best way to improve configuring-host-based-intrusion-detection skill output is to specify what is monitored, what is excluded, and what counts as normal change. Include directories, event types, and maintenance windows. For example: “Monitor /etc , /usr/bin , and /usr/sbin , exclude resolv.conf , and treat patch windows as authorized changes.” Tell the skill whether you need deployment instructions, a baseline plan, a tuning strategy, or an investigation workflow. The same HIDS stack can produce very different results depending on the job: rollout to pilot hosts, compliance evidence, alert triage, or SIEM integration. Clear intent improves both setup quality and response usefulness.

A common failure mode is over-monitoring system paths without exclusions or baselining. Improve results by asking for a phased rollout, a false-positive suppression plan, and a short list of high-value rules first. If you are using the configuring-host-based-intrusion-detection guide for Security Audit, ask for evidence-friendly outputs such as monitored paths, rule IDs, and review steps.

Use the first output to identify gaps: missing endpoints, noisy directories, weak rule coverage, or unclear alert ownership. Then refine with concrete follow-up prompts like: “Tighten FIM for Linux web servers,” “Add Windows-specific exclusions,” or “Map alerts to a SOC triage checklist.” That kind of iteration produces a more deployable HIDS design than a single broad request.

Developers and IT professionals often struggle to protect endpoints from sophisticated attacks that bypass traditional network defenses. Host-based intrusion detection systems (HIDS) provide a critical layer of security by monitoring individual endpoints for suspicious activity and unauthorized changes. Host-based intrusion detection in endpoint security involves software agents installed on devices to detect malicious behavior, file changes, and policy violations. This guide explains how HIDS works, how to implement it, troubleshoot common issues, and follow best practices for stronger endpoint protection.

Host-based intrusion detection is a security approach that monitors and analyzes the activities on a single endpoint device to detect unauthorized or malicious behavior. Unlike network-based intrusion detection systems (NIDS) that monitor traffic across networks, HIDS focuses on the internal state and events of the host, such as file integrity, system logs, processes, and user activity.

In endpoint security, HIDS agents run locally on devices like laptops, servers, or desktops. They collect data on system calls, file changes, and configuration modifications, then compare this data against known attack signatures or baseline behaviors. When suspicious activity is detected, HIDS can alert administrators or trigger automated responses. This approach helps detect attacks that evade network monitoring, such as insider threats, malware executing locally, or unauthorized configuration changes. HIDS complements other security measures by providing detailed insights into endpoint behavior, enabling faster detection and response to threats.

What prerequisites are required for host-based intrusion detection in endpoint security? Installing HIDS involves deploying an agent on each endpoint device that you want to monitor. The installation process varies depending on the HIDS solution and operating system but generally follows these steps: First, download the appropriate agent installer package from the vendor or open-source project. For example, OSSEC and Wazuh are popular open-source HIDS tools supporting multiple platforms.

This command installs the OSSEC agent on a Debian-based Linux system. The agent will then be configured to communicate with the OSSEC server. On Windows, you typically run an MSI installer and follow the setup wizard to install the agent. After installation, configure the agent with the server address and authentication keys.

Once installed, start the agent service to begin monitoring. The agent collects system events, file integrity data, and logs, then sends this information to the central server for analysis. 4. Practical Deployment, Analysis & Case Studies Effective HIDS configuration involves setting monitoring rules, defining alert thresholds, and tuning the system to reduce false positives. Start by specifying which files, directories, and logs to monitor for changes or suspicious activity.

For example, configure file integrity monitoring on critical system files and configuration directories. This helps detect unauthorized modifications that may indicate a breach. This configuration snippet tells the HIDS agent to monitor changes in the /etc, /usr/bin, and /var/log directories. Monitoring these locations helps catch tampering with system settings and binaries.

Next, define alert levels for different types of events. You can set high-priority alerts for root access attempts or malware signatures, and lower priority for less critical changes. Tuning these settings reduces noise and helps focus on real threats. Regularly update detection rules and signatures to keep up with new attack techniques. Integrate HIDS alerts with your security information and event management (SIEM) system for centralized monitoring and faster incident response.

Select a HIDS solution that fits your environment, such as OSSEC, Wazuh, or commercial products. Download the agent installer for your endpoint operating system. Run the installation command or setup wizard with administrative privileges to install the agent. Ensure the agent service is enabled to start on boot.

