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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Imane Kerrakchou
Mohammed First University
Computer Science & IT Engineering

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A survey and analysis of intrusion detection models based on information security and object technology - cloud intrusion dataset (ISOT-CID) Yassine Ayachi; Youssef Mellah; Mohammed Saber; Noureddine Rahmoun; Imane Kerrakchou; Toumi Bouchentouf
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

Nowadays society, economy, and critical infrastructures have become principally dependent on computers, networks, and information technology solutions, on the other side, cyber-attacks are becoming more sophisticated and thus presenting increasing challenges in accurately detecting intrusions. Failure to prevent intrusions could compromise data integrity, confidentiality, and availability. Different detection methods are proposed to tackle computer security threats, which can be broadly classified into anomaly-based intrusion detection systems (AIDS) and signature-based intrusion detection systems (SIDS). One of the most preferred AIDS mechanisms is the machine learning-based approach which provides the most relevant results ever, but it still suffers from disadvantages like unrepresentative dataset, indeed, most of them were collected during a limited period of time, in some specific networks and mostly don't contain up-to-date data. Additionally, they are imbalanced and do not hold sufficient data for all types of attacks, especially new attack types. For this reason, up-to-date datasets such as information security and object technology-cloud intrusion dataset (ISOT-CID) are very convenient to train predictive models on a cloud-based intrusion detection approach. The dataset has been collected over a sufficiently long period and involves several hours of attack data, culminating into a few terabytes. It is large and diverse enough to accommodate machine-learning studies. 
Modeling the impact of jamming attacks in the internet of things Imane Kerrakchou; Sara Chadli; Yassine Ayachi; Mohammed Saber
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.pp1206-1215

Abstract

Security is a key requirement in the context of the Internet of Things. The IoT is connecting many objects together via wireless and wired connections with the goal of allowing ubiquitous interaction, where all components may communicate with others without constraints. The wireless sensor network is one of the most essential elements of IoT concepts. Because of their unattended and radio-shared nature for communication, security is becoming an important issue. Wireless sensor nodes are susceptible to different types of attacks. Such attacks can be carried out in several various ways. One of the most commonly utilized methods is Jamming. However, there are also some other attack types that we need to be aware of, such as Tampering, Wormhole, etc. In this paper, we have provided an analysis of the layered IoT architecture. A detailed study of different types of Jamming attacks, in a wireless sensor network, is presented. The packet loss rate, energy consumption, etc. are calculated, and the performance analysis of the WSN system is achieved. The protocol chosen to evaluate the performance of the WSN is the S-MAC protocol. Different simulations are realized to evaluate the performance of a network attacked by the different types of Jamming attacks.
Selection of efficient machine learning algorithm on Bot-IoT dataset for intrusion detection in internet of things networks Imane Kerrakchou; Adil Abou El Hassan; Sara Chadli; Mohamed Emharraf; Mohammed Saber
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1784-1793

Abstract

With the growth of internet of things (IoT) systems, they have become the target of malicious third parties. In order to counter this issue, realistic investigation and protection countermeasures must be evolved. These countermeasures comprise network forensics and network intrusion detection systems. To this end, a well-organized and representative data set is a crucial element in training and validating the system's credibility. In spite of the existence of multiple networks, there is usually little information provided about the botnet scenarios used. This article provides the Bot-IoT dataset that embeds traces of both legitimate and simulated IoT networks as well as several types of the attacks. It provides also a realistic test environment to address the drawbacks of existing datasets, namely capturing complete network information, precise labeling, and a variety of recent and complex attacks. Finally, this work evaluates the confidence of the Bot-IoT dataset by utilizing a variety of machine learning and statistical methods. This work will provide a foundation to enable botnet identification on IoT-specific networks.
Co-Authors Adil Abou El Hassan Mohamed Emharraf Mohammed Saber Noureddine Rahmoun Sara Chadli Toumi Bouchentouf Yassine Ayachi Youssef Mellah