Samir Gaber Sayed Abdelgawad

@helwan.edu.eg

Electronics & Communcations Engineering Department - Faculty of Engineering in Helwan
Helwan University

Samir Gaber Sayed Abdelgawad


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https://researchid.co/sgsayed

RESEARCH, TEACHING, or OTHER INTERESTS

Electrical and Electronic Engineering, Computer Networks and Communications, Computer Engineering
40

Scopus Publications

Scopus Publications

  • AegisGuard: A Multi-Stage Hybrid Intrusion Detection System with Optimized Feature Selection for Industrial IoT Security
    Mounir Mohammad Abou Elasaad, Samir G. Sayed, Mohamed M. El-Dakroury
    Sensors, 2025
    The rapid expansion of the Industrial Internet of Things (IIoT) within smart grid infrastructures has increased the risk of sophisticated cyberattacks, where severe class imbalance and stringent real-time requirements continue to hinder the effectiveness of conventional intrusion detection systems (IDSs). Existing approaches often achieve high accuracy on specific datasets but lack generalizability, interpretability, and stability when deployed across heterogeneous IIoT environments. This paper introduces AegisGuard, a hybrid intrusion detection framework that integrates an adaptive four-stage sampling process with a calibrated ensemble learning strategy. The sampling module dynamically combines SMOTE, SMOTE-ENN, ADASYN, and controlled under sampling to mitigate the extreme imbalance between benign and malicious traffic. A quantum-inspired feature selection mechanism then fuses statistical, informational, and model-based significance measures through a trust-aware weighting scheme to retain only the most discriminative attributes. The optimized ensemble, comprising Random Forest, Extra Trees, LightGBM, XGBoost, and CatBoost, undergoes Optuna-based hyperparameter tuning and post-training probability calibration to minimize false alarms while preserving accuracy. Experimental evaluation on four benchmark datasets demonstrates the robustness and scalability of AegisGuard. On the CIC-IoT 2023 dataset, it achieves 99.6% accuracy and a false alarm rate of 0.31%, while maintaining comparable performance on TON-IoT (98.3%), UNSW-NB15 (98.4%), and Bot-IoT (99.4%). The proposed framework reduces feature dimensionality by 54% and memory usage by 65%, enabling near real-time inference (0.42 s per sample) suitable for operational IIoT environments.
  • Smart Grid intrusion detection system based on AI techniques
    Mounir Mounir, Samir G. Sayed, Mohamed M. El El-Dakroury
    Journal of Cybersecurity and Information Management, 2025
    Smart grids (SGs) are integral to modern utility systems, managing power generation, energy consumption, and communication networks. However, as these systems become increasingly interconnected, they are exposed to sophisticated cyber threats that can compromise their functionality and security. To address these challenges, this paper presents an AI-driven detection framework designed to significantly enhance cybersecurity in smart grids. The proposed system combining Recurrent Neural Networks (RNNs) with Support vector classifier to improve detection accuracy, recognition capabilities, and system robustness. The methodology comprises four main stages: (1) data preprocessing to ensure high-quality input for analysis, (2) traffic detection using RNNs to capture temporal patterns, (3) classification of traffic as normal or abnormal via support vector classifier (SVC), and (4) identification of specific attack types through another SVC for refined threat categorization. This integrated approach enables real-time detection of both known and emerging threats, focusing on minimizing false positives and maximizing detection precision. The system was evaluated on three comprehensive benchmark datasets: UNSW_NB15 and BoT-IoT, achieving an average accuracy of 100%. These results underscore the superiority of this AI-based solution over traditional intrusion detection systems, providing a robust and scalable framework for securing smart grids and other critical infrastructures.
  • Securing the Future: Real-Time intrusion Detection in IIoT Smart Grids through Innovative AI Solutions
    Mounir Mounir, Samir G. Sayed, Mohamed M. El El-Dakroury
    Journal of Cybersecurity and Information Management, 2025