This command installs the OSSEC agent on Linux. After installation, the agent will be ready to configure communication with the server. Edit the agent configuration file to specify the IP address or hostname of the central HIDS server and any required authentication keys.

Within this file, update the section to point to your server. This enables the agent to send collected data securely. Specify which files and directories to monitor by editing the rules configuration. Include critical system paths and logs relevant to your security policy. Start the HIDS agent service and check its status to confirm it is running without errors.

These commands start the OSSEC agent and display its current status, confirming it is active and monitoring. Review alerts generated by the HIDS server and adjust monitoring rules to reduce false positives. Regular tuning improves detection accuracy and operational efficiency. How do host-based intrusion detection systems complement other endpoint security tools? Host-based intrusion detection systems provide detailed monitoring of endpoint activities that network-based tools may miss.

They detect local changes, unauthorized file modifications, and suspicious processes that indicate compromise. When combined with antivirus, endpoint detection and response (EDR), and firewalls, HIDS adds a deeper layer of visibility. It helps identify insider threats, zero-day exploits, and malware that evade signature-based detection. Integration with centralized logging and SIEM platforms enables correlation of endpoint events with network and application data. This comprehensive view improves threat detection, investigation, and response capabilities.

HIDS monitors various indicators that suggest a security breach or malicious activity on endpoints. Common indicators include unexpected changes to critical system files, unauthorized user account creation, suspicious process execution, and unusual network connections initiated by the host. File integrity monitoring alerts you to modifications in system binaries or configuration files that attackers often target. Log analysis can reveal failed login attempts, privilege escalations, or attempts to disable security services.

By detecting these indicators early, HIDS helps prevent further damage and supports incident response efforts. Host-based intrusion detection is a vital component of endpoint security that monitors individual devices for unauthorized changes and suspicious activity. By focusing on the internal state of endpoints, HIDS detects threats that network-based tools might miss, such as insider attacks and local malware execution.

Developers and security teams should implement HIDS alongside other endpoint protections to gain comprehensive visibility and faster threat detection. Following best practices like regular updates, tuning alerts, and secure communication ensures effective and reliable intrusion detection on endpoints. Host-based intrusion detection can identify attacks such as unauthorized file modifications, privilege escalation attempts, malware execution, and insider threats by monitoring system changes and suspicious activities on endpoints.

FAQ 2: How does host-based intrusion detection differ from network-based intrusion detection? Host-based intrusion detection monitors activity on individual devices, focusing on file integrity and system logs, while network-based detection analyzes traffic across the network to spot suspicious patterns. Yes, extensive monitoring can affect performance. Optimizing the scope of monitored files and scheduling scans during low-usage periods helps minimize impact on endpoint responsiveness.

HIDS rules and software should be updated regularly, ideally as soon as new versions or signatures are released, to ensure detection of the latest threats and vulnerabilities. HIDS is suitable for most endpoint types, including servers, desktops, and laptops, but compatibility depends on the HIDS solution and operating system support.

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Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Recent benchmark efforts have advanced the evaluation of large language models (LLMs) in cybersecurity, including tasks such as penetration testing and vulnerability identification. However, a critical cybersecurity task, namely intrusion detection from system logs, remains unexplored. In this work, we present a new benchmark to assess LLMs’ capabilities in supporting host-based intrusion detection systems (HIDS). This task requires fine-grained reasoning over large-scale, noisy, and highly imbalanced system logs, where complex interactions between benign and malicious activities make reliable detection challenging. Our benchmark unifies three public system log datasets, DARPA-E3, DARPA-E5, and NodLink, and introduces a data construction pipeline that transforms raw host telemetry into LLM-compatible inputs, enabling systematic evaluation under realistic intrusion detection settings. Our evaluation of frontier LLMs reveals substantial performance gaps across datasets. While many models achieve high precision (often above 0.8) on simpler datasets, their performance degrades significantly as system logs become noisier and more complex, with MCC frequently dropping below 0.5 and false positive rates increasing sharply. We further analyze model behavior and identify distinct regimes, including conservative detectors with low false positive rates and over-sensitive models that generate excessive alerts. Overall, our results highlight that while LLMs show strong potential for HIDS, their effectiveness is highly sensitive to data complexity, and robust system design is essential for reliable deployment.

