Blockchain and AI-Integrated Security Framework for Wireless Ad Hoc Networks using Support Vector Machine (SVM)

In this study, we aim to propose a Blockchain and AI-Integrated Security Framework for Wireless Ad Hoc Networks that utilizes machine learning classifiers like Support Vectors Machine (SVM) in order to provide an evolving cyber threats-resistant network. By virtue of the blockchain mechanism, the framework is able to create a security architecture with decentralized and tamper-resistant features, which lead to aspects like safe authentication, data integrity, and trust management among network nodes. Besides, SVM, as an efficient algorithm for anomaly detection and classification, helps to spot the presence of harmful activities within the network. The consensus mechanism combines the advantage of the blockchain with the higher effectiveness, that is transaction verification and the speed of the process without involving the security in jeopardy. The feature selection technique is exploited to make error detection more precise by cleaning the data set before the classification takes place. In addition, the dynamic trust model rating biases the objective of the network, evaluates the behavior of the node, and limits the performance of the insider threat. The linkage of Blockchain with SVM-based intrusion detection is the one offering most positive results as it equips wireless ad hoc networks with real-time threat detection thus reducing false alarms and enhancing the decision-making process. The proposed framework is configured to function in dynamic network environments and still consume a low level of processing resources which allows it to work on the devices with fewer resources. The system can be implemented in this way to help by reducing the number of failures and by providing secure and efficient communication in wireless ad hoc networks.