Blockchain-Based Decentralized Intrusion Detection System (B-DIDS) for enhancing malware detection in distributed networks

The increase in the complexity of the distributed networks along with the rise in the threat of sophisticated malware attacks expect an innovative and robust solutions for intrusion detection. In this work, a novel Blockchain-Based Decentralized Intrusion Detection System (B-DIDS) is proposed for enhancing the malware detection paradigm in the distributed networks. Unlike the traditional intrusion detection systems, the proposed B-DIDS system leverages the inherent advantages of blockchain technology by including some of the additional features like the immutability, decentralization in addition with the transparency for addressing the challenges imposed by the centralized solutions like single points of failure and its limited scalability. The framework integrates the blockchain technology in addition with the hybrid machine learning algorithm (ILSTM), for identifying and mitigating the malware threats in real time. The ILSTM algorithm utilizes Isolation Forest (IF) algorithm in addition with the Long Short-Term Memory (LSTM). The IF algorithm isolates the anomalies by randomly selecting the features by splitting the data until all the points got isolated and the LSTM is used for capturing the complex patterns in the time series data which is helpful for identifying the anomalies. The smart contracts are employed for automating the consensus process inorder to verify the network anomalies by ensuring the durability and tamper proof responses for malicious activities. Also the system adopts a hybrid consensus mechanism by combining the Proof of Authority (PoA) and the Practical Byzantine Fault Tolerance (PBFT) for achieving the low latency detection and energy efficiency. The experiment is conducted based on simulated environment shows that the proposed algorithm achieves better detection accuracy in addition with better resilience against the sophisticated cyberattacks when compared to the existing systems. The proposed system also ensures the secure exchange of the intrusion data among the various nodes by fostering the collaborative approaches for threat intelligence when gets preserved with data privacy. In this research the potential of the blockchain based architecture is highlighted as a transformative solution for the next generation intrusion detection systems by paving the way for more secure and the reliable distributed networks.