Cybersecurity & Ethical HackingB.Tech • M.Tech • MCAComplexity: AdvancedIEEE Verified
AI Network Intrusion Detection & Packet Anomaly Sniffer
Real-time network traffic packet analyzer using Scapy and Random Forest / XGBoost classifiers to detect DDoS, Port Scans, and SQL injection payloads.
Core Stack:Python 3.11ScapyXGBoostScikit-LearnFastAPIReactTailwindCSS
4.9(34 Academic Reviews)
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Full Source Code & Database SQL
60-Page IEEE Project Report (.docx & .pdf)
25-Slide Seminar Presentation Deck (.pptx)
50+ Viva Voce Questions & Answers Guide
Verified System Artifact #1
Detailed Project Overview
A deep packet inspection security tool that captures live pcap network streams, extracts 42 statistical flow features (packet rate, inter-arrival time, byte entropy), and flags malicious zero-day network intrusions with sub-second latency. Includes an interactive threat monitoring SOC dashboard.
Academic Problem Statement
Static rule-based firewalls (like basic iptables) fail against polymorphic malware and distributed slow-rate DDoS attacks. An automated machine-learning classifier is required to identify signatureless malicious traffic patterns.
Proposed Methodology & Flow
1. Raw Ethernet frames sniffed in real-time via Scapy socket layer.
2. Bi-directional flow aggregations extracted (TCP flags, payload entropy, flow duration).
3. Trained XGBoost model classifies flow as Normal, DoS, Probe, R2L, or U2R.
4. Automated IP blacklisting script triggers on threshold breach.