Article
Computer Science, Information Systems
Muhammad Mudassar Yamin, Mohib Ullah, Habib Ullah, Basel Katt
Summary: Artificial intelligence technologies are actively being used for both cyber defense and offensive purposes, with attacks ranging from tampering with medical images to influencing the safety of autonomous vehicles. This research investigates recent cyberattacks utilizing AI techniques, identifies mitigation strategies, and explores future scenarios for controlling such attacks by analyzing existing trends in AI-based cyberattacks.
JOURNAL OF INFORMATION SECURITY AND APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Ignacio Fernandez De Arroyabe, Carlos F. A. Arranz, Marta F. Arroyabe, Juan Carlos Fernandez de Arroyabe
Summary: Our study examines the impact of cyber-capabilities and cyber-attacks on investment in cybersecurity systems in organizations. The findings show that organizations invest in cybersecurity systems based on their cybersecurity capabilities and experience of cyber-attacks. This study contributes to our understanding of cybersecurity and provides valuable insights for management.
COMPUTERS & SECURITY
(2023)
Article
Computer Science, Information Systems
Abubakar Sadiq Mohammed, Eirini Anthi, Omer Rana, Neetesh Saxena, Pete Burnap
Summary: Industrial Cyber-Physical Systems (ICPS) rely on Supervisory Control and Data Acquisition (SCADA) for process monitoring and control. However, communication through insecure protocols such as Modbus, DNP3, and OPC Data Access makes these SCADA systems vulnerable to various attacks, including denial of service (DoS) attacks. This paper introduces a novel Field Flooding attack that exploits the packet memory structure of the Modbus protocol to perform a DoS attack on Programmable Logic Controllers (PLCs). The proposed mechanism, utilizing supervised machine learning with the XGBoost algorithm, achieves 99% accuracy in detecting this attack.
COMPUTERS & SECURITY
(2023)
Article
Computer Science, Information Systems
Iqbal H. Sarker
Summary: This paper introduces CyberLearning, a machine learning-based cybersecurity modeling approach, and evaluates the effectiveness of various machine learning-based security models through empirical analysis.
INTERNET OF THINGS
(2021)
Article
Computer Science, Information Systems
Fernando J. Rendon-Segador, Juan A. Alvarez-Garcia, Angel Jesus Varela-Vaca
Summary: Cyber-attacks cause significant financial losses, and they are becoming increasingly sophisticated. As a result, there is a high demand for cybersecurity systems to protect both public and private institutions. This study focuses on developing a deep learning model for detecting different cyber-attacks, exploring the relevance of feature selection, and analyzing the importance of attention mechanisms in improving feature assessment. Comparative experiments were conducted using benchmark datasets in the field of cybersecurity.
COMPUTERS & SECURITY
(2023)
Review
Chemistry, Analytical
Bandar Alotaibi
Summary: The Industrial Internet of Things (IIoT) is a research area derived from the Internet of Things (IoT) that has revolutionized manufacturing and production. However, the inter-connectivity provided by IIoT also opens the door for cyber-attacks. IoT security is a major challenge hindering the adoption of IIoT, leading to increased research proposals in the past decade. This paper provides a literature survey of IIoT security, identifies threats, classifies them based on the exploited IIoT layer, and highlights the use of emerging technologies to enhance IIoT security.
Review
Chemistry, Multidisciplinary
Ziyad R. Alashhab, Mohammed Anbar, Manmeet Mahinderjit Singh, Iznan H. Hasbullah, Prateek Jain, Taief Alaa Al-Amiedy
Summary: This survey discusses the security issues and challenges in cloud computing, proposes a new taxonomy for classifying cloud computing attacks and DDoS attacks, and compares it with existing surveys. The survey aims to serve as a guide and reference for researchers working on new DDoS attack detection approaches within the cloud computing environment.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Civil
Mhd Ali Alomrani, Mosaddek Hossain Kamal Tushar, Deepa Kundur
Summary: The push for green transportation has increased to combat the alarming rise in atmospheric CO2 levels, leading carmakers to develop clean cars and countries to set aggressive EV adoption targets. However, electric vehicles are vulnerable to cyberattacks due to their sensors, communication channels, and decision-making components. This paper proposes a learning-based detection model that can identify deceptive electric vehicles, trained on real driving traces and malicious datasets generated by a reinforcement learning agent. The model shows greater robustness to intelligent and stealthy attacks compared to handcrafted attacks.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Software Engineering
Emma Lejeune
Summary: Recent advances in machine learning have been driven by classification problems and use of benchmark datasets. Metamodels in mechanics are of interest due to their efficiency in computation time. However, understanding which machine learning methods and model architectures perform best on mechanical data remains limited.
