Black Hole Attack Detection Using K-Nearest Neighbor Algorithm and Reputation Calculation in Mobile Ad Hoc Networks

Abstract

The characteristics of the mobile ad hoc network (MANET), such as no need for infrastructure, high speed in setting up the network, and no need for centralized management, have led to the increased popularity and application of this network in various fields. Security is one of the essential aspects of MANETs. Intrusion detection systems (IDSs) are one of the solutions used to ensure security in this network. Clustering-based IDSs are very popular in this network due to their features, such as proper scalability. This paper proposes a new algorithm in MANETs to detect black hole attack using the K-nearest neighbor (KNN) algorithm for clustering and fuzzy inference for selecting the cluster head. With the use of beta distribution and Josang mental logic, the trust of each node will be calculated. According to the reputation and remaining energy, fuzzy inference will select the cluster head. Finally, the trust server checks the destination node. If allowed, it notifies the cluster head; otherwise, it detects the node as a malicious node in the black hole attack in each cluster. The simulation results show that the proposed method has improved the packet loss rate, throughput, packet delivery ratio, total network delay, and normalized routing load parameters compared with recent black hole detection methods.

Crossover Boosted Grey Wolf Optimizer‐based framework for leader election and resource allocation in Intrusion Detection Systems for MANETs

“…Farahani 30 demonstrated a KNN based intrusion detection model to find the black hole attack in MANET. The cluster head was selected through fuzzy interference based on the remaining energy and reputation.…”

Artificial Neural Network-Based Mechanism to Detect Security Threats in Wireless Sensor Networks

“…An effective approach proposed in [ 6 ] was used to identify black-hole attacks with notable efficiency. The occurrence of a black-hole attack leads to notable enhancements in crucial network parameters such as TH, TND, NRL, PDR, and PLR.…”

“…Routing attacks manipulate the routing process to disrupt the accurate transmission of data, leading to network congestion, data loss, and possible denial of service. These attacks on the network layer come in several forms, such as flooding attacks, spoofing attacks, sybil attacks, black-hole attacks, gray-hole attacks, sink-hole attacks, and wormhole attacks [ 6 ]. Some of these types of attack are shown in Figure 1 .…”

Machine Learning Use-Cases in C-ITS Applications

“…Similarly, Pandey and Singh [86] use SVM to detect nodes performing blackhole attack with 95% accuracy by observing their energy consumption. An unsupervised, clustering based anomaly detection is performed in the work of Farahani [87]. In this work k-NN model is implemented to identify fraud clusters in the latent space of nodes with high confidence.…”