Article
Engineering, Multidisciplinary
Maha M. Althobaiti, K. Pradeep Mohan Kumar, Deepak Gupta, Sachin Kumar, Romany F. Mansour
Summary: Advanced developments in Industrial Cyber-Physical Systems (CPSs), including Internet of Things (IoT), provide practical use in various application areas but also pose threats to user security. Recently, cognitive computing and artificial intelligence techniques have opened new opportunities for the revolution of industrial CPSs. AI based intrusion detection systems are crucial for achieving security in industrial CPS environments.
Article
Computer Science, Information Systems
T. Thilagam, R. Aruna
Summary: This study proposes an IDS based on a customized RC-NN combined with the Ant Lion optimization algorithm, which efficiently detects various attacks in cloud network environments. By blending CNN with LSTM, high accuracy attack classification is achieved, resulting in superior classification accuracy and reduced error rates.
Article
Engineering, Civil
Mohamed Abdel-Basset, Nour Moustafa, Hossam Hawash, Imran Razzak, Karam M. Sallam, Osama M. Elkomy
Summary: With the integration of IoT in transportation, Internet of Vehicles has become crucial for designing Smart Transportation Systems. This study introduces a federated deep learning-based intrusion detection framework to efficiently detect attacks. The framework utilizes context-aware transformer network and blockchain-managed federated training for secure and reliable training without centralized authority, demonstrating its effectiveness against state-of-the-art approaches in securing intelligent transportation systems against cyber-attacks.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Review
Computer Science, Information Systems
Mohamad Mulham Belal, Divya Meena Sundaram
Summary: This paper presents a literature review on the use of machine learning and deep learning algorithms as security techniques in cloud computing. It discusses different types of attacks and threats, the limitations of traditional security techniques, and the development of defenses based on machine learning and deep learning. The analysis of case studies provides insights into common security issues and countermeasures in cloud environments, and the paper also discusses future research directions and challenges in cloud computing security.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Dilli Babu Salvakkam, Vijayalakshmi Saravanan, Praphula Kumar Jain, Rajendra Pamula
Summary: The increasing popularity of cloud computing systems has raised concerns about privacy, confidentiality, and availability. Intrusion detection has become crucial, especially in detecting new types of intrusions that can compromise cloud security. This research proposes a unique method using deep learning to detect cloud computing intrusions and presents an accuracy enhancement model for intrusion detection.
COGNITIVE COMPUTATION
(2023)
Article
Chemistry, Multidisciplinary
Mesfer Al Duhayyim, Khalid A. Alissa, Fatma S. Alrayes, Saud S. Alotaibi, ElSayed M. Tag El Din, Amgad Atta Abdelmageed, Ishfaq Yaseen, Abdelwahed Motwakel
Summary: This study introduces a new Stochastic Fractal Search Algorithm with Deep Learning Driven Intrusion Detection System (SFSA-DLIDS) for a cloud-based CPS environment, with a focus on intrusion recognition and classification to enhance security.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Hardware & Architecture
Omar Elnakib, Eman Shaaban, Mohamed Mahmoud, Karim Emara
Summary: Internet of Things (IoT) is a disruptive technology with significant importance for Intrusion Detection Systems (IDSs). This paper proposes an enhanced anomaly-based Intrusion Detection Deep learning Multi-class classification model (EIDM) that can accurately classify 15 traffic behaviors including 14 attack types with 95% accuracy. Comparative study shows EIDM outperforms other state-of-the-art deep learning-based IDSs in terms of accuracy and efficiency.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Automation & Control Systems
Ly Vu, Quang Uy Nguyen, N. Diep Nguyen, Dinh Thai Hoang, Eryk Dutkiewicz
Summary: This article proposes a novel solution to enable robust cloud IDSs using deep neural networks. By developing two deep generative models to synthesize malicious samples on the cloud systems, the accuracy of cloud IDSs is significantly improved. The experiments also show that this method enhances the accuracy of detecting DDoS attacks.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Chemistry, Analytical
Theyazn H. H. Aldhyani, Hasan Alkahtani
Summary: Cloud computing is the most cost-effective means of providing online IT services, but it is also prone to new flaws such as economic denial of sustainability attacks (EDoS), which exploit the pay-per-use paradigm to generate unanticipated usage charges. To mitigate EDoS attacks, this research presents an effective approach using machine and deep learning algorithms. The proposed algorithms outperformed existing systems in terms of accuracy and are essential for improving cloud computing service provider security.
