Browsing by Author "Mashaal, Omar"

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    Feature selection for intrusion detection systems
    (Institute of Electrical and Electronics Engineers Inc., 2020-12) Kamalov, Firuz; Moussa, Sherif; Zgheib, Rita; Mashaal, Omar
    In this paper, we analyze existing feature selection methods to identify the key elements of network traffic data that allow intrusion detection. In addition, we propose a new feature selection method that addresses the challenge of considering continuous input features and discrete target values. We show that the proposed method performs well against the benchmark selection methods. We use our findings to develop a highly effective machine learning-based detection systems that achieves 99.9% accuracy in distinguishing between DDoS and benign signals. We believe that our results can be useful to experts who are interested in designing and building automated intrusion detection systems. ©2020 IEEE.
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    Neural network-based parking system object detection and predictive modeling
    (Institute of Advanced Engineering and Science, 2023-03) El Khatib, Ziad; Ben Mnaouer, Adel; Moussa, Sherif; Mashaal, Omar; Ismail, Nor Azman; Abas, Mohd Azman Bin; Abdulgaleel, Fuad
    A neural network-based parking system with real-time license plate detection and vacant space detection using hyper parameter optimization is presented. When number of epochs increased from 30, 50 to 80 and learning rate tuned to 0.001, the validation loss improved to 0.017 and training object loss improved to 0.040. The model means average precision mAP_0.5 is improved to 0.988 and the precision is improved to 99%. The proposed neural network-based parking system also uses a regularization technique for effective predictive modeling. The proposed modified lasso ridge elastic (LRE) regularization technique provides a 5.21 root mean square error (RMSE) and an R-square of 0.71 with a 4.22 mean absolute error (MAE) indicative of higher accuracy performance compared to other regularization regression models. The advantage of the proposed modified LRE is that it enables effective regularization via modified penalty with the feature selection characteristics of both lasso and ridge. © 2023, Institute of Advanced Engineering and Science. All rights reserved.