L. Gnanaprasanambikai’s research while affiliated with Karpagam Academy of Higher Education and other places

The application of wireless sensor networks is not limited to a particular domain. Technology advancements result in innovative solutions for simple communication to large applications via wireless sensor IoT networks. Besides the advancements, there is a serious issue in terms of threats or attacks on wireless sensor networks, which is common. Various intrusion detection methodologies have evolved so far to detect common network attacks. But it is essential to concentrate on other routing attacks like selective forwarding attack, black hole attack, Sybil attack, wormhole attack, identity replication attack, and hello flood attack. Existing research models concentrate on any one of the above-mentioned routing attacks and attain better detection performance. Detecting each attack through different detection mechanisms will increase the overall cost, and it is a tedious process. Considering this factor, in this research work, a novel intrusion detection system is introduced to detect routing attacks in wireless sensor networks using fuzzy and feed-forward neural networks. The experimental results demonstrate that the proposed model attains an average detection rate of 97.8% and a maximum detection accuracy of 98.8%, compared to existing techniques like support vector machine (SVM), decision tree (DT), and random forest (RF) models.