Article
Computer Science, Artificial Intelligence
Deepak Kumar Jain, Weiping Ding, Ketan Kotecha
Summary: This research develops a new fuzzy deep neural network (FDNN) with Honey Bader Algorithm (HBA) for privacy-preserving intrusion detection technique, named FDNN-HBAID for cloud environment. The presented FDNN-HBAID system is based on the design of an intrusion detection approach with a blockchain-enabled privacy-preserving scheme. The experimental validation on benchmark datasets revealed that the FDNN-HBAID approach had shown the potential to achieve security and privacy in the cloud infrastructure.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Information Systems
Devrim Akgun, Selman Hizal, Unal Cavusoglu
Summary: In this study, an intrusion detection system using preprocessing procedures and a deep learning model for detecting DDoS attacks was proposed. Various models based on deep neural networks (DNN), convolutional neural networks (CNN), and long short term memory (LSTM) were evaluated in terms of detection performance and real-time performance. The suggested CNN-based inception-like model achieved the best results in binary and multiclass accuracy. The proposed IDS system with preprocessing methods outperformed state-of-the-art studies.
COMPUTERS & SECURITY
(2022)
Review
Chemistry, Multidisciplinary
Patrick Vanin, Thomas Newe, Lubna Luxmi Dhirani, Eoin O'Connell, Donna O'Shea, Brian Lee, Muzaffar Rao
Summary: The rapid growth of the Internet and communications has led to a significant increase in transmitted data. Attackers continuously create new methods to steal or corrupt this data, presenting a major challenge for intrusion detection. Machine learning algorithms have gained popularity in efficiently and accurately detecting network intrusion. This paper presents the concept of intrusion detection systems (IDS) and provides a taxonomy of machine learning methods. It also discusses the main metrics used to assess IDS and reviews recent IDS solutions using machine learning, highlighting their strengths and weaknesses.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Electrical & Electronic
Hafsa Benaddi, Khalil Ibrahimi, Abderrahim Benslimane, Mohammed Jouhari, Junaid Qadir
Summary: This paper proposes a DRL-based IDS for network traffics using MDP and analyzes the IDS behavior through modeling the interaction between the IDS and attacker players. The proposed DRL-IDS outperforms existing models in terms of detection rate, accuracy, and false alarms reduction.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Engineering, Civil
Marwa A. Elsayed, Michael Wrana, Ziad Mansour, Karim Lounis, Steven H. H. Ding, Mohammad Zulkernine
Summary: The aerospace and defense industries are highly susceptible to cyber threats due to their sensitive nature, and a security breach can have far-reaching consequences at the national level. This paper introduces a novel adaptive intrusion detection system for the MIL-STD-1553 communication bus in aerospace vehicles, utilizing advanced deep learning techniques to enhance its ability to detect unknown attack patterns.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Hardware & Architecture
Sheng-lin Yin, Xing-lan Zhang, Shuo Liu
Summary: This study proposes an intrusion detection model based on deep capsule network and attention mechanism, which can effectively extract key features and accurately detect anomalous data, yielding good experimental results.
Article
Computer Science, Information Systems
Talal Halabi, Martine Bellaiche
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2020)
Article
Computer Science, Hardware & Architecture
Talal Halabi, Omar Abdel Wahab, Ranwa Al Mallah, Mohammad Zulkernine
Summary: This article explores novel ways to launch intelligent attacks through connected vehicles, aiming to minimize the impact on road traffic services with optimal strategies. By modeling the processes of attack and defense, the article proposes solutions to address data corruption attacks and enhance the security of IoV.
IEEE TRANSACTIONS ON RELIABILITY
(2021)
Article
Telecommunications
Anika Anwar, Talal Halabi, Mohammad Zulkernine
Summary: This paper proposes a trustworthy communication-driven vehicular collaboration framework to prevent data integrity attacks. The framework incorporates a distributed coalition formation game and a trust-based data aggregation procedure to enable dynamic joining and leaving of collaborative communities and protect against false events.
VEHICULAR COMMUNICATIONS
(2022)
Proceedings Paper
Computer Science, Theory & Methods
Cameron Lischke, Tongtong Liu, Joe McCalmon, Md Asifur Rahman, Talal Halabi, Sarra Alqahtani
Summary: This paper investigates the security vulnerability in Multi-Agent Reinforcement Learning (MARL) algorithms, focusing on compromised agent attacks, and proposes a novel detection method that outperforms existing baseline models.
2022 IEEE INTERNATIONAL CONFERENCE ON CYBER SECURITY AND RESILIENCE (IEEE CSR)
(2022)
Review
Mathematics, Interdisciplinary Applications
Yaser Al Mtawa, Anwar Haque, Talal Halabi
Summary: This study aims to provide a compact yet comprehensive review of the state-of-the-art solutions to fault analysis in transmission power systems, to help the research community in choosing suitable techniques for fault analysis.
