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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Mohamed Elboukhari
Mohammed 1st University
Computer Science & IT Engineering

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Deep learning intrusion detection system for mobile ad hoc networks against flooding attacks Oussama Sbai; Mohamed Elboukhari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp878-885

Abstract

Mobile ad hoc networks (MANETs) are infrastructure-less, dynamic wireless networks and self-configuring, in which the nodes are resource constrained. With the exponential evolution of the paradigm of smart homes, smart cities, smart logistics, internet of things (IoT) and internet of vehicle (IoV), MANETs and their networks family, such as flying ad-hoc networks (FANETs), vehicular ad-hoc networks (VANETs), and wireless sensor network (WSN), are the backbone of the whole networks. Because of their multitude use, MANETs are vulnerable to various attacks, so intrusion detection systems (IDS) are used in MANETs to keep an eye on activities in order to spot any intrusions into networks. In this paper, we propose a knowledge-based intrusion detection system (KBIDS) to secure MANETs from two classes of distributed denial of service (DDoS) attacks, which are UDP/data and SYN flooding attacks. We use the approach of deep learning exactly deep neural network (DNN) with CICDDoS2019 dataset. Simulation results obtained show that the proposed architecture model can attain very interesting and encouraging performance and results (Accuracy, Precision, Recall and F1-score).
Mobile Ad Hoc networks intrusion detection system against packet dropping attacks Oussama Sbai; Mohamed Elboukhari
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp819-825

Abstract

Due to the extreme lack of a stable infrastructure, also self-organization of network components, unpredictable network topologies, and the lack of a central authority for routing, security assurance in mobile ad hoc networks (MANETs) is an important and difficult challenge. Among the famous threat that MANETs suffer from: blackhole, grayhole, and selfishness attacks, because the target of these attacks is to drop packets and disturb the routing operation of the network. A scalable, reliable, and robust network intrusion detection system (NIDS) should be created to effectively combat these families of network layer routing assaults in order to offer high availability for MANETs. In this paper, we present a MANETs-IDS based on machine learning algorithm against blackhole, grayhole, and selfishness attacks with Ad Hoc on-demand distance vector (AODV) routing protocol (RFC 3561) and optimized link state routing (OLSR) potocol (RFC 3626), using ns-3 simulation platform. Our simulation took into consideration the density of the network and a random mobility model of nodes. The obtained experimental results show that the proposed detection algorithm reached very promoting performances (in term of accuracy, processing time, time to build the model, precision, recall, F-measure).
Co-Authors Oussama Sbai