Recent benchmark efforts have explored the evaluation of large language models (LLMs) in cybersecurity. General benchmarks such as GLUE [ 1 ] and MMLU [ 2 ] assess broad language capabilities but do not capture domain-specific security challenges. More recent domain-specific benchmarks, including CyberSecEval [ 3 ] and CyberBench [ 4 ] , focus on structured tasks such as vulnerability understanding and security-oriented NLP. Other efforts such as Cybench [ 5 ] and CyberGym [ 6 ] evaluate LLM agents in CTF-style or vulnerability exploitation scenarios. In parallel, specialized harnesses have been designed for cybersecurity tasks like penetration testing [ 7 , 8 ] , vulnerability detection [ 9 , 10 ] , threat intelligence analysis [ 11 , 12 , 13 ] , and interpreting attack artifacts such as malicious scripts [ 14 ] . These benchmarks/tasks focus on software implementations or offensive security. Yet, a benchmark is missing for a critical cyber-security task, namely intrusion detection on the log telemetry. As intrusion detection is performed more frequently than the other tasks by defenders and LLMs are demonstrating increasingly advanced reasoning capabilities, a natural question arises: can they support real-world cybersecurity operations that demand fine-grained analysis of massive, noisy telemetry streams? In this work, we systematically investigate their performance on Host-based intrusion detection System (HIDS) [ 15 ] , which presents a uniquely challenging evaluation setting. Unlike isolated text inputs, host telemetry consists of heterogeneous event streams, such as process executions, file accesses, network connections, and provenance dependencies, generated continuously at enterprise scale. Malicious evidence is rare and often fragmented across multiple events, requiring models to reason over weak and distributed signals under severe class imbalance and extended temporal contexts. Benchmark construction poses an additional challenge: widely used datasets such as DARPA-E3 [ 16 ] and DARPA-E5 [ 17 ] contain massive audit logs with realistic background noise, making naive LLM evaluation impractical. Direct prompting on raw logs is constrained by context limits and computational cost, while filtering to attack-only events removes the benign context necessary for realistic detection.

Several works have proposed integrating LLMs into HIDS frameworks, including agent-based systems for explainable intrusion detection [ 18 ] , knowledge-driven provenance-based detection systems [ 19 ] , and hybrid pipelines [ 20 ] . However, LLMs play a limited role in these works and their capabilities are not systematically assessed. Concurrently, Bilot et al. developed PIDSMaker [ 21 ] , a benchmark containing log telemetry to evaluate the provenance-based intrusion detection systems (PIDS) [ 22 ] . However, the customized design for PIDS does not match the strengths (e.g., built-in knowledge of cyber-attack patterns) and weaknesses (e.g., limited context window) of LLMs.

To address this gap, we introduce HIDBench , a unified benchmark for evaluating LLMs in HIDS. HIDBench integrates three public log datasets (DARPA-E3, DARPA-E5, and NodLink) through a coherent data pipeline that transforms raw host telemetry into LLM-compatible inputs. Rather than directly prompting on raw logs, we design an evaluation framework that organizes system events around attack-centric windows, preserving malicious activity alongside its surrounding benign context while keeping inputs within model context limits. We further construct structured representations of system behavior that enable models to reason over heterogeneous signals involving processes, files, and network interactions, directly targeting the core challenges of long-context reasoning, sparse attack signals, and class imbalance. HIDBench is designed as a model-agnostic harness: integrating a new LLM requires only specifying its API endpoint, with no changes to the data pipeline, prompts, and evaluation logic.

5. Performance Evaluation & Extended Technical Observations Our evaluation of 9 frontier LLMs reveals substantial and recurring performance gaps across datasets. While many models achieve high precision on simpler datasets, performance degrades sharply as logs become noisier and more complex, with Matthews Correlation Coefficient (MCC) frequently dropping below 0.5 0.5 and false positive rates increasing substantially.

Analysis of model behavior reveals distinct detection regimes, including conservative detectors that suppress alerts and over-sensitive models that generate excessive false positives. These findings underscore the importance of robust system design for reliable HIDS deployment and establish HIDBench as a rigorous testbed for this underexplored but operationally critical task. Rather than introducing a new standalone intrusion detection algorithm, HIDBench is designed as a unified benchmark and evaluation harness for systematically assessing LLM capabilities in host-based intrusion detection under realistic telemetry settings. Specifically, our main contributions are as follows: Benchmark. We introduce HIDBench , a unified and plug-and-play benchmark for evaluating LLMs in host-based intrusion detection. Our benchmark integrates three widely used datasets (DARPA-E3, DARPA-E5, and NodLink) into a standardized evaluation framework, enabling systematic and cross-dataset comparison.

