Browsing by Author "Moussa, Sherif"
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Item A Comparative Study of Autoregressive and Neural Network Models: Forecasting the GARCH Process(Springer Science and Business Media Deutschland GmbH, 2022) Kamalov, Firuz; Gurrib, Ikhlaas; Moussa, Sherif; Nazir, AmrilThe Covid-19 pandemic has highlighted the importance of forecasting in managing public health. The two of the most commonly used approaches for time series forecasting methods are autoregressive (AR) and deep learning models (DL). While there exist a number of studies comparing the performance of AR and DL models in specific domains, there is no work that analyzes the two approaches in the general context of theoretically simulated time series. To fill the gap in the literature, we conduct an empirical study using different configurations of generalized autoregressive conditionally heteroskedastic (GARCH) time series. The results show that DL models can achieve a significant degree of accuracy in fitting and forecasting AR-GARCH time series. In particular, DL models outperform the AR-based models over a range of parameter values. However, the results are not consistent and depend on a number of factors including the DL architecture, AR-GARCH configuration, and parameter values. The study demonstrates that DL models can be an effective alternative to AR-based models in time series forecasting. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item A new watermarking scheme for digital videos using DCT(Walter de Gruyter GmbH, 2022-01-01) Al-Gindy, Ahmed; Omar, Aya Al-Chikh; Mashal, Omar; Shaker, Yomna; Alhogaraty, Eslam; Moussa, SherifWith the advent of high-speed broadband Internet access, the need to protect digital videos is highly recommended. The main objective of this study is to propose an adaptive algorithm for watermarked digital videos in the frequency domain based on discrete cosine transform (DCT). The watermark signature image is embedded into the whole frame of the video. The green channel of the RGB frame is selected for the embedding process using the DCT algorithm as it shows the recommended quality of the watermarked frames. The experiment results indicate that the proposed algorithm shows robustness and high quality of the watermarked videos by testing various strength values Δ for different videos. It offers resistance against different types of attacks. © 2022 Ahmed Al-Gindy et al., published by De Gruyter.Item Autoencoder-based Intrusion Detection System(Institute of Electrical and Electronics Engineers Inc., 2021) Kamalov, Firuz; Zgheib, Rita; Leung, Ho Hon; Al-Gindy, Ahmed; Moussa, SherifGiven the dependence of the modern society on networks, the importance of effective intrusion detection systems (IDS) cannot be underestimated. In this paper, we consider an autoencoder-based IDS for detecting distributed denial of service attacks (DDoS). The advantage of autoencoders over traditional machine learning methods is the ability to train on unlabeled data. As a result, autoencoders are well-suited for detecting unknown attacks. The key idea of the proposed approach is that anomalous traffic flows will have higher reconstruction loss which can be used to flag the intrusions. The results of numerical experiments show that the proposed method outperforms benchmark unsupervised algorithms in detecting DDoS attacks. © 2021 IEEE.Item Conditional Variational Autoencoder-Based Sampling(Springer Science and Business Media Deutschland GmbH, 2023) Kamalov, Firuz; Ali-Gombe, Adamu; Moussa, SherifImbalanced data distribution implies an uneven distribution of class labels in data which can lead to classification bias in machine learning models. The present paper proposes an autoencoder-based sampling approach to balance the data. Concretely, the proposed method utilizes a conditional variational autoencoder (VAE) to learn the latent variables underpinning the distribution of minority labels. Then, the trained encoder is employed to produce new minority samples to equalize the sample distribution. The results of numerical experiments reveal the potency of the suggested technique on several datasets. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Critical Controlling for the Network Security and Privacy Based on Blockchain Technology: A Fuzzy DEMATEL Approach(Multidisciplinary Digital Publishing Institute (MDPI), 2023-07) Kamalov, Firuz; Gheisari, Mehdi; Liu, Yang; Feylizadeh, Mohammad Reza; Moussa, SherifThe Internet of Things (IoT) has been considered in various fields in the last decade. With the increasing number of IoT devices in the community, secure, accessible, and reliable infrastructure for processing and storing computed data has become necessary. Since traditional security protocols are unsuitable for IoT devices, IoT implementation is fraught with privacy and security challenges. Thus, blockchain technology has become an effective solution to the problems of IoT security. Blockchain is an empirical data distribution and storage model involving point-to-point transmission, consensus mechanism, asymmetric encryption, smart contract, and other computer technologies. Security and privacy are becoming increasingly important in using the IoT. Therefore, this study provides a comprehensive framework for classifying security criteria based on blockchain technology. Another goal of the present study is to identify causal relationship factors for the security issue using the Fuzzy Decision-Making Trial-and-Evaluation Laboratory (FDEMATEL) approach. In order to deal with uncertainty in human judgment, fuzzy logic is considered an effective tool. The present study’s results show the proposed approach’s efficiency. Authentication (CR6), intrusion detection (CR4), and availability (CR5) were also introduced as the most effective and essential criteria, respectively. © 