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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Robotics and Control (JRC)
Khatib, Amine
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

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Enhancing Multi-Robot Systems Cooperation through Machine Learning-based Anomaly Detection in Target Pursuit Khatib, Amine; Hamed, Oussama; Hamlich, Mohamed; Mouchtachi, Ahmed
Journal of Robotics and Control (JRC) Vol 5, No 3 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i3.20333

Abstract

Effectively pursuing dynamically moving targets in the domain of multi-robot systems (MRS) poses a significant challenge. This paper proposes an innovative leader-follower strategy within the MRS framework, enabling robots to dynamically adjust their roles based on target proximity. This approach fosters coordination, allowing robots to act cohesively when pursuing diverse targets, from other robots to mobile objects. The centralized architecture of the MRS facilitates wireless communication, enabling robots to share sensor-derived data providing proximity cues rather than precise location information. However, data anomalies arising from sensor errors, transmission glitches, or encoding issues pose challenges, compromising the reliability of target-related information. To mitigate this, the paper introduces an advanced methodology integrating the leader-follower strategy with Discriminant Analysis (DA)-based anomaly detection. This novel approach validates and filters data, enhancing data integrity and supporting decision-making processes. The integration of DA methods within the leader-follower strategy is detailed, emphasizing steps in anomaly detection implementation, showcasing robustness in selecting high-quality information for decision-making in dynamic environments. The research's real-world relevance addresses the problem of the impact of sensor anomalies on the performance and reliability of MRS in dynamic environments. By integrating machine learning-based anomaly detection, this methodology enhances MRS adaptability and robustness, particularly in scenarios requiring precise target tracking and coordination. Numerical experiments and simulations demonstrate the efficacy of the DA-based anomaly detection and collaborative hunting strategy in MRS. This method contributes to improved target tracking, enhanced system coordination, and streamlined pursuit of dynamic targets, affirming its practical applicability in surveillance, search and rescue operations, and industrial automation.
Network intrusion detection in big datasets using Spark environment and incremental learning Elmoutaoukkil, Abdelwahed; Hamlich, Mohamed; Khatib, Amine; Chriss, Marouane
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4414-4421

Abstract

Internet of things (IoT) systems have experienced significant growth in data traffic, resulting in security and real-time processing issues. Intrusion detection systems (IDS) are currently an indispensable tool for self-protection against various attacks. However, IoT systems face serious challenges due to the functional diversity of attacks, resulting in detection methods with machine learning (ML) and limited static models generated by the linear discriminant analysis (LDA) algorithm. The process entails adjusting the model parameters in real time as new data arrives. This paper proposes a new method of an IDS based on the LDA algorithm with the incremental model. The model framework is trained and tested on the IoT intrusion dataset (UNSW-NB15) using the streaming linear discriminant analysis (SLDA) ML algorithm. Our approach increased model accuracy after each training, resulting in continuous model improvement. The comparison reveals that our dynamic model becomes more accurate after each batch and can detect new types of attacks.
Co-Authors Chriss, Marouane Elmoutaoukkil, Abdelwahed Hamed, Oussama Hamlich, Mohamed Mouchtachi, Ahmed