Multi-Zone-Wise Blockchain Based Intrusion Detection and Prevention System for IoT Environment

Blockchain merges technology with the Internet of Things (IoT) for addressing security and privacy-related issues. However, conventional blockchain suffers from scalability issues due to its linear structure, which increases the storage overhead, and Intrusion detection performed was limited with attack severity, leading to performance degradation. To overcome these issues, we proposed MZWB (Multi-Zone-Wise Blockchain) model. Initially, all the authenticated IoT nodes in the network ensure their legitimacy by using the Enhanced Blowfish Algorithm (EBA), considering several metrics. Then, the legitimately considered nodes for network construction for managing the network using Bayesian-Direct Acyclic Graph (B-DAG), which considers several metrics. The intrusion detection is performed based on two tiers. In the first tier, a Deep Convolution Neural Network (DCNN) analyzes the data packets by extracting packet flow features to classify the packets as normal, malicious, and suspicious. In the second tier, the suspicious packets are classified as normal or malicious using the Generative Adversarial Network (GAN). Finally, intrusion scenario performed reconstruction to reduce the severity of attacks in which Improved Monkey Optimization (IMO) is used for attack path discovery by considering several metrics, and the Graph cut utilized algorithm for attack scenario reconstruction (ASR). UNSW-NB15 and BoT-IoT utilized datasets for the MZWB method simulated using a Network simulator (NS-3.26). Compared with previous performance metrics such as energy consumption, storage overhead accuracy, response time, attack detection rate, precision, recall, and F-measure. The simulation result shows that the proposed MZWB method achieves high performance than existing works     

Fuzzy reputation-based trust model

We present PATROL-F (comPrehensive reputAtion-based TRust mOdeL with Fuzzy subsystems) as a comprehensive model for reputation-based trust incorporating fuzzy subsystems to protect interacting hosts in distributed systems. PATROL-F is the fuzzy version of our previous model PATROL, and aims at achieving a truly unique model incorporating various concepts that are important for the calculation of reputation values and the corresponding decisions of whether or not to trust. Among the incorporated concepts are direct experiences and reputation values, the credibility of a host to give recommendations, the decay of information with time based on a dynamic decay factor, first impressions, and a hierarchy of host systems. The model also implements the concepts of similarity, popularity, activity, and cooperation among hosts. In addition, PATROL-F's fuzzy subsystems account for humanistic and subjective concepts such as the importance of a transaction, the decision in the uncertainty region, and setting the result of interaction. We present simulations of PATROL-F and show its correctness and reliability.