AI-Powered IPsec VPN Protocol Analyzer and Security Assessment Framework
“Virtual Private Networks (VPNs) protect sensitive government and military communication, but configuring them correctly is extremely complex. When configured poorly, they expose secret data. This project builds an intelligent scanner that automatically inspects encrypted network traffic, figures out how the VPN is set up, detects security flaws, and generates comprehensive risk reports without needing human experts to manually read packets.”
Deconstructed Problem Blueprint
WHAT TO BUILD?
An AI-powered protocol analysis and security assessment platform that inspects live or captured IPsec VPN traffic, identifies cryptographic parameters, detects misconfigurations, and automatically generates compliance and executive reports.
WHO IS IT FOR?
National Technical Research Organisation (NTRO) cybersecurity analysts, defense intelligence units, and enterprise security operations center (SOC) teams.
WHY DOES IT EXIST?
Manual inspection of IPsec packets (IKE handshakes and ESP payloads) requires deep cryptographic expertise and is too slow for modern threat landscapes. Misconfigured ciphers, weak Diffie-Hellman groups, or outdated protocols leave backdoors open for state-sponsored adversaries.
INPUT → PROCESS → OUTPUT ARCHITECTURE PIPELINE
PCAP/PCAPNG network capture files, live packet streams from network interfaces, and simulated IPsec testbed traffic containing IKE negotiations, ESP, and AH packets.
Deep packet parsing via Scapy/Tshark, metadata extraction (cipher suites, DH groups, modes), ML-based classification of inner traffic types inside encrypted ESP tunnels, and automated security rule-engine evaluation against cryptographic standards.
An interactive web dashboard showcasing real-time security scores, threat matrices, AI confidence intervals, cryptographic compliance audits, and downloadable executive PDF reports.
CONSTRAINTS
- Must handle high-throughput packet captures efficiently without crashing
- Ability to infer encrypted payload characteristics (e.g., VoIP vs. Video Streaming) via traffic fingerprinting without decrypting payload data
- Compliance with rigorous government cybersecurity standards and air-gapped deployment readiness
JURY METRICS
- Accuracy of IKE version, cipher suite, and mode identification (>98%)
- Precision of inner traffic type prediction from encrypted ESP streams
- Speed of parsing and generating the security assessment report
- Dashboard UX clarity and actionability for non-expert auditors
CORE BOTTLENECK
Extracting meaningful feature vectors from encrypted IPsec ESP payload traffic to accurately classify inner application types (like WhatsApp vs. VoIP) without violating cryptographic privacy or having access to plaintext.
Why You Should Choose This PS (Pros)
- High strategic value for national security agencies like NTRO
- Clear, structured deliverables with explicit modular requirements (Testbed, Capture, AI Engine, Assessment, Reporting)
- Strong 'wow factor' via deep packet analytics combined with machine learning
Challenges & Risks to Watch (Cons)
- Requires setting up a working IPsec testbed (strongSwan/Libreswan) to generate custom training datasets
- Complex network protocol parsing domain that requires solid foundational knowledge of IPsec, IKEv1/IKEv2, and ESP
Recommended Tech Stack & Grand Finale Features
Killer Features to Impress the SIH Jury
View Official SIH 2026 Portal Raw Text▼
However, the security of an IPsec deployment depends on multiple factors, including the chosen cryptographic algorithms, authentication mechanisms, key exchange protocols, and operational mode (Tunnel or Transport). Misconfigurations, outdated cipher suites, improper key management, or protocol implementation flaws can significantly weaken the overall security posture.
Traditional protocol analysis tools provide packet-level visibility but often require expert interpretation. There is a growing need for intelligent systems capable of automatically analyzing IPsec deployments, identifying protocol characteristics, assessing security risks, and generating actionable recommendations.
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Design and develop an AI-driven protocol analysis platform capable of automatically analysing IPsec VPN deployments established under different security configurations. The platform should inspect captured traffic or live network streams, identify protocol characteristics, infer VPN operating modes, evaluate cryptographic configurations and generate an automated security assessment report.
The solution should assist analysts in understanding the security posture of IPsec deployments without requiring manual packet inspection. Participants are expected to develop an intelligent framework capable of performing the following tasks.
a) VPN Testbed Generation: Develop a laboratory environment capable of establishing IPsec VPNs using multiple configurations. The framework should support variations such as:
b) Traffic Capture: Acquire network traces using tools such as Wireshark, TCP-dump, Custom packet capture utilities. Captured dataset should include