The implementation of Mobile Ad hoc Networks in numerous domains is necessitated by the fast evolving wireless service requirements as well as deployment demands over the last few decades. The application areas are environmental monitoring, disaster rescue operations, military communications, and other safety-critical sectors.The underlying routing protocol has a considerable impact on the effectiveness of an ad hoc network deployment in a specific situation. MANETs are vulnerable to major security risks that are difficult to combat with current security measures. As a result, several safe routing protocols have been created to improve MANET security. This work proposes a safe and energy-efficient routing mechanism using group key management. Initially, Particle Swarm Optimization (PSO) was adopted for efficient selection of cluster heads and malicious node detection, which aims to establish the trust among connected nodes that can enhance security as every participating node will produce as well as propagate authentic, accurate, and trusted content within the network. Two specialized nodes, dubbed Calculator Key (CK) and Distribution Key (DK), are then responsible for producing, verifying, and distributing secret keys among nodes employing Asymmetric key cryptography. The proposed method is named Optimal Cluster_ Trust Asymmetric Key Management Protocol (OptCH_TAKMP), which is considered an excellent energy-efficient cluster head election process with a securable routing mechanism. It is compared with three state-of-art methods. Simulation results show that proposed OptCH_TAKMP achieves 31.4% of routing overhead, 23% of end-to-end delay, 78.6% of energy efficiency, 94.8% of throughput, 28.2% of average latency, 91.4% of malicious detection rate, and 92.4% of packet delivery ratio, 85.2% of network lifetime, 19.2% of communication cost and 28.6% of trust computation error.
Cybersecurity continues to be a major issue for all industries engaged in digital activity given the cyclical surge in security incidents. Since more Internet of Things (IoT) devices are being used in homes, offices, transportation, healthcare, and other venues, malicious attacks are happening more frequently. Since distance between IoT as well as fog devices is closer than distance between IoT devices as well as the cloud, attacks can be quickly detected by integrating fog computing into IoT. Due to the vast amount of data produced by IoT devices, ML is commonly employed for attack detection. This research proposes novel technique in cybersecurity-based network traffic analysis and malicious attack detection using IoT artificial intelligence techniques for a sustainable smart city. A traffic analysis has been carried out using a kernel quadratic vector discriminant machine which enhances the data transmission by reducing network traffic. This enhances energy efficiency with reduced traffic. Then, the malicious attack detection is carried out using adversarial Bayesian belief networks. The experimental analysis has been carried out in terms of throughput, data traffic analysis, end-end delay, packet delivery ratio, energy efficiency, and QoS. The proposed technique attained a throughput of 98%, data traffic analysis of 74%, end-end delay of 45%, packet delivery ratio of 92%, energy efficiency of 92%, and QoS of 79%.
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