COMPUTER-AIDED DESIGN
(2021)
Article
Computer Science, Information Systems
Ethan Brewer, Jason Lin, Dan Runfola
Summary: With increasing opportunities to disrupt critical space infrastructure systems, ensuring the cybersecurity of data pipelines associated with satellite images has become crucial. This study evaluates the vulnerability of convolutional networks trained on satellite images to trigger-based backdoor attacks and explores countermeasure techniques to detect and repair the damage caused by such attacks.
INFORMATION SCIENCES
(2022)
Review
Radiology, Nuclear Medicine & Medical Imaging
Vera Sorin, Shelly Soffer, Benjamin S. Glicksberg, Yiftach Barash, Eli Konen, Eyal Klang
Summary: This study systematically reviews the literature on adversarial attacks in radiology. A total of 22 studies were included, primarily focused on image classification algorithms. Some attacks proved highly effective, reducing the AUC of algorithm performance to 0 and achieving success rates up to 100%.
EUROPEAN JOURNAL OF RADIOLOGY
(2023)
Article
Computer Science, Hardware & Architecture
Norberto Garcia, Tomas Alcaniz, Aurora Gonzalez-Vidal, Jorge Bernal Bernabe, Diego Rivera, Antonio Skarmeta
Summary: This paper presents an AI-based anomaly detection system for real-time detection of SlowDoS attacks over application-level encrypted traffic. The system combines clustering analysis and deep learning techniques in a distributed AI model to achieve a success rate of 98% and a false negative rate below 0.5% in detecting different types of SlowDoS attacks in a real testbed.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Athanasios Dimitriadis, Efstratios Lontzetidis, Boonserm Kulvatunyou, Nenad Ivezic, Dimitris Gritzalis, Ioannis Mavridis
Summary: Traditional attack detection approaches are insufficient due to the increasing number of successful and sophisticated attacks. This paper introduces an approach called Fronesis, which combines ontological reasoning and various frameworks to detect ongoing cyber-attacks. The proposed approach examines digital artifacts and applies rule-based reasoning to identify adversarial techniques, correlating them to tactics and mapping them to phases of the Cyber Kill Chain model for effective attack detection. The approach is demonstrated through an email phishing attack scenario.
Article
Computer Science, Theory & Methods
Yuantian Miao, Chao Chen, Lei Pan, Qing-Long Han, Jun Zhang, Yang Xiang
Summary: In recent years, stealing attacks against controlled information using machine learning algorithms have emerged as a significant cyber security threat, making detection and defense challenging and urgent. This survey reviews the recent advances in this type of attack, categorizes them into three types based on the targeted controlled information, and proposes countermeasures focusing on detection, disruption, and isolation for effective protection.
ACM COMPUTING SURVEYS
(2021)
Article
Computer Science, Information Systems
Zheyu Song, Yonghong Tian, Junjin Zhang
Summary: This article proposes a ransomware attack similarity analysis method based on the ATT&CK matrix, which reveals the behavioral patterns of attackers by analyzing the similarity between attack events, and proposes corresponding countermeasures to enhance network security defenses.
Article
Engineering, Civil
Lei Liu, Ming Zhao, Miao Yu, Mian Ahmad Jan, Dapeng Lan, Amirhosein Taherkordi
Summary: This paper proposes a task offloading scheme in Vehicular Edge Computing (VEC) that utilizes multi-hop vehicle computation resources to improve response delay and enhance user experience.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Kai Zhang, Jiao Tian, Hongwang Xiao, Ying Zhao, Wenyu Zhao, Jinjun Chen
Summary: Blockchain has attracted attention from the IoT research community due to its decentralization and consistency. However, the accessibility of all nodes to the chain data raises privacy concerns. To address this issue, we propose a novel LDP mechanism that splits and perturbs input numerical data using digital bits, without requiring a fixed input range and large data volume. Our adaptive privacy budget allocation model significantly reduces the deviation of the perturbation function and provides high data utility while maintaining privacy.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Automation & Control Systems
Rahim Khan, Jason Teo, Mian Ahmad Jan, Sahil Verma, Ryan Alturki, Abdullah Ghani
Summary: This article proposes a lightweight and trustworthy device-to-server mutual authentication scheme for edge-enabled IoT networks. Simulation results validate its exceptional performance compared to field proven approaches.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Automation & Control Systems
Fazlullah Khan, Mian Ahmad Jan, Ryan Alturki, Mohammad Dahman Alshehri, Syed Tauhidullah Shah, Ateeq Ur Rehman
Summary: The Internet of Medical Things (IoMT) effectively addresses various issues in conventional healthcare systems such as personnel shortages, care quality, insufficient supplies, and expenses. IoMT technology offers advantages in treatment efficiency and quality, but faces increasing cyberattacks. This article proposes a cyberattack detection method using ensemble learning and fog-cloud architecture to ensure security. The method outperforms baseline approaches in terms of precision by 4% according to evaluation on the ToN-IoT dataset.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Civil
