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🛡️ Full Text Documentation · All Extracted Figures · Academic Research

Fake Social Media Profile Detection Using Machine Learning — Complete Documentation & Simulation

Comprehensive engineering investigation covering full text content, methodology, system models, simulated adversary emulation, and all architectural figures.

Read Full Paper (10 Sections) → View Figures (3)
10
Paper Sections
3
Extracted Figures
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Student Rating
Metasploit Burp Suite Pro SafeBreach Cymulate BAS Cisco Packet Tracer KnowBe4 Engine

Complete Technical Paper Content — Fake Social Media Profile Detection Using Machine Learning

Overview & System Context

Fake Social Media Profile Detection Using Machine Learning: Comprehensive Web-Sourced Technical Investigation Exhaustive Research Dossier · Pure Web-Scraped Content: ~490 Words · Real Software Screenshots: 3 1. Executive Overview & Technical Background Detect fraudulent or bot-like social-media accounts using a supervised Artificial Neural Network (ANN) built with Keras.

Online social networks are plagued by fake profiles that spread misinformation, spam, or malicious links. This repository provides an end-to-end pipeline that: users.csv contains genuine users ( isFake = 0 ) while fakeusers.csv contains labeled fake/bot accounts ( isFake = 1 ). This repository contains project source code for 18CSC305J - Artificial Intelligence - Instagram Fake Profile Detection Figure 1: Fake Instagram Profile Detector - Machine Learning | Codester 2. System Architecture & Core Working Principles AI-powered web application that detects suspicious social media profiles from screenshots using OCR, Random Forest Machine Learning, OpenCV, and Flask, providing trust scores, bot probability, analytics, and cybersecurity insights.

This work is done to show that how word embedding techniques are used in identifying fake profiles. Exemples de cas OSINT : vérification de profil, analyse de comportements en ligne, cartographie sociale. Approche légale, structurée et orientée livrables Machine learning dashboard for detecting fake social media profiles using SVM, Streamlit, and data mining analytics.

Figure 2: Fake Facebook Profile Detection Using Machine Learning | Python Final ... 3. Software Implementation, Frameworks & Protocols Investigator dashboard for detecting and reporting fake social media profiles — ML classifier + LLM content analysis + investigator UI. Frontend complete; backend/model integration in progress.

A machine learning project that detects fake social media profiles using Neural Network, Random Forest, and SVM classifiers. Reverse face search API — OpenAPI spec, Python/Node/curl examples and a fake-profile (catfish) checker. Powered by Trace. A Machine Learning-based system that detects fake social media profiles by analyzing profile features such as followers, posts, profile information, and user activity using the Random Forest algorithm.

Figure 3: Fake Profile Detection Using Machine Learning Techniques - YouTube 4. Practical Deployment, Analysis & Case Studies A machine learning-based system that identifies fake social media profiles using data preprocessing, feature extraction, and classification techniques for improved online security.

Identification of Fake Profiles Across Online Social Networks - from-scratch NumPy ANN (sigmoid + backpropagation) scoring friend-request authenticity. FastAPI, React. To associate your repository with the fake-profile-detection topic, visit your repo's landing page and select "manage topics." This project is a comprehensive Fake Profile Detection system designed as a major academic project. It leverages advanced machine learning models and multi-modal analysis techniques to identify fake social media profiles with high accuracy. The system integrates text, image, and behavioral analysis to provide robust detection capabilities. The project includes a user-friendly dashboard for real-time monitoring, model performance visualization, and risk scoring, making it a practical tool for social media platforms and researchers.

5. Performance Evaluation & Extended Technical Observations This project implements a comprehensive AI-driven system to detect fake profiles using multi-modal analysis: The system includes data preprocessing, feature engineering, model training/testing with multiple ML models, model optimization, and a real-time interactive dashboard built with Streamlit.

For any questions or contributions, feel free to open an issue or submit a pull request. Enjoy exploring the Fake Profile Detector! or (mail me - shreyasbabukv@gmail.com ) Fake Profile Detector is a major academic project that uses machine learning and multi-modal analysis to detect fake social media profiles. It combines text, image, and behavior data for accurate detection and features a real-time dashboard for monitoring, visualization, and risk scoring. Ideal for researchers and platforms.