    The world is witnessing an unprecedented boom in the development of information technology, which has come to encompass all aspects of life, Smart networks based on the Industrial Internet of Things (IIoT) are among the latest technologies used in various industries, contributing to improved production efficiency, reduced costs, and enhanced security, With the increasing reliance on this technology, the challenge of complex cyberattacks are also on the rise, These attacks are considered one of the major challenges facing smart networks, as attackers can exploit vulnerabilities in systems to access sensitive data or disrupt industrial operations, To counteract these threats, advanced intrusion detection systems should be developed, leveraging artificial intelligence and big data analytics to effectively detect and respond to attacks in real-time. Therefore, it is imperative to strive towards developing advanced and intelligent security systems to combat cyberattacks, ensuring the safety of industrial operations and data protection. This paper provides two IDS based on AI that are developed to negate the raising sophisticated cyberattacks. IN the first technique, Group of ML techniques such as Decision tree, Random Forrest classifiers, support vector classifier, and K_Nearest Neigbor are used with Feature reduction algorithms classifying network traffic subspecies to enhancing the accuracy and efficiency of detection systems. The second proposed technique for specifying the type of intrusion advantage various methodologies, particularly in the context of IoT networks and deep learning, the two algorithms are trained and tested using three well-known datasets to investigate wide domain of cyberattacks targeting the IIoT infrastructure. Results of the simulation show that the algorithm proposed in this work provides high improvement in detection of cyberattacks. The first algorithm achieved an accuracy of 99.9% and a very low false positive rate of just 0.1%. In addition, the second proposed algorithm identifies type of attack with a detection ratio of 99.76%. These results demonstrate how the proposed IDS based on AI algorithms can effectively detect network intrusion, and significantly enhance the security of IIoT system
  • Intrusion Detection in IoT Networks Using Deep Learning: A Comprehensive Approach
    Abdallah S. Elnamaky, Emad Elsamahy, Samir G. Sayed, Ahmed Salem
    2025 15th International Conference on Electrical Engineering Iceeng 2025, 2025
    Due to the wide availability and usage of Internet of Things (IoT) devices in many fields, these devices have inherent vulnerabilities due to their design. Therefore, the probability of cyberattacks which target the IoT networks has significantly increased. To increase the security of IoT networks, this study suggests an Intrusion Detection System (IDS) that makes use of a deep learning technique. Moreover, to effectively categorize network traffic into normal and potentially harmful attacks, our suggested approach utilizes deep learning techniques such as: Long Short-Term Memory (LSTM), Recurrent neural network (RNN), Dense neural network (DNN). Reducing the dimensionality of traffic characteristics using feature selection approaches minimizes the detection delays and computational resources. The system was trained and tested using benchmark BoT-IoT dataset which has a variety of attack types. Our findings demonstrate that the suggested IDS attain high accuracy and F1-score of 96.91% and 95.78% in addition to low false negative and false positive rates 0.1690 and 0.00834 in comparison to their corresponding results in literature.
  • Framework for Intrusion Detection in IoT: Integrating Deep Learning and Time Series Anomaly Detection Techniques
    International Journal of Intelligent Engineering and Systems, 2025
  • Bluffing the Hackers: Automated Decoy Creation and Real-Time Cyber Deception
    Mohamed Farrag, Samir G. Sayed, Marwa Zamzam
    2024 7th International Conference on Signal Processing and Information Security Icspis 2024, 2024
    In the digital age, the proliferation of cyber threats necessitates advanced cybersecurity solutions. This paper introduces “Bluff,” an innovative cybersecurity platform designed to enhance intrusion prevention through automated decoy deployment and real-time threat tracking. “Bluff” integrates both backend and frontend components within a single cross-platform executable, simplifying installation and use. Leveraging deception technology, “Bluff” creates strategic decoys that divert cyber attackers, thereby protecting critical systems and gathering valuable threat intelligence. This comprehensive solution not only fortifies defenses but also manipulates adversaries’ perceptions, enabling early threat detection. Through detailed implementation and performance evaluation, we demonstrate “Bluff’s” efficacy in providing a proactive and sophisticated defense mechanism, contributing significantly to the evolving landscape of cybersecurity.
  • Automating Cyber Defense: Enhancing Threat Intelligence with AI-Driven Annotation
    Youssef Mostafa, Samir G. Sayed, Marwa Zamzam
    2024 7th International Conference on Signal Processing and Information Security Icspis 2024, 2024
