Machine Learning Techniques for SinkHole Attack Prediction in IoT

Sinkhole nodes attempt to falsify source-destination routes in order to attract network traffic using the AOMDV routing protocol. They accomplish this by altering routing control packets so that they publish erroneous routing information, which makes sinkhole nodes seem like the most efficient way to get to particular locations. Routing security has always been a challenge, especially when it comes to detecting and preventing active attacks. SinkHole attacks threaten the network by preventing data availability. In this paper, the main objective is to evaluate the performance of various state-of-the-art classifiers, such as ML Naïve Bayes (NB), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN) algorithms, to accurately detect SinkHole attacks. Our detection model achieved an accuracy of 95.2%, 89.3% and 97.1% respectively for the KNN, SVM and ANN-based models. Detecting attacks using machine learning involves training a model to recognize patterns associated with such attacks, then using the model to classify incoming network traffic as normal or potentially malicious. It is important to note that machine learning models for detecting SinkHole attacks should be part of an overall cybersecurity strategy, and not the only method of defense.