Malicious node detection using SVM and secured data storage using blockchain in WSN

Wireless Sensor Networks (WSNs) have numerous important applications, but their vulnerability to malicious nodes seriously compromises data integrity and network reliability. This research addresses the urgent issue of detecting malicious nodes in WSNs, focusing on enhancing security measures. The method suggested utilizes Support Vector Machines (SVM) to identify harmful nodes in WSNs. A strong defence mechanism is provided by SVM, which analyses node behaviour and detects anomalies using machine learning algorithms, thus protecting against possible attacks. In addition, transparent and tamper-proof record-keeping and secure data storage are ensured through blockchain technology. This research combines SVM-based malicious node identification with blockchain technology to enable secure data storage in WSNs. By combining both methods, the resilience of WSNs is enhanced, ensuring data security and network dependability. Malicious nodes are successfully identified by the suggested methodology, as indicated by the experimental results. By utilizing blockchain technology, data storage becomes more secure and immutable, enhancing the resistance of WSNs against attacks.