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

Gholamreza Farahani,Shehzad Ashraf Chaudhry

doi:10.1155/2021/8814141

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.

Highlights

Today, in many environments, security is based on an indepth defense approach, in which multiple layers of defense are used to prevent enemies from violating security policies.is approach assumes that even if the enemy infiltrates one of the defensive layers, he will not be able to inflict damage because the other layers will provide an adequate level of support.A mobile ad hoc network (MANET) is a network without infrastructure and a selfconfigured network of mobile devices that are connected wirelessly
Each node after the transaction with another node, depending on how the opposite node behaves during the transaction and the quality of service received, evaluates that transaction and considers it as a positive (p) or negative (n) transaction. en, at certain intervals, each node calculates the amount of trust in that node according to the number of positive and negative transactions stored for each neighboring node. e proposed scheme uses the beta distribution and Josang mental logic to calculate trust. erefore, before explaining the proposed method for calculating direct trust between two nodes, these two methods and how to use them in calculating trust have been described
Cluster head selection based on fuzzy logic: given that the purpose of this paper is to improve network security and energy has always been one of the most important parameters in MANETs, the two parameters of remaining energy of the node and the maximum number of trustable neighbors are considered as fuzzy system inputs

Summary

Introduction

In many environments, security is based on an indepth defense approach, in which multiple layers of defense are used to prevent enemies from violating security policies. A MANET is a network without infrastructure and a selfconfigured network of mobile devices that are connected wirelessly. Each device in a MANET has complete independence and freedom to move in any direction; in many cases, the connection between each mobile device and other devices is changing. Without the need for a fixed communication infrastructure to create a dynamic network, the importance of MANETs in applications such as military battlefield communications, relief and emergency operations, environmental protection, taxi networks, and independent space communications is increasing. Growing demand for MANETs has raised many concerns about security issues, especially for sensitive security applications. MANETs are inherently insecure due to shared wireless media and the lack of any central control. e unique characteristics of MANETs have created new challenges for security design [1]

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Conclusion