BLOCKCHAIN AND STEGANOGRAPHY BASED INTRUSION DETECTION

Abstract

The escalating sophistication of cyber threats has rendered conventional intrusion detection systems (IDS) increasingly inadequate due to centralized vulnerabilities, tampering risks, and blindness to covert attack vectors. This thesis proposes a novel framework integrating blockchain technology and steganographic analysis to address these limitations, leveraging blockchain’s decentralized consensus and cryptographic immutability to eliminate single points of failure while ensuring tamper-proof logging, and repurposing steganographic detection to identify hidden payloads in network traffic, multimedia, and blockchain transactions. The Three-Layer Consensus Protocol—combining stego-embedded triggers, distributed validation (PBFT consensus across 50 nodes), and immutable storage—achieves 97.6% detection accuracy against hybrid threats, while the StegoChainNet model, with spatial attention modules and temporal blockchain analyzers, reduces false positives by 37% and detects Spread Spectrum Image Steganography (SSIS) at 92% accuracy. Experimental validation on CIC-IDS2017 and IStego100K datasets demonstrates sub-2-second alert confirmation latency and 91.2% precision in covert channel detection, outperforming Snort (89.2%) and SRNet (89.9%). Challenges include scalability-throughput tradeoffs (63% throughput loss at 50+ nodes), adversarial evasion via GAN-generated stego-payloads (18% accuracy drop), and regulatory conflicts (GDPR vs. immutability), with case studies in healthcare and finance showing 63% reduced exfiltration risks and 51% fewer lateral breaches. Future work prioritizes quantum-resistant cryptography and lightweight protocols to enable enterprise adoption, establishing blockchain-steganography convergence as a transformative paradigm for next-generation IDS that balances security, transparency, and adaptability in evolving cyber landscapes.