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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Alaeddine Boukhalfa
Ibn Tofail University
Computer Science & IT Electrical & Electronics Engineering Engineering

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LSTM deep learning method for network intrusion detection system Alaeddine Boukhalfa; Abderrahim Abdellaoui; Nabil Hmina; Habiba Chaoui
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (822.858 KB) | DOI: 10.11591/ijece.v10i3.pp3315-3322

Abstract

The security of the network has become a primary concern for organizations. Attackers use different means to disrupt services or steal information, these various attacks push to think of a new way to block them all in one manner. In addition, these intrusions can change and penetrate the devices of security. To solve these issues, we suggest, in this paper, a new idea for Network Intrusion Detection System (NIDS) based on Long Short-TermMemory (LSTM) to recognize menaces and to obtain a long-term memory on them, inorder to stop the new attacks that are like the existing ones, and at the sametime, to have a single mean to block intrusions. According to the results of the experiments of detections that we have carried out, the Accuracy reaches upto 99.98 % and 99.93 % for respectively the classification of two classes and several classes, Also the False Positive Rate (FPR) reaches up to only 0,068 % and 0,023 % for respectively the classification of two classes and several classes, which proves that the proposed model is very effective, it has a great ability to memorize and differentiate between normal traffic and attack traffic and its identification is more accurate than other Machine Learning classifiers.
Parallel processing using big data and machine learning techniques for intrusion detection Alaeddine Boukhalfa; Nabil Hmina; Habiba Chaoni
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (179.591 KB) | DOI: 10.11591/ijai.v9.i3.pp553-560

Abstract

Currently, information technology is used in all the life domains, multiple devices produce data and transfer them across the network, these transfers are not always secured, they can contain new menaces invisible by the current security devices. Moreover, the large amount and variety of the exchanged data cause difficulties related to the detection time. To solve these issues, we suggest in this paper, a new approach based on storing the large amount and variety of network traffic data employing Big Data techniques, and analyzing these data with Machine Learning algorithms, in a distributed and parallel way, in order to detect new hidden intrusions with less processing time. According to the results of the experiments, the detection accuracy of the Machine Learning methods reaches 99.9 %, and their processing time has been reduced considerably by applying them in a parallel and distributed way, which proves that our proposed model is effective for the detection of new intrusions.
Co-Authors Abderrahim Abdellaoui Habiba Chaoni Habiba Chaoui Nabil Hmina Nabil Hmina