Title: Trust-based-tuning of Bayesian-watchdog intrusion detection for fast and improved detection of black hole attacks in mobile ad hoc networks

Authors: Ruchi Makani; B.V.R. Reddy

Addresses: University School of Information and Communication Technology, GGS Indraprastha University, 16-C, Dwarka, 110078, New Delhi, India ' University School of Information and Communication Technology, GGS Indraprastha University, 16-C, Dwarka, 110078, New Delhi, India

Abstract: The 'watchdog' is a well-known intrusion detection mechanism for mobile ad hoc networks (MANET), which not only monitors the traffic between peer nodes but also performs analysis on the data to discern any malicious activity. It was widely adopted for detecting black hole attacks. Watchdog suffers from serious limitation viz. high number of false positive/negative. Integration of the Bayesian filtering in watchdog, improves system performance. The Bayesian-watchdog capability can be further enhanced by its effective tuning. This paper presents the concept of 'trust based tuning' of the Bayesian-watchdog, which is a novel approach towards enhancing the detection speed, eliminating false alarms and improving data throughput. The proposed trust based tuning algorithm dynamically tunes the Bayesian filtering function. The proposed algorithm has been evaluated through simulations and compared with the Bayesian-based watchdog method. Encouraging results, in terms of enhanced data throughput, speed in detection of attacks and accuracy in reporting malicious activity under variable mobility and scalable scenarios have been observed.

Keywords: Bayesian; intrusion detection; mobile ad hoc networks; MANET; trust; watchdog.

DOI: 10.1504/IJAIP.2022.121029

International Journal of Advanced Intelligence Paradigms, 2022 Vol.21 No.1/2, pp.53 - 71

Received: 21 Jan 2017
Accepted: 19 Dec 2017
Published online: 23 Feb 2022 *

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