2023 by the authors.Item Feature selection for intrusion detection systems(Institute of Electrical and Electronics Engineers Inc., 2020-12) Kamalov, Firuz; Moussa, Sherif; Zgheib, Rita; Mashaal, OmarIn 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.Item FPGA implementation of floating-point complex matrix inversion based on GAUSS-JORDAN elimination(2013) Moussa, Sherif; Razik, Ahmed M. Abdel; Dahmane, Adel Omar; Hamam, HabibThis work presents the architecture of an optimized complex matrix inversion using GAUSS-JORDAN elimination (GJ-elimination) on FPGA with single precision floating-point representation to be used in MIMO-OFDM receiver. This module consists of single precision floating point arithmetic components and control unit which perform the GJ-elimination algorithm. The proposed architecture performs the GJ-elimination for complex matrix element by element. Only critical arithmetic operations are calculated to get the needed values without performing all the arithmetic operations of the GJ-elimination algorithm. This results in a reduced hardware resources and execution time. © 2013 IEEE.Item Intelligent Indoor Positioning Systems: The Case of Imbalanced Data(Springer Science and Business Media Deutschland GmbH, 2023) Kamalov, Firuz; Moussa, Sherif; Reyes, Jorge AvanteThe ubiquity of Wi-Fi over the last decade has led to increased popularity of intelligent indoor positioning systems (IPS). In particular, machine learning has been recently utilized to develop intelligent IPS. Most of the existing research focus on developing intelligent IPS using balanced data. In this paper, we investigate a hitherto unexamined issue of imbalanced data in the context of machine learning-based IPS. We consider several traditional machine learning algorithms to determine the optimal method for training IPS on imbalanced data. We also analyze the effect of imbalance ratio on the performance of the IPS. The results show that the k-nearest neighbors algorithm provides the best approach to developing intelligent IPS for imbalanced data. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Internet of Medical Things Privacy and Security: Challenges, Solutions, and Future Trends from a New Perspective(MDPI, 2023-02) Kamalov, Firuz; Pourghebleh, Behrouz; Gheisari, Mehdi; Liu, Yang; Moussa, SherifThe Internet of Medical Things (IoMT), an application of the Internet of Things (IoT) in the medical domain, allows data to be transmitted across communication networks. In particular, IoMT can help improve the quality of life of citizens and older people by monitoring and managing the body’s vital signs, including blood pressure, temperature, heart rate, and others. Since IoMT has become the main platform for information exchange and making high-level decisions, it is necessary to guarantee its reliability and security. The growth of IoMT in recent decades has attracted the interest of many experts. This study provides an in-depth analysis of IoT and IoMT by focusing on security concerns from different points of view, making this comprehensive survey unique compared to other existing studies. A total of 187 articles from 2010 to 2022 are collected and categorized according to the type of applications, year of publications, variety of applications, and other novel perspectives. We compare the current studies based on the above criteria and provide a comprehensive analysis to pave the way for researchers working in this area. In addition, we highlight the trends and future work. We have found that blockchain, as a key technology, has solved many problems of security, authentication, and maintenance of IoT systems due to the decentralized nature of the blockchain. In the current study, this technology is examined from the application fields’ points of view, especially in the health sector, due to its additional importance compared to other fields. © 2023 by the authors.Item KDE-Based Ensemble Learning for Imbalanced Data(MDPI, 2022-09) Kamalov, Firuz; Moussa, Sherif; Avante Reyes, JorgeImbalanced class distribution affects many applications in machine learning, including medical diagnostics, text classification, intrusion detection and many others. In this paper, we propose a novel ensemble classification method designed to deal with imbalanced data. The proposed method trains each tree in the ensemble using uniquely generated synthetically balanced data. The data balancing is carried out via kernel density estimation, which offers a natural and effective approach to generating new sample points. We show that the proposed method results in a lower variance of the model estimator. The proposed method is tested against benchmark classifiers on a range of simulated and real-life data. The results of experiments show that the proposed classifier significantly outperforms the benchmark methods. © 2022 by the authors.Item LoRa-enabled GPU-based CubeSat Yolo Object Detection with Hyperparameter Optimization(Institute of Electrical and Electronics Engineers Inc., 2022) Khatib, Ziad El; Mnaouer, Adel Ben; Moussa, Sherif; Abas, Mohd Azman Bin; Ismail, Nor Azman; Abdulgaleel, Fuad; Elmasri, Ibrahim; Ashraf, LoayItem MIMO-OFDM scheme based on parity bit selected spreading(Institute of Electrical and Electronics Engineers Inc., 2012) Moussa, Sherif; Dahmane, Adel Omar; D'Amours, Claude; Hamam, HabibIn this paper, a novel transmission scheme is developed to effectively combine parity bit selected spreading technique and MIMO-OFDM to obtain improved bit error rate performance in the presence of frequency selective fading channels with low system complexity. Unlike conventional MIMO-OFDMA, where users are separated in different frequency bands (subchannels), and each user is coded separately using STBC or SFBC, the proposed new scheme enables multi access by joint code design across multiple antennas, subcarriers, and