Keywords:
Anomaly Detection
Blackhole Attacks
Cyber security
Wireless
Deep Neural Networks
Network Traffic Analysis

  • Abstract

    Mobile Ad-hoc Networks are highly susceptible to a range of security threats, with Blackhole attacks being particularly challenging due to their stealthy nature. This research paper introduces a novel approach that leverages the power of machine learning and deep learning tech-niques to enhance the intrusion detection capabilities in Mobile Ad-hoc Networks. Machine learning and deep learning are two powerful techniques that can be used to detect blackhole attacks in MANETs. Machine learning techniques can be used to learn the normal behavior of the net-work and then identify any deviations from that behavior as potential at-tacks. Deep learning techniques can learn more complex patterns in the data, which can make them more effective at detecting blackhole attacks. This paper proposes a machine learning and deep learning-based intru-sion detection system for blackhole attacks in MANETs. The system uses a combination of machine learning and deep learning techniques to learn the normal behavior of the network and then identify any deviations from that behavior as potential attacks.

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How to cite

Verma, P. K., Gupta, M., & Vashishth, T. K. (2024). Machine learning and deep learning based intrusion detection for blackhole attacks in mobile ad-hoc networks. Multidisciplinary Science Journal, (| Accepted Articles). Retrieved from https://malque.pub/ojs/index.php/msj/article/view/2201
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