Proceedings Paper
Computer Science, Hardware & Architecture
Aawista Chaudhry, Talal Halabi, Mohammad Zulkernine
Summary: This paper presents a new approach to specifically designing stealthy data corruption attacks within Intelligent Transportation Systems (ITS) and other data-reliant Cyber-Physical Systems. A Stackelberg security game is used to generate optimal attack and defense strategies. This research direction will advance the design of robust misbehavior detection systems.
52ND ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS WORKSHOP VOLUME (DSN-W 2022)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Jatin Patel, Talal Halabi
Summary: Cloud computing adoption is increasing, with a focus on improving performance and service quality. This paper investigates caching as a method to enhance performance in Web applications in Mobile Cloud Computing, showing that caching can speed up data retrieval by up to four times.
2021 IEEE 6TH INTERNATIONAL CONFERENCE ON SMART CLOUD (SMARTCLOUD 2021)
(2021)
Proceedings Paper
Computer Science, Theory & Methods
Talal Halabi
Summary: This paper addresses the challenge of ensuring the security and privacy of computations in edge computing through an adaptive approach of defense randomization. The results demonstrate that a non-deterministic defense policy yields better security compared to a static defense strategy.
2021 ACM/IEEE 6TH SYMPOSIUM ON EDGE COMPUTING (SEC 2021)
(2021)
Proceedings Paper
Computer Science, Information Systems
Najmeh Seifollahpour Arabi, Talal Halabi, Mohammad Zulkernine
Summary: Intelligent Transportation Systems utilize new technologies to provide intelligent road services and optimize decision-making, but they are vulnerable to cyber attacks, especially in dynamic TSC systems. This paper highlights the threat of intelligent attacks using DRL on TSC systems, which can lead to traffic congestion and other serious issues.
PROCEEDINGS OF THE 2021 IEEE INTERNATIONAL CONFERENCE ON CYBER SECURITY AND RESILIENCE (IEEE CSR)
(2021)
Proceedings Paper
Computer Science, Hardware & Architecture
Habib Ben Abdallah, Afeez Adewale Sanni, Krunal Thummar, Talal Halabi
Summary: The paper proposes an efficient algorithm for energy-efficient user allocation in data centers, comparing its performance with traditional approaches. The results show that the algorithm is significantly effective in real-time environments.
2021 24TH CONFERENCE ON INNOVATION IN CLOUDS, INTERNET AND NETWORKS AND WORKSHOPS (ICIN)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
A. B. M. Bodrul Alam, Talal Halabi, Anwar Haque, Mohammad Zulkernine
2020 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Dharitri Tripathy, Rudrarajsinh Gohil, Talal Halabi
2020 IEEE 6TH INT CONFERENCE ON BIG DATA SECURITY ON CLOUD (BIGDATASECURITY) / 6TH IEEE INT CONFERENCE ON HIGH PERFORMANCE AND SMART COMPUTING, (HPSC) / 5TH IEEE INT CONFERENCE ON INTELLIGENT DATA AND SECURITY (IDS)
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Vrushang Patel, Seungho Choe, Talal Halabi
2020 IEEE 6TH INT CONFERENCE ON BIG DATA SECURITY ON CLOUD (BIGDATASECURITY) / 6TH IEEE INT CONFERENCE ON HIGH PERFORMANCE AND SMART COMPUTING, (HPSC) / 5TH IEEE INT CONFERENCE ON INTELLIGENT DATA AND SECURITY (IDS)
(2020)
Proceedings Paper
Computer Science, Theory & Methods
Talal Halabi, Mohammad Zulkernine
2019 49TH ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS WORKSHOPS (DSN-W)
(2019)
Proceedings Paper
Computer Science, Information Systems
Talal Halabi, Martine Bellaiche
2019 IEEE CONFERENCE ON COMMUNICATIONS AND NETWORK SECURITY (CNS)
(2019)
Editorial Material
Computer Science, Theory & Methods
Kiho Lim, Christian Esposito, Tian Wang, Chang Choi
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Editorial Material
Computer Science, Theory & Methods
Jesus Carretero, Dagmar Krefting
Summary: Computational methods play a crucial role in bioinformatics and biomedicine, especially in managing large-scale data and simulating complex models. This special issue focuses on security and performance aspects in infrastructure, optimization for popular applications, and the integration of machine learning and data processing platforms to improve the efficiency and accuracy of bioinformatics.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Renhao Lu, Weizhe Zhang, Qiong Li, Hui He, Xiaoxiong Zhong, Hongwei Yang, Desheng Wang, Zenglin Xu, Mamoun Alazab
Summary: Federated Learning allows collaborative training of AI models with local data, and our proposed FedAAM scheme improves convergence speed and training efficiency through an adaptive weight allocation strategy and asynchronous global update rules.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Qiangqiang Jiang, Xu Xin, Libo Yao, Bo Chen
Summary: This paper proposes a multi-objective energy-efficient task scheduling technique (METSM) for edge heterogeneous multiprocessor systems. A mathematical model is established for the task scheduling problem, and a problem-specific algorithm (IMO) is designed for optimizing task scheduling and resource allocation. Experimental results show that the proposed algorithm can achieve optimal Pareto fronts and significantly save time and power consumption.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Editorial Material
