Browsing by Author "Moussa, Sherif"
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- ItemA 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.
- ItemA 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.
- ItemAutoencoder-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.
- ItemConditional 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.
- ItemFeature 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.
- ItemFPGA 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.
- ItemInternet 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.
- ItemKDE-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.
- ItemLoRa-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, LoayThis paper presents a Lora-enabled GPU-based CubeSat Neural-Network Real-Time Object Detection with hyperparameter optimization is presented. When number of epochs increased from 10 to 50 and learning rate tuned to 0.00104, the validation loss improved to 0.018 and training object loss improved to 0.042. Model mean average precision mAP_0.5 is improved to 0.986 and the precision is improved to 98.9%. Epoch and learning rate are traded-off to optimize model accuracy performance. The Lora-enabled CubeSat onboard transceiver provides long range onboard sensors readings providing spaced-based IoT application capability. © 2022 IEEE.
- ItemMIMO-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.
- ItemMIMO-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.
- ItemMonotonicity of the χ2-statistic and Feature Selection(Springer Science and Business Media Deutschland GmbH, 2022) Kamalov, Firuz; Leung, Ho Hon; Moussa, SherifFeature selection is an important preprocessing step in analyzing large scale data. In this paper, we prove the monotonicity property of the χ2-statistic and use it to construct a more robust feature selection method. In particular, we show that χY,X12≤χY,(X1,X2)2. This result indicates that a new feature should be added to an existing feature set only if it increases the χ2-statistic beyond a certain threshold. Our stepwise feature selection algorithm significantly reduces the number of features considered at each stage making it more efficient than other similar methods. In addition, the selection process has a natural stopping point thus eliminating the need for user input. Numerical experiments confirm that the proposed algorithm can significantly reduce the number of features required for classification and improve classifier accuracy. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
- ItemNeural 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.
- ItemOFDM 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.
- ItemOFDM 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.
- ItemOrthogonal 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.
- ItemRapid 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.
- ItemRapid prototyping of MIMO-OFDM based on parity bit selected and permutation spreading(John Wiley and Sons Ltd, 2016) Moussa, Sherif; Razik, Ahmed M. Abdel; Dahmane, Adel Omar; D'Amours, Claude; Hamam, HabibSummary In this paper, a novel MIMO-OFDM transmission scheme is developed to effectively enable multi-access by joint code design across multiple antennas, subcarriers, OFDM frames, and users. It achieves better spectrum efficiency while improving bit error rate performance. The proposed scheme uses either parity bit selected or permutation techniques to assign spreading codes at the transmitter side. As a result, the detection at the receiver is greatly improved because of the fact that identifying the spreading code(s) directly yields the transmitted data symbols. The paper also investigates the field-programmable gate array implementation of the proposed algorithms; optimization techniques are proposed to reduce area, power, and time. These techniques include a pipelined architecture for inverse FFT/FFT blocks, an efficient low complexity algorithm for despreading based on counters and comparators and an optimized architecture for complex matrix inversion using Gauss-Jordan elimination (GJ-elimination). Finally, the fixed-point optimized field-programmable gate array architecture for MIMO-OFDM transceiver is developed, where the maximum allowed performance loss because of quantization is defined, the tradeoffs between BER performance and area reduction are investigated. Copyright © 2015 John Wiley & Sons, Ltd.
- ItemReview - Challenges of mobility aware MAC protocols in WSN(Institute of Electrical and Electronics Engineers Inc., 2018) Sudheendran, Sijo; Bouachir, Ons; Moussa, Sherif; Dahmane, Adel OmarIn today's smart world WSN plays an important role in IoT. The WSN nodes can be used for wildlife, patient, air quality monitoring. WSN consists of numerous sensor nodes that are connected to each other. One of the major concerns of WSN is the mobility of nodes. The mobility of the nodes creates concern to the MAC protocols that's defined for WSN static nodes. Mobile-WSN demands the participated nodes to send packets with a bursty traffic, low energy consumption and reliable connection. MAC protocol is the most important in designing the WSN as MAC plays an important role regarding throughput, mobility, security and energy consumption. This paper gives a review on mobility aware protocols such as M-MAC, MA-MAC, MMH-MAC, M-Contiki, MobiIQ, MobiDisc. © 2018 IEEE.
- ItemRF LNA with Active Inductor Linearizer for Wireless Communication(Institute of Electrical and Electronics Engineers Inc., 2021) El-Khatib, Ziad; Kamalov, Firuz; Moussa, Sherif; Algindy, AhmedThis paper presents the design of a fully-integrated RF low noise amplifier with active inductor linearizer. The proposed circuit achieves 16 dBc of third order intermodulation IM3 distortion cancellation at 1.9 GHz with 8 dB third-order intercept point IP3 improvement. The proposed circuit utilize an active inductor linearizer to improve linearity of the circuit and offering tunability. The simulation results show an overall proposed circuit peak gain is 14.5 dB and the minimum noise Figure is 0.75 dB at 1.9 GHz frequency with power consumption of 7.4 mA. © 2021 IEEE.