    The cybersecurity landscape is increasingly challenged by the growing digital footprint and the sophistication of cyber threats, necessitating experts to stay updated and extract actionable insights from a deluge of Cyber Threat Intelligence data. Given the impracticality of manually processing approximately 60,000 pieces of Cyber Threat Intelligence released monthly, this paper introduces the Cybersecurity Entity Extraction Tool, an efficient technique leveraging pre-trained transformer-based large language models and an artificial neural network to extract named entities from unstructured sources within the cybersecurity domain. This tool not only facilitates the identification and contextual understanding of cyber threats but also achieves a commendable F1-score of 92% across 14 distinct labels, significantly mitigating the limitations of previous methods and enhancing the capability of analysts to process large volumes of Cyber Threat Intelligence data efficiently.
  • Joint trajectory and CoMP clustering optimization in UAV-assisted cellular systems: a coalition formation game approach
    Mostafa M. Abdelhakam, Mahmoud M. Elmesalawy, Ibrahim I. Ibrahim, Samir G. Sayed
    Eurasip Journal on Wireless Communications and Networking, 2023
    In this paper, the flexibility of unmanned aerial vehicles (UAVs), as well as the benefits of coordinated multi-point (CoMP) transmission, are utilized for mitigating the interference in cellular networks. Specifically, the joint problem of CoMP clusters and UAVs’ trajectories is addressed for downlink transmission in a UAV-assisted cellular system. The problem is presented as a non-convex optimization problem that aims to maximize the sum rate of the ground users by taking into account the clustering, UAV mobility and backhaul capacity constraints. Since the formulated problem is known to be NP-hard, we partition it into two sub-problems. Particularly, by using coalitional game theory, the CoMP clusters are obtained with a given UAVs’ trajectories. Then, UAVs’ trajectories are optimized with given CoMP clusters using successive convex approximation technique. Based on the block coordinate descent method, the two sub-problems are solved alternatively until convergence. Numerical results are conducted and demonstrated the effectiveness of the proposed algorithm.
  • Collaborative CoMP and trajectory optimization for energy minimization in multi-UAV-assisted IoT networks with QoS guarantee
    Mostafa M. Abdelhakam, Mahmoud M. Elmesalawy, Ibrahim I. Ibrahim, Samir G. Sayed
    Computer Networks, 2023
  • Detecting Malware Activities With MalpMiner: A Dynamic Analysis Approach
    Mustafa F. Abdelwahed, Mustafa M. Kamal, Samir G. Sayed
    IEEE Access, 2023
    Day by day, malware as a service becomes more popular and easy to acquire, thus allowing anyone to start an attack without any technical background, which in turn introduces challenges for detecting such attacks. One of those challenges is the detection of malware activities early to prevent harm as much as possible. This paper presents a trusted dynamic analysis approach based on Answer Set Programming (ASP), a logic engine inference named Malware-Logic-Miner (MalpMiner). ASP is a nonmonotonic reasoning engine built on an open-world assumption, which allows MalpMiner to adopt commonsense reasoning when capturing malware activities of any given binary. Furthermore, MalpMiner requires no prior training; therefore, it can scale up quickly to include more malware-attack attributes. Moreover, MalpMiner considers the invoked application programming interfaces’ values, resulting in correct malware behaviour modelling. The baseline experiments prove the correctness of MalpMiner related to recognizing malware activities. Moreover, MalpMiner achieved a detection ratio of 99% with a false-positive rate of less than 1% while maintaining low computational costs and explaining the detection decision.
  • Whitelisting Requirements for Effective Cyber Defense Solutions
    May Medhat, Samir G. Sayed, Samia M. Abd-Alhalem, Ali E. Takieldeen
    2023 International Telecommunications Conference Itc Egypt 2023, 2023
  • Two-timescale optimization approach for coordinated multi-point design in unmanned aerial vehicle-assisted cellular networks
    Mostafa M. Abdelhakam, Mahmoud M. Elmesalawy, Ibrahim I. Ibrahim, Samir G. Sayed
    Transactions on Emerging Telecommunications Technologies, 2023
  • Flight Trajectory and CoMP Design for Communication Energy Minimization in UAV-Enabled Cellular Networks
    Mostafa M. Abdelhakam, Mahmoud M. Elmesalawy, Ibrahim I. Ibrahim, Samir G. Sayed
    18th International Computer Engineering Conference Icenco 2022, 2022
  • CtuNet: A Deep Learning-based Framework for Fast CTU Partitioning of H265/HEVC Intra- coding
    Farid Zaki, Amr E. Mohamed, Samir G. Sayed
    Ain Shams Engineering Journal, 2021
  • Energy Aware Mobile Cloud Computing using Femtocells Technology
    Samir G. Sayed, Samar A. Said, Sameh A. Salem
    2021 International Mobile Intelligent and Ubiquitous Computing Conference Miucc 2021, 2021
  • YARAMON: A Memory-based Detection Framework for Ransomware Families