System Design. We design a principled pipeline that transforms large-scale, noisy, and highly imbalanced system telemetry into structured, LLM-compatible inputs. Our approach preserves both malicious activity and surrounding benign context, enabling realistic evaluation under LLM context constraints. Comprehensive Evaluation and Insights. We conduct an extensive evaluation of frontier LLMs and uncover consistent performance gaps across datasets. Our analysis reveals that high precision can be misleading under severe class imbalance, and identifies distinct model behaviors (e.g., conservative vs. over-sensitive detectors), highlighting key challenges for deploying LLM-powered HIDS.

Figure 1 illustrates the workflow of our benchmark HIDBench . It consists of four components: (i) Data Construction & Segmentation , which transforms high-volume, highly-imbalanced raw host logs into structured inputs suitable for evaluation by LLMs; (ii) Malicious Evidence Identification , which extracts attack evidence using the LLM; (iii) Attack Graph Expansion , which augments the identified evidence with its surrounding context; and (iv) Attack Chain Reconstruction , which instructs the LLM to perform intrusion detection over the attack graph and produce structured outputs. Together, these components address the core challenges of scale, imbalance, and contextual complexity that make HIDS a particularly challenging setting for LLM.

Feeding the logs of an HIDS dataset directly into an LLM usually cannot generate meaningful results for two reasons. First, an HIDS dataset usually contains millions of events, far exceeding the input window of an LLM (e.g., 1M tokens). Second, though we can slice an HIDS dataset into smaller segments that fit in the LLM’s input window, naive approaches like constant-interval sliding windows would severely degrade the input quality, because benign events often overwhelmingly dominate a window and cause class imbalance.

We carefully construct log segments by examining the ground-truth reports. To preserve execution semantics and system interactions, we retain identifiers, event attributes, and entity-level information (e.g., process paths, file paths, and network attributes) following prior provenance-based HIDS preprocessing practices [ 23 , 24 ] . We additionally remove redundant events to suppress noise. Detailed preprocessing descriptions are provided in Appendix B.1 . We first identify the attack interval that is documented by the report (e.g.,"/tmp/vUgefal" for DARPA E3 Cadets). Then, given a log stream L L with an attack interval [ t s , t e ] [t_{s},t_{e}] , we define the attack duration as ∆ I t = t e − t s \Delta t=t_{e}-t_{s} . We partition events into three temporally aligned segments: benign events before the attack, attack-period events, and benign events after the attack. Specifically, the benign context is selected using windows of the same duration as the attack interval, i.e., [ t s − ∆ t , t s ) [t_{s}-\Delta t,t_{s}) before the attack and ( t e , t e + ∆ t ] (t_{e},t_{e}+\Delta t] after the attack. The three segments are concatenated to form an attack-centric window that preserves both malicious activity and its surrounding benign context.

Though the input window limits of the tested LLMs range from 131,072 (DeepSeek V3.2 and gpt-oss-120b) to 1,048,576 (Gemini-2.5-Flash) tokens, we found that none of the attack-centric windows exceed the limit. An attack-centric window after the previous step still contains a substantial amount of benign events that obscures malicious events. Directly prompting LLMs to detect the attack events over such inputs risks producing erroneous outputs due to the “lost-in-the-middle” effect [ 25 ] . To address this issue, we request the LLM to identify a compact set of high-confidence attack evidence I \mathcal{E} from the input events E E . An HIDS dataset could contain various event types, but it is genuinely more difficult to classify certain types, e.g., network events with only IP addresses and flow types. Hence, we focus on events with semantically rich signals such as command-line executions and process behaviors (e.g., execution of a suspicious process “./gtcache” via “/bin/sh” ). By anchoring LLM on the high-confidence attack evidence I \mathcal{E} , we found that detection accuracy can be significantly improved. The prompt template for this step is described in Appendix C.7 .

