Detecting Black Hole Attack using Support Vector Machine with XGBoosting in Mobile Ad-Hoc Networks
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Abstract
Mobile Ad-Hoc Networks (MANET) is a type of ad-hoc networks which use less infrastructure, that means the nodes in this network forward the massages without the need of infrastructure such as routers, switches etc. One of the most used attacks that can affect MANET performance is the black hole attack. This attack leads to dropping the packets that means these packets will never arrive and it will decrease the delivery ratio for the packets. This attack is a real problem as the sender is not informed that the data has not reached the intended receiver. The main goal of this study is to propose a solution for detecting black hole attacks using Extreme Gradient Boosting (XGBoost) based on a Support Vector Machine (SVM), the system for detection seeks to examine network traffic and spot anomalies by examining node activities. Attacking nodes in black hole situations exhibit specific behavioural traits that set them apart from other nodes, the traffic under a black hole attack is created using an NS-2 simulator to test the effectiveness of this strategy, and the malicious node is then identified based on the classification of the traffic into malicious and non-malicious. The results of the proposed technique outperformed the existing machine learning techniques such as Neural Network (NN), SVM, k-Nearest Neighbors (KNN), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), AdaBoost-SVM in terms of accuracy score as it achieved 98.67% as well as other classification performance measures (Precision, Recall, and F-measure).
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