    May Medhat, Menna Essa, Hend Faisal, Samir G. Sayed
    2020 15th International Conference for Internet Technology and Secured Transactions Icitst 2020, 2020
  • Collaborative Framework for Early Detection of RAT-Bots Attacks
    Ahmed A. Awad, Samir G. Sayed, Sameh A. Salem
    IEEE Access, 2019
  • Data Mining Based Strategy for Detecting Malicious PDF Files
    Samir G. Sayed, Mohmed Shawkey
    Proceedings 17th IEEE International Conference on Trust Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering Trustcom Bigdatase 2018, 2018
  • Low complexity intra-prediction algorithm for video coding standards
    Farid Z. Saleh, Samir G. Sayed, Amr E. Mohamed
    Advances in Intelligent Systems and Computing, 2018
  • Energy aware mobile cloud computing algorithm for android smartphones
    Samar A. Said, Sameh A. Salem, Samir G. Sayed
    Advances in Intelligent Systems and Computing, 2018
  • Enhanced approach to detect malicious VB script files based on data mining techniques
    Doaa Wael, Samir G. Sayed, Nashwa AbdelBaki
    Procedia Computer Science, 2018
  • A network-based framework for RAT-bots detection
    Ahmed A. Awad, Samir G. Sayed, Sameh A. Salem
    2017 8th IEEE Annual Information Technology Electronics and Mobile Communication Conference Iemcon 2017, 2017
  • A host-based framework for RAT bots detection
    Ahmed A. Awad, Samir G. Sayed, Sameh A. Salem
    2017 International Conference on Computer and Applications Icca 2017, 2017
  • Decentralized cognitive radio network spectrum access algorithm for throughput improvement
    Ibrahim E. Farrag, Samir G. Sayed
    2nd International Conference on Computer and Communication Systems Icccs 2017, 2017
  • Malicious VBScript detection algorithm based on data-mining techniques
    Doaa Wael, Ahmed Shosha, Samir G. Sayed
    Accs Peit 2017 2017 Intl Conf on Advanced Control Circuits Systems and 2017 Intl Conf on New Paradigms in Electronics and Information Technology, 2017
  • Opportunistic multi-channel MAC protocol for cognitive radio networks
    M. Anany, Samir G. Sayed
    Canadian Conference on Electrical and Computer Engineering, 2016
  • Hand gesture recognition using modified 1$ and background subtraction algorithms
    Hazem Khaled, Samir G. Sayed, El Sayed M. Saad, Hossam Ali
    Mathematical Problems in Engineering, 2015
  • A real-time approach for detecting malicious executables
    Samir Sayed, Rania R. Darwish, Sameh A. Salem
    Advances in Intelligent Systems and Computing, 2014
  • Modified Cooperative Access with Relay's Data (MCARD) based Directional Antenna for multi-rate WLANs
    Ahmed Magdy, S. Sayed, K.R. Mahmoud, I.I. Ibrahim
    Alexandria Engineering Journal, 2014
  • Cooperative communications based on smart antenna systems using PSO algorithm
    Progress in Electromagnetics Research Symposium, 2013
  • BTAC: A busy tone based cooperative MAC protocol for wireless local area networks
    Samir Gaber Sayed, Yang Yang, Jing Xu
    Mobile Networks and Applications, 2011
  • Harnessing battery recovery effect in wireless sensor networks: Experiments and analysis
    Chi-Kin Chau, Fei Qin, Samir Sayed, Muhammad Wahab, Yang Yang
    IEEE Journal on Selected Areas in Communications, 2010
  • Analysis of energy efficiency of a busy tone based cooperative MAC protocol for multi-rate WLANs
    Samir Sayed, Yang Yang, Haiyou Guo, Honglin Hu
    IEEE Wireless Communications and Networking Conference Wcnc, 2010
  • CARD: Cooperative Access with Relay's Data for multi-rate wireless local area networks
    S. Sayed, Y. Yang, H. Hu
    IEEE International Conference on Communications, 2009
  • Throughput analysis of cooperative access with relay's data protocol for unsaturated WLANs
    Samir Sayed, Yang Yang, Honglin Hu
    Proceedings of the 2009 ACM International Wireless Communications and Mobile Computing Connecting the World Wirelessly Iwcmc 2009, 2009
  • Throughput analysis of cooperative access protocol for multi-rate WLANs
    Samir Sayed, Yang Yang, Honglin Hu
    IEEE Wireless Communications and Networking Conference Wcnc, 2009
  • Energy efficiency analysis of cooperative access with relay's data algorithm for multi-rate WLANs
    Samir Sayed, Yang Yang, Haiyou Guo, Honglin Hu
    IEEE International Symposium on Personal Indoor and Mobile Radio Communications PIMRC, 2009
  • RID: relay with integrated data for multi-rate wireless cooperative networks
    Samir Sayed, Yang Yang
    5th International Conference on Broadband Communications Networks and Systems Broadnets 2008, 2008
  • BTAC: A busy tone based cooperative mac protocol for wireless local area networks
    Samir Sayed, Yang Yang
    3rd International Conference on Communications and Networking in China Chinacom 2008, 2008
  • A new wavelet-based image coding using embedded vector quantizer
    Canadian Conference on Electrical and Computer Engineering, 2002