Computer Science, Theory & Methods
Weimin Li, Lu Liu, Kevin I. K. Wang, Qun Jin
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Mohammed Riyadh Abdmeziem, Amina Ahmed Nacer, Nawfel Moundji Deroues
Summary: Internet of Things (IoT) devices have become ubiquitous and brought the need for group communications. However, security in group communications is challenging due to the asynchronous nature of IoT devices. This paper introduces an innovative approach using blockchain technology and smart contracts to ensure secure and scalable group communications.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Robert Sajina, Nikola Tankovic, Ivo Ipsic
Summary: This paper presents and evaluates a novel approach that utilizes an encoder-only transformer model to enable collaboration between agents learning two distinct NLP tasks. The evaluation results demonstrate that collaboration among agents, even when working towards separate objectives, can result in mutual benefits.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Hebert Cabane, Kleinner Farias
Summary: Event-driven architecture has been widely adopted in the software industry for its benefits in software modularity and performance. However, there is a lack of empirical evidence to support its impact on performance. This study compares the performance of an event-driven application with a monolithic application and finds that the monolithic architecture consumes fewer computational resources and has better response times.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Haroon Wahab, Irfan Mehmood, Hassan Ugail, Javier Del Ser, Khan Muhammad
Summary: Wireless capsule endoscopy (WCE) is a revolutionary diagnostic method for small bowel pathology. However, the manual analysis of WCE videos is cumbersome and the privacy concerns of WCE data hinder the adoption of AI-based diagnoses. This study proposes a federated learning framework for collaborative learning from multiple data centers, demonstrating improved anomaly classification performance while preserving data privacy.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Maruf Monem, Md Tamjid Hossain, Md. Golam Rabiul Alam, Md. Shirajum Munir, Md. Mahbubur Rahman, Salman A. AlQahtani, Samah Almutlaq, Mohammad Mehedi Hassan
Summary: Bitcoin, the largest cryptocurrency, faces challenges in broader adaption due to long verification times and high transaction fees. To tackle these issues, researchers propose a learning framework that uses machine learning to predict the ideal block size in each block generation cycle. This model significantly improves the block size, transaction fees, and transaction approval rate of Bitcoin, addressing the long wait time and broader adaption problem.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Rafael Duque, Crescencio Bravo, Santos Bringas, Daniel Postigo
Summary: This paper introduces the importance of user interfaces for digital twins and presents a technique called ADD for modeling requirements of Human-DT interaction. A study is conducted to assess the feasibility and utility of ADD in designing user interfaces, using the virtualization of a natural space as a case study.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Xiulin Li, Li Pan, Wei Song, Shijun Liu, Xiangxu Meng
Summary: This article proposes a novel multiclass multi-pool analytical model for optimizing the quality of composite service applications deployed in the cloud. By considering embarrassingly parallel services and using differentiated parallel processing mechanisms, the model provides accurate prediction results and significantly reduces job response time.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Seongwan Park, Woojin Jeong, Yunyoung Lee, Bumho Son, Huisu Jang, Jaewook Lee
Summary: In this paper, a novel MEV detection model called ArbiNet is proposed, which offers a low-cost and accurate solution for MEV detection without requiring knowledge of smart contract code or ABIs.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Sacheendra Talluri, Nikolas Herbst, Cristina Abad, Tiziano De Matteis, Alexandru Iosup
Summary: Serverless computing is increasingly used in data-processing applications. This paper presents ExDe, a framework for systematically exploring the design space of scheduling architectures and mechanisms, to help system designers tackle complexity.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Chao Wang, Hui Xia, Shuo Xu, Hao Chi, Rui Zhang, Chunqiang Hu
Summary: This paper introduces a Federated Learning framework called FedBnR to address the issue of potential data heterogeneity in distributed entities. By breaking up the original task into multiple subtasks and reconstructing the representation using feature extractors, the framework improves the learning performance on heterogeneous datasets.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)