High-confidence evidence identified by MEI captures salient malicious signals but lacks the surrounding context necessary for reasoning over multi-step attack behaviors. As an example, in the Browser Extension w/ Drakon Dropper attack from the DARPA-E3 THEIA dataset, the suspicious process “./gtcache” exhibited stealthy shell execution and repeated memory manipulation behaviors (e.g., EVENT_MMAP and EVENT_MPROTECT), but reconstructing the full attack chain required additional provenance context, including process lineage, file modifications, and external network communications. Without such context, benign intermediary processes and previously unseen network entities involved in the attack chain may be omitted from the investigation.

Therefore, we develop a lightweight context-building module to augment I \mathcal{E} from the previous step, inspired by prior provenance-based attack analysis works [ 26 , 23 , 27 ] . Given the extracted evidence I \mathcal{E} , we perform k k -hop expansion to collect interacting entities and relations that capture contextual dependencies. The resulting subgraph is serialized into a structured representation for downstream reasoning. To reduce positional bias introduced by deterministic serialization, we randomly shuffle the order of nodes and edges before serialization while preserving graph structure and edge semantics. Appendix B.2 provides the pseudo-code of this procedure.

Given the serialized subgraph produced by AGE, we prompt the LLM to generate two structured outputs: (i) an attack narrative describing the sequence of adversarial actions, and (ii) key indicators of compromise (IOCs) such as suspicious processes, files, and network connections. While IOCs can also be derived by traditional intrusion detection systems, attack narratives provide higher-level contextual explanations favored by human analysts. To ensure fair comparison, we use the same prompt template across all models without dataset-specific hints or handcrafted rules. Full prompt templates are provided in Appendix C.7 .

In this step, we also experiment with some post-processing techniques, including majority voting under test-time scaling [ 28 ] and iterative self-reflection [ 29 , 30 ] . We found majority voting improves the detection results and include it as a default option in HIDBench . Self-reflection produces mixed outcomes and we discuss this issue in Section 4 . Datasets. We evaluate HIDBench on three public host telemetry datasets: DARPA-E3 [ 16 ] , DARPA-E5 [ 17 ] , and NodLink-simulated data (NL-SD) [ 31 ] , comprising nine sub-datasets in total. All datasets consist of host-level audit logs capturing endpoint system activities under realistic attack scenarios, with highly imbalanced distributions between malicious and benign events. Dataset statistics after “Data Construction & Segmentation” described in Section 2.1 are summarized in Table 2 in Appendix. The full dataset descriptions are provided in Appendix C.1 .

Ground-truth Labels. Labeling strategy significantly affects evaluation reliability, particularly given the severe class imbalance in host telemetry data. We adopt the labeling strategy that precisely models the attack activities. For DARPA-E3/E5, we adopt the labeling strategy from Orthrus [ 32 ] and Velox [ 33 ] , which strictly follows the official ground-truth documentation without additional expansion. Far fewer attack events are labeled compared to other more aggressive labeling strategies (e.g., multi-hop neighbors of annotated malicious nodes [ 23 , 34 , 35 ] ), which present a more challenging but realistic setting for intrusion detection. For NL-SD, we use the ground-truth node labels directly provided by the dataset authors [ 36 ] .

LLM Models. We evaluate a diverse set of frontier LLMs spanning multiple providers and development paradigms, including both proprietary and open-weight models. Specifically, we include Claude-Opus-4.6 [ 37 ] , Claude-Sonnet-4.5 [ 38 ] , Claude-Sonnet-4 [ 39 ] , GPT-5.2 [ 40 ] , GPT-4.1 [ 41 ] and Gemini-2.5-Flash [ 42 ] as proprietary models and GPT-OSS-120B [ 43 ] , DeepSeek-V3.2 [ 44 ] and Qwen3.6-Plus [ 45 ] as open-weight models. This selection covers a range of model families, scales, and training paradigms, enabling a comprehensive assessment of current LLM capabilities in HIDS.

Evaluation Metrics. We evaluate model performance using precision, false positive rate (FPR), and Matthews Correlation Coefficient (MCC). Precision and FPR measure alert quality and false-alarm behavior, while MCC captures overall prediction quality under severe class imbalance. Detailed metric definitions are provided in Appendix C.3 . Data Contamination. A primary threat to validity is that the tested LLMs have seen the ground-truth labels of the public dataset and use them to answer prompts. To mitigate such data contamination issue, our prompt templates do not expose dataset names, attack labels, or ground-truth annotations. Models only receive serialized provenance graphs and must reason over contextual system behaviors. We also manually inspected outputs and did not observe evidence of explicit ground-truth memorization. The substantial performance degradation on harder datasets such as DARPA-E5 further suggests that successful detection depends on contextual reasoning rather than memorized attack signatures.