Aamir Akbar, Muhammad Ibrar, Mian Ahmad Jan, Lei Wang, Nadir Shah, Houbing Herbert Song
Summary: Since millions of smart vehicles in Internet-of-Vehicles (IoV) produce and relay data, creating social networks of vehicles in IoV is crucial for the future Intelligent Transportation System (ITS). However, the IoV architecture has been fragmented to meet the needs of different work domains. To address these problems, the concept of Social IoV (SIoV) was introduced. One of the challenges in SIoV is the rapid growth and depletion of social relations between vehicles due to the dynamic and unstable nature of IoV. Therefore, we propose an adaptive clustering technique called SeAC to improve the stability and efficiency of SIoV.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Muhammad Babar, Mian Ahmad Jan, Xiangjian He, Muhammad Usman Tariq, Spyridon Mastorakis, Ryan Alturki
Summary: With the rise of IoT, the awareness of edge computing is gaining importance. However, edge computing faces challenges in tackling the diverse applications of IoT due to the massive heterogeneous data they produce. To address these challenges, we propose an optimized IoT-enabled big data analytics architecture for edge-cloud computing using machine learning.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Engineering, Multidisciplinary
Jiaming Pei, Zhi Yu, Jinhai Li, Mian Ahmad Jan, Kuruva Lakshmanna
Summary: This research focuses on addressing the communication efficiency problem in federated learning and proposes a new framework called TKAGFL. The TKAGFL framework improves communication efficiency through the use of federated generative adversarial networks, improved homomorphic encryption methods, and optimized communication parameter compression algorithms. Experimental results demonstrate that the TKAGFL framework achieves higher accuracy and faster convergence compared to other algorithms or frameworks.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2023)
Article
Engineering, Multidisciplinary
Muhammad Ibrar, Lei Wang, Aamir Akbar, Mian Ahmad Jan, Venki Balasubramanian, Gabriel-Miro Muntean, Nadir Shah
Summary: This paper proposes an adaptive capacity task offloading solution for social industrial IoT based on device-to-device communications and social relationships. Experimental results show that the proposed approach outperforms existing methods.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Information Systems
Muneeb Ul Hassan, Mubashir Husain Rehmani, Jinjun Chen
Summary: Over the past decade, blockchain technology has gained significant attention due to its integration with various everyday applications of modern information and communication technologies (ICT). The peer-to-peer (P2P) architecture of blockchain enhances these applications by providing strong security and trust-oriented guarantees. However, recent research has shown that blockchain networks may still face security, privacy, and reliability issues. In this article, we provide a comprehensive survey on the integration of anomaly detection models in blockchain technology. We discuss the role of anomaly detection in ensuring security, present evaluation metrics and requirements, survey various models, and highlight future research directions.
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS
(2023)
Article
Computer Science, Artificial Intelligence
Ying Zhao, Dong Yuan, Jia Tina Du, Jinjun Chen
Summary: Directional distribution analysis is essential for abstracting dispersion and orientation of spatial datasets, but it must be used cautiously to protect individuals' privacy. There is a tension between accurate directional distribution results and location privacy. In this paper, we propose a geo-ellipse-indistinguishability privacy notion to protect individual location data and present elliptical privacy mechanisms based on gamma distribution and multivariate normal distribution. The empirical evaluation shows that our proposed elliptical approach achieves significantly higher directional distribution utility compared to circular noise function based methods.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Cybernetics
Xingjuan Cai, Wanwan Guo, Mengkai Zhao, Zhihua Cui, Jinjun Chen
Summary: This article proposes a knowledge graph-based many-objective model for explainable social recommendation (KGMESR), which considers the explainability, accuracy, novelty, and content quality of social recommendation results. The model utilizes social behavior information to calculate user similarity and quantifies the explainability of results using entity vectors and embedding vectors. A many-objective recommendation algorithm based on the partition deletion strategy is designed for efficiency. Experimental results demonstrate preferable recommendation results and two case studies affirm the explainability of the proposed model.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Engineering, Civil
Xiong Li, Shuai Shang, Shanpeng Liu, Ke Gu, Mian Ahmad Jan, Xiaosong Zhang, Fazlullah Khan
Summary: With the development of