• Background Virtual Private Networks (VPNs) are fundamental to secure communication over untrusted networks. Among the available VPN technologies, IPsec is widely adopted across enterprise, government, military, and cloud infrastructures because of its ability to provide confidentiality, integrity and authentication.
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 Misconfigurations, outdated cipher suites, improper key management, or protocol implementation flaws can significantly weaken the overall security posture.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.
• Description: 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:
• Tunnel Mode • Transport Mode • AES-128 • AES-256 • AES-GCM • AES-CBC + HMAC • Different DH Groups • Perfect Forward Secrecy enabled/disabled • IPv4 and IPv6 communication • Different types of traffic – VoIP, Whatsapp, E-mail, Web-browsing, ICMP, Video streaming etc.
b) Traffic Capture: Acquire network traces using tools such as Wireshark, TCP-dump, Custom packet capture utilities. Captured dataset should include
• IKE negotiation • ESP packets • AH packets (optional) • Normal communication c) AI-Based Protocol Identification: Develop an AI engine capable of automatically identifying • IPsec protocol • IKE version • Tunnel Mode • Transport Mode • Encryption algorithm • Authentication algorithm • Key exchange method • Security Association characteristics • Predict Type of traffic inside ESP-IPsec d) Security Assessment: The framework should automatically evaluate • Cryptographic strength • Configuration compliance • Security Association parameters • Key lifetime • Replay protection • Forward Secrecy configuration • Cipher suite strength • Metadata exposure e) The output should include a comprehensive security score, traffic analysis and metadata inference. Automatically generate • Executive Report & Technical Report • Risk Score • Threat Matrix • AI Confidence Score • Expected Solution/Deliverables: • Working software prototype • AI classification engine • Interactive dashboard • Security assessment report • Demonstration video • Technical documentation • Dataset used for training/testing