We evaluate all models using precision, MCC, and FPR. Full per-model, per-dataset results are provided in Table 3 in the Appendix. In the main text, we organize our findings under four research questions ( RQ1 to RQ4 ) highlighted below. Table 2 reveals substantial variation in both data volume and class imbalance across datasets. The DARPA datasets are significantly larger, ranging from 39,198 to 121,263 events per sub-dataset, after data construction steps including attack-centric splitting and filtering based on unique event identifiers. These values reflect the size of the LLM-compatible inputs rather than the full raw logs. The datasets exhibit extremely low malicious-to-benign ratios, as low as 0.004% in DARPA-E3 TRACE and 0.005% in DARPA-E5 TRACE. In contrast, NL-SD sub-datasets are considerably smaller (481–2,411 events) and exhibit higher malicious ratios, ranging from 3.4% to 6.0%.

These structural differences translate directly into measurable performance variation, as shown in Figure 2 and Table 3 . Across all evaluated LLMs, DARPA-E3 TRACE is consistently the easiest sub-dataset, achieving nearly perfect precision and MCC for most models (average precision: 1.000; average MCC: 0.879) with near-zero FPR. This suggests that attacks in TRACE expose highly distinguishable malicious behaviors that remain separable from benign background activity despite severe class imbalance. In contrast, DARPA-E3 THEIA exhibits substantially lower MCC despite relatively high precision.

Averaged across models, THEIA achieves only 0.229 MCC, compared to 0.475 for CADETS and 0.879 for TRACE. For example, GPT-4.1 and GPT-OSS-120B both achieve perfect precision (1.000) on THEIA, yet their MCC scores remain low (0.250 and 0.216, respectively), indicating that identifying isolated suspicious events alone is insufficient for reconstructing the full attack chain. This behavior suggests that THEIA requires reasoning over long-range contextual dependencies between benign and malicious activities. CADETS presents an intermediate level of difficulty. Although its average MCC is substantially higher than THEIA, it consistently produces higher FPR than TRACE. Even small FPR values become operationally costly under the significantly larger event volume of provenance graphs.

Moving to DARPA-E5 and NL-SD, detection quality degrades substantially as dataset complexity increases. Compared to DARPA-E3, average precision on E5 drops from 0.839 to 0.237 on CADETS and from 0.781 to 0.071 on THEIA, while FPR increases significantly across models (e.g., GPT-4.1 rises from 0.02% to 0.543% FPR on CADETS). These results suggest that the noisier telemetry and more ambiguous attack behaviors in E5 substantially increase the difficulty of reliable attack reconstruction. NL-SD exhibits a different failure mode. While HW17 and HW20 maintain relatively strong average MCC values (0.458 and 0.570, respectively), WIN10 remains substantially harder despite its higher malicious ratio, achieving only 0.268 average MCC across models. This suggests that overlapping attack campaigns and interleaved attack chains introduce additional reasoning complexity beyond class imbalance alone. See Appendix C.4 for additional dataset-specific analysis.

Taken together, these results reveal that detection difficulty is multidimensional and dataset-specific. Class imbalance and background noise jointly explain the uniform performance degradation observed in DARPA-E5, while attack pattern visibility, rather than imbalance, is the primary driver of variation within DARPA-E3. In NL-SD, neither imbalance nor noise is the bottleneck; instead, attack density and cross-platform behavioral ambiguity introduce qualitatively different challenges that current LLMs are ill-equipped to handle. These findings suggest that no single dataset characteristic captures the full difficulty of HIDS evaluation, and that robust benchmarking must account for the interplay of multiple complexity factors simultaneously. 1 1 1 Recent non-LLM HIDS such as Orthrus [ 24 ] and Velox [ 33 ] , report similar performance on DARPA-E3 and E5. For example, Orthrus and Velox achieve average precision of 0.66 and 0.85 on E3, and 0.43 and 0.12 on E5, respectively. However, the numbers do not necessarily reflect their real capability gaps due to different setups (e.g., the non-LLM HIDS have a training phase using the “attack-free” logs on the same machines, which are not used by HIDBench ).

RQ2: How do frontier LLMs navigate the tradeoff between precision and false positive rate? Figure 3 reveals a clear and consistent pattern: as dataset complexity increases, the precision–FPR tradeoff becomes increasingly difficult to maintain. On DARPA-E3, the simplest dataset family, most models cluster in the upper-left region of the plot, achieving both high precision and near-zero FPR. Averaged across models, DARPA-E3 achieves 0.893 precision with only 0.008% average FPR, indicating that frontier LLMs can effectively suppress false alarms when attack behaviors remain distinguishable from benign activity. Claude-Opus-4.6 represents the strongest operating point in this regime, achieving approximately 0.92 average precision while maintaining only 0.01% average FPR.

As dataset complexity increases, models spread more noticeably along the precision–FPR tradeoff curve. Compared to DARPA-E3, average precision on DARPA-E5 drops from 0.893 to 0.614, while average FPR increases from 0.008% to 0.128%. The tradeoff becomes most pronounced on NL-SD, where models separate into distinct behavioral regimes. Over-sensitive models aggressively flag suspicious behaviors at the cost of elevated FPR, while conservative models maintain lower FPR but suppress broader classes of attack indicators. For example, Gemini-2.5-Flash reaches approximately 2.1% average FPR across NL-SD, substantially higher than other evaluated models. These results suggest that under noisier and more behaviorally heterogeneous environments, frontier LLMs increasingly diverge not only in overall capability, but also in their implicit detection strategies. Additional model-specific analysis of DARPA-E5 and NL-SD is provided in Appendix C.5 .

We also observe a consistent gap between proprietary frontier models and open-weight/open-access models across datasets. Averaged over all sub-datasets, proprietary models achieve higher overall precision and substantially lower FPR. For example, on DARPA-E3, proprietary models achieve approximately 0.90 average precision compared to 0.82 for open models, while maintaining similarly negligible FPR. The gap becomes more pronounced on harder datasets such as DARPA-E5 and NL-SD, where open models exhibit substantially less stable operating behavior. For instance, GPT-OSS-120B and Gemini-2.5-Flash frequently drift toward high-FPR regimes under noisy telemetry, whereas Claude-Opus-4.6 and GPT-5.2 maintain comparatively stable precision–FPR tradeoffs. These results suggest that stronger frontier reasoning capabilities improve robustness under complex provenance analysis settings, particularly when attacks are heavily interleaved with benign behaviors.

Overall, these results demonstrate that the precision–FPR tradeoff is not a fixed property of an LLM, but strongly depends on dataset complexity and attack characteristics. While most frontier models achieve highly stable operating points on structured and lower-noise datasets such as DARPA-E3, their behaviors diverge substantially as background noise, attack density, and behavioral ambiguity increase. In particular, proprietary frontier models generally maintain more stable precision–FPR tradeoffs under complex settings, whereas open-weight/open-access models exhibit greater variance and are more likely to drift toward high-FPR regimes. These observations motivate a closer examination of the distinct behavioral patterns exhibited by different LLM families, which we explore in RQ3.

RQ3: Can frontier LLMs be categorized into distinct behavioral regimes, and what drives these differences? To move beyond per-dataset comparisons, we characterize each model’s false positive behavior globally across all datasets. Using only a single dataset-level FPR can be misleading because different provenance datasets exhibit substantially different event scales, attack densities, and background noise levels, leading to highly uneven FPR distributions and dataset-specific outliers. To better capture overall operational behavior, we therefore use two complementary metrics: the average FPR across all datasets f i avg f_{i}^{\mathrm{avg}} , which reflects a model’s overall false-alarm tendency, and the worst-case FPR f i max f_{i}^{\mathrm{max}} , which captures peak deployment risk under challenging conditions. Based on these metrics, we define three behavioral regimes. Conservative models maintain both low average and low worst-case FPR ( f i avg < 0.25 f_{i}^{\mathrm{avg}}<0.25 , f i max < 1.0 f_{i}^{\mathrm{max}}<1.0 ). Over-sensitive models exceed either threshold ( f i avg ≥ 0.50 f_{i}^{\mathrm{avg}}\geq 0.50 or f i max ≥ 2.0 f_{i}^{\mathrm{max}}\geq 2.0 ), indicating unstable behavior under noisy conditions. Balanced models fall between these two extremes.

Figure 4 reveals that this categorization is both meaningful and stable. Claude-Opus-4.6 and Claude-Sonnet-4 emerge as the most conservative models, maintaining consistently low FPR across the DARPA datasets while avoiding extreme false-positive spikes on NL-SD. At the other extreme, Gemini-2.5-Flash exhibits the most severe over-sensitivity, with FPR peaking at 4.52% on NL-HW17 and remaining substantially elevated on WIN10 (1.41%), despite near-zero FPR on DARPA-E3. GPT-4.1 and DeepSeek-V3.2 similarly fall into the over-sensitive regime, with GPT-4.1 reaching 0.54% FPR on DARPA-E5 CADETS and both models exhibiting elevated false positives across multiple NL-SD settings. The balanced regime, occupied by Qwen3.6-Plus, GPT-OSS-120B, and Claude-Sonnet-4.5, represents models that avoid extreme false-alarm rates while maintaining moderate detection coverage.

A consistent pattern emerges across all regimes: FPR on DARPA datasets remains near zero for virtually all models, while NL-SD, particularly HW17, acts as the primary differentiator. This confirms that behavioral regime membership is not an intrinsic property of a model, but is revealed only under sufficiently challenging conditions. Models that appear behaviorally similar on simpler datasets diverge substantially under concurrent attacks and cross-platform behavioral ambiguity, underscoring the necessity of diverse and realistic benchmarks for meaningful model comparison.

We further analyze the monetary cost of using LLMs for HIDS. Stronger frontier models such as Claude-Opus-4.6 and GPT-5.2 achieve better detection performance but incur substantially higher inference cost, evaluating a full dataset such as DARPA-E3 CADETS. In contrast, lightweight models such as Gemini-2.5-Flash and GPT-OSS-120B typically remain below $0.1 per dataset, though often with reduced robustness on more challenging datasets. Runtime overhead follows a similar trend, with larger reasoning-oriented models requiring substantially longer inference time; however, even the slowest models complete inference within approximately one minute per sample. Detailed per-model cost and runtime statistics are provided in Appendix D .

The main results reveal substantial performance gaps across datasets, raising a natural follow-up question: can these gaps be narrowed through more sophisticated inference strategies, without modifying model parameters? We investigate three complementary directions: explicit reasoning, test-time scaling via majority voting, and iterative self-reflection, probing the extent to which inference-time strategies can compensate for the inherent difficulty of HIDS.

How much does LLM reasoning rely on inferred versus explicit evidence? To understand whether LLMs rely on overtly suspicious signals or contextual reasoning, we analyze reasoning traces produced by DeepSeek on DARPA-E3 CADETS. We first use GPT-5.4 for initial IOC categorization and then manually verify all classifications. We divide IOCs into two categories: explicit IOCs, corresponding to directly observable malicious artifacts, and inferred IOCs, which require contextual reasoning over co-occurring events and provenance relationships. Across all analyzed attack cases, inferred IOCs consistently outnumber explicit ones, suggesting that the model actively constructs contextual evidence chains beyond directly observable malicious indicators. Additional quantitative statistics and case studies are provided in Appendix E.3 .

As shown in Table 8 , inferred IOCs consistently outnumber explicit ones across all four attack cases, with the gap most pronounced in medium-quality cases. This suggests that LLMs do not simply pattern-match on obvious indicators, but actively construct evidence chains that extend beyond directly observable signals. Representative examples in Table 1 illustrate this: while explicit IOCs capture artifacts such as suspicious executables and external IPs, inferred IOCs surface subtler relationships such as system services and configuration files implicated through multi-step behavioral context. Further case analysis is provided in Appendix E.3 .

Does test-time scaling improve detection performance? We evaluate majority voting under test-time scaling with sample sizes k ∈ { 1 , 3 , 5 , 7 } k\in\{1,3,5,7\} . As shown in Figure 5 , increasing k k from 1 to 3 yields the largest performance improvement across models, while larger values provide diminishing returns. Stronger models such as Claude-Opus-4.6 remain relatively stable even at k = 1 k=1 , whereas weaker or more variable models benefit substantially from aggregation. See Appendix E.2 for details.

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