users. Such system will benefit from the combined space and frequency domain freedom as well as multiuser diversity. Hence, better spectrum efficiency is achieved while improving bit error rate performance with respect to signal-to- interference ratio. © 2012 IEEE.Item MIMO-OFDM scheme based on permutation spreading(Institute of Electrical and Electronics Engineers Inc., 2012) Moussa, Sherif; Dahmane, Adel Omar; D'Amours, Claude; Hamam, HabibIn this paper, a novel transmission scheme is developed to effectively combine permutation spreading technique with MIMO-OFDM to obtain improved bit error rate performance in the presence of frequency selective fading channels with low system complexity. Unlike conventional MIMO-OFDMA, where users are separated in different frequency bands (subchannels), and each user is coded separately using STBC or SFBC, the proposed new scheme enables multi access by joint code design across multiple antennas, subcarriers, and users. Such system will benefit from the combined space and frequency domain freedom as well as multiuser diversity. Hence, better spectrum efficiency is achieved while improving bit error rate performance with respect to signal-to-interference ratio © 2012 GIRI.Item Monotonicity of the χ2-statistic and Feature Selection(Springer Science and Business Media Deutschland GmbH, 2022) Kamalov, Firuz; Leung, Ho Hon; Moussa, SherifItem Nested ensemble selection: An effective hybrid feature selection method(Elsevier Ltd, 2023-09) Kamalov, Firuz; Sulieman, Hana; Moussa, Sherif; Reyes, Jorge Avante; Safaraliev, MurodbekIt has been shown that while feature selection algorithms are able to distinguish between relevant and irrelevant features, they fail to differentiate between relevant and redundant and correlated features. To address this issue, we propose a highly effective approach, called Nested Ensemble Selection (NES), that is based on a combination of filter and wrapper methods. The proposed feature selection algorithm differs from the existing filter-wrapper hybrid methods in its simplicity and efficiency as well as precision. The new algorithm is able to separate the relevant variables from the irrelevant as well as the redundant and correlated features. Furthermore, we provide a robust heuristic for identifying the optimal number of selected features which remains one of the greatest challenges in feature selection. Numerical experiments on synthetic and real-life data demonstrate the effectiveness of the proposed method. The NES algorithm achieves perfect precision on the synthetic data and near optimal accuracy on the real-life data. The proposed method is compared against several popular algorithms including mRMR, Boruta, genetic, recursive feature elimination, Lasso, and Elastic Net. The results show that NES significantly outperforms the benchmarks algorithms especially on multi-class datasets. © 2023 The Author(s)Item 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, FuadA 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.Item OFDM with parity bit selected block spreading(Institute of Electrical and Electronics Engineers Inc., 2010) Moussa, Sherif; Dahmane, Adel Omar; D'Amours, Claude; Hamam, HabibIn this paper, a new scheme that combines Orthogonal Frequency Division Multiplexing (OFDM) with parity bit selected block spreading is introduced. In this proposed method, the data symbols in each block is used to select the spreading code. Therefore, frequency diversity in the system is increased as well as improving the symbol detection due to the inherent coding in the spreading technique. Simulation results show significant improvement in BER performance for the proposed system compared with conventional OFDM one. © 2009 IEEE.Item OFDM with permutation block spreading(Institute of Electrical and Electronics Engineers Inc., 2011) Moussa, Sherif; Dahmane, Adel Omar; D'Amours, Claude; Hamam, HabibIn this paper, a new Orthogonal Frequency Division Multiplexing (OFDM) with Permutation block spreading scheme is introduced; the transmitted stream is divided into blocks and the data symbols in each block is used to select the spreading code. Therefore, the increased frequency diversity in the system due to spreading improves bit error rate performance in the presence of frequency selective fading channels. In addition to that the new scheme improves symbol detection due to the inherited coding in the spreading technique. Simulation results show significant improvement in BER performance for the proposed system compared with conventional OFDM. © 2011 IEEE.Item Orthogonal variance-based feature selection for intrusion detection systems(Institute of Electrical and Electronics Engineers Inc., 2021) Kamalov, Firuz; Moussa, Sherif; Khatib, Ziad El; Mnaouer, Adel BenIn this paper, we apply a fusion machine learning method to construct an automatic intrusion detection system. Concretely, we employ the orthogonal variance decomposition technique to identify the relevant features in network traffic data. The selected features are used to build a deep neural network for intrusion detection. The proposed algorithm achieves 100% detection accuracy in identifying DDoS attacks. The test results indicate a great potential of the proposed method. © 2021 IEEE.Item Rapid prototyping of channel estimation techniques in MIMO-OFDM systems(2013) Hogue, Marie-Josée Vincent; Dahmane, Adel Omar; Moussa, Sherif; D'Amours, ClaudeIn this paper, reduced complexity channel estimation techniques that was first proposed in case of Single Input Single Output Orthogonal Frequency Division Multiplexing (SISO-OFDM) is generalized in case of multiple-input multiple-output-OFDM (MIMO-OFDM) systems. The impact of complexity reduction is analyzed through floating point simulation, fixed point simulation and FPGA implementation using rapid prototyping. © 2013 IEEE.