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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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Journal : IAES International Journal of Artificial Intelligence (IJ-AI)

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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