Intrusion Detection System Using Random Forest On Nsl Kdd Dataset — Complete Documentation & Simulation

Overview & System Context

Intrusion Detection System Using Random Forest On Nsl Kdd Dataset: Comprehensive Web-Sourced Technical Investigation Exhaustive Research Dossier · Pure Web-Scraped Content: ~1050 Words · Real Software Screenshots: 1 1. Executive Overview & Technical Background This project implements a network intrusion detection system using the NSL-KDD dataset. It was developed as a reproduction and evaluation of the methodology described in the paper "A Subset Feature Elimination Mechanism for Intrusion Detection System" (Nkiama, Said & Saidu, 2016).

Network intrusion detection involves classifying network traffic as either normal or an attack. This project trains and evaluates two machine learning models — Decision Tree and Random Forest — on the NSL-KDD dataset, following the feature selection approach proposed in the reference paper, and critically evaluates how well the paper's reported results could be reproduced.

Nkiama, H., Said, S. M., & Saidu, M. (2016). A Subset Feature Elimination Mechanism for Intrusion Detection System . International Journal of Advanced Computer Science and Applications (IJACSA).

Paper link: https://thesai.org/Publications/ViewPaper?Volume=7&Issue=4&Code=ijacsa&SerialNo=45 NSL-KDD Dataset — an improved version of the KDD Cup 1999 dataset that removes duplicate records and provides a more balanced set of attack categories (DoS, Probe, R2L, U2R).

Figure 1: Video Demonstration & Practical Interface Overview This project also references the implementation approach used in: Cynthia Koopman's Network-Intrusion-Detection repository: https://github.com/CynthiaKoopman/Network-Intrusion-Detection Decision Tree — a single tree-based classifier that splits data based on feature values.

Random Forest — an ensemble of decision trees whose predictions are combined by majority voting. 2. System Architecture & Core Working Principles A machine learning-based Network Intrusion Detection System using the NSL-KDD dataset. This project reproduces and critically evaluates a published research paper by implementing Decision Tree and Random Forest classifiers, along with Exploratory Data Analysis (EDA), feature engineering, model evaluation, and performance analysis.

This repository contains code for an Intrusion Detection System (IDS) implemented using machine learning techniques. The IDS is designed to detect and classify different types of network attacks. The IDS code implements the NSL-KDD dataset, which is a popular dataset used for evaluating intrusion detection systems. The train and test files from the NSL-KDD dataset are used to process the data and map the attacks.

The code utilizes the RandomForestClassifier from the sklearn library to train the IDS model. The RandomForestClassifier is used for feature selection, and a graphical representation is formed to visualize the importance of each feature. This feature selection step helps in identifying the most relevant features for the intrusion detection system. The code also includes data preprocessing steps, such as one-hot encoding of categorical features and finalizing the data for training the IDS. The preprocessed data is then used to train the IDS model and obtain accuracy scores.

The NSL-KDD dataset is used for training and testing the IDS. It consists of labeled instances for various types of network attacks. The dataset is divided into train and test files, which are used for model training and evaluation. Please ensure that you have the NSL-KDD dataset files ( train and test ) available in the same directory as the code files before running the code.

3. Software Implementation, Frameworks & Protocols Feel free to experiment with different feature selection methods, classifiers, or hyperparameters to improve the performance of the intrusion detection system. Contributions to this project are welcome. If you have any suggestions, bug fixes, or feature enhancements, please submit a pull request or open an issue.

IDS with NSL-KDD dataset trained using Random Forest, leveraging Decision Tree and Naive Bayes. Detects network anomalies to ensure robust security. Subject: Ethical Hacking & Network Security Models: Decision Tree · Random Forest Dataset: NSL-KDD (standard IDS benchmark) This project implements a machine learning-based Intrusion Detection System (IDS) that classifies network traffic as either Normal or Attack using the NSL-KDD dataset. Two supervised learning models are trained and compared — Decision Tree and Random Forest — to identify malicious network activity automatically.

Random Forest outperforms Decision Tree because it builds an ensemble of 100 trees and uses majority voting, which reduces overfitting and variance. The NSL-KDD dataset is an improved version of the original KDD Cup 1999 dataset. It fixes major issues like duplicate records that caused overfitting.

4. Practical Deployment, Analysis & Case Studies Note: If the dataset files are not found, the script automatically generates synthetic demo data with the same structure so you can still run and test the code. Download KDDTrain+.txt and KDDTest+.txt from the NSL-KDD page and place them in the project folder.

Shows true positives, true negatives, false positives, and false negatives for both models side-by-side. Bar chart of the top 15 most influential features identified by the Random Forest model. Key features include src_bytes , dst_bytes , count , and serror_rate . Why Random Forest over Decision Tree? A single Decision Tree is prone to overfitting — it memorizes training data. Random Forest builds many trees on random subsets and combines their votes (bagging), making it more robust and accurate.

Why NSL-KDD over KDD'99? KDD'99 had ~78% duplicate records — training on it made models look artificially accurate. NSL-KDD removes duplicates, giving a fairer benchmark. What is a Confusion Matrix? A 2×2 table that shows how many predictions were correct vs wrong: 5. Performance Evaluation & Extended Technical Observations This project is for educational purposes as part of an Ethical Hacking course. Free to use and modify.

Built a machine learning IDS using Decision Tree and Random Forest to classify network traffic. Implemented preprocessing, feature engineering, and evaluation with confusion matrix and feature importance visualization. Achieved ~98% accuracy and developed an interactive dashboard for analysis. This project implements an Intrusion Detection System (IDS) using Machine Learning on the NSL-KDD cybersecurity dataset.

The goal is to classify network traffic as either normal or malicious by applying data preprocessing, feature engineering, model training, and evaluation techniques. The project follows an end-to-end machine learning workflow and is structured to resemble a real-world cybersecurity analytics project.

The dataset contains network connection records with multiple features describing traffic behavior and connection statistics. Random Forest feature importance was used to identify the most influential indicators of malicious traffic. These features represent network traffic volume, connection behavior, error rates, and service usage patterns. Harvested Online Documentation & Reference Sources • https://github.com/nadineshehabuni/Network-Intrusion-Detection-NSL-KDD • https://github.com/nilanshu24/Intrusion-Detection-System • https://github.com/Amrit-raj-cyb/AI-based-Intrusion-Detection-System-NSL-KDD-dataset- • https://github.com/LoveSolanki707/NSL-KDD-Intrusion-Detection