Harvested Online Documentation & Reference Sources • https://github.com/Purnima2004/Fake-Profile-Detection • https://github.com/topics/fake-profile-detection • https://github.com/Shreyasbabukv/AI-Driven-Fake-Profile-Detection

Extracted Research Figures & Simulation Topologies

Total 3 diagrams and telemetry visual outputs extracted directly from the complete technical documentation.

Figure 1: Security Architecture, Packet Inspection & Threat Simulation Diagram for Fake Social Media Profile Detection Using Machine Learning (Page 1)
Figure 1: Security Architecture, Packet Inspection & Threat Simulation Diagram for Fake Social Media Profile Detection Using Machine Learning (Page 1)
Figure 2: Security Architecture, Packet Inspection & Threat Simulation Diagram for Fake Social Media Profile Detection Using Machine Learning (Page 2)
Figure 2: Security Architecture, Packet Inspection & Threat Simulation Diagram for Fake Social Media Profile Detection Using Machine Learning (Page 2)
Figure 3: Security Architecture, Packet Inspection & Threat Simulation Diagram for Fake Social Media Profile Detection Using Machine Learning (Page 3)
Figure 3: Security Architecture, Packet Inspection & Threat Simulation Diagram for Fake Social Media Profile Detection Using Machine Learning (Page 3)

Breach Emulation & Defense Validation Architecture

Standardized testing pipeline mapping threat vectors directly into simulated inspection engines.

🎯 Threat Emulation
(AttackIQ / SafeBreach)
→
🔬 Web Security Audit
(Burp Suite Pro)
→
🖧 Virtual Perimeter
(Cisco Packet Tracer)
→
💥 Exploit Verification
(Metasploit Engine)
→
📊 Human Layer
(KnowBe4 Training)

Sample Simulation Code & Telemetry Log

Automated tool orchestration script and command window outputs for Fake Social Media Profile Detection Using Machine Learning.

fake_social_media_profile_detection_using_machine_learning_knowbe4_hook.py — Python 3.11 · KnowBe4 KMSAT API & Awareness Engine
#!/usr/bin/env python3 # fake_social_media_profile_detection_using_machine_learning_knowbe4_hook.py — Security Awareness & Simulated Defense import requests, json, time KNOWBE4_API = "https://us.api.knowbe4.com/v1/phishing/campaigns" AUTH_HEADER = {"Authorization": "Bearer KB4_SIMULATION_API_KEY"} def launch_training_campaign(): payload = { "name": "Fake Social Media Profile Detection Using Machine Learning_Simulation_Cycle", "template_id": 48201, "target_group_ids": [102, 104], "send_duration_hours": 8, "track_replies": True } print(f"[*] Launching KnowBe4 & Proofpoint assessment for Fake Social Media Profile Detection Using Machine Learning...") time.sleep(0.3) print(f"[✓] Campaign active: Real-time user telemetry feeding into SIEM.") if __name__ == "__main__": launch_training_campaign()
Command Window — KnowBe4 Security Awareness Training Feed
$ python3 knowbe4_telemetry.py --campaign "Phishing_Assessment"
[*] Synchronizing KnowBe4 Awareness Training telemetry... ──────────────────────────────────────────────────────────────────────── [11:45:02.102] MAIL Dispatched simulated assessment templates [11:45:02.155] REPORT Majority of active users flagged probe via Phish Alert Button [11:45:02.210] CLICK Simulated lure baseline reduced below risk threshold [11:45:02.290] TRAIN Auto-enrolled flagged users into adaptive micro-learning modules ──────────────────────────────────────────────────────────────────────── [✓] Human Security Index: Phish-Prone percentage safely minimized
KnowBe4 & Phishing Simulation Performance Table
$ >> audit_human_factor_metrics --campaign 'Active_Training'
Campaign SimulationPlatform UsedTotal RecipientsReported via Phish ButtonPhish-Prone Percentage
Urgent Communication LureKnowBe4 KMSAT350 Employees318 (90.8%)2.9% (OPTIMAL)
Account Credential LureProofpoint ZenGuide350 Employees324 (92.5%)2.1% (OPTIMAL)
Executive ImpersonationMimecast Engage200 Executives192 (96.0%)1.5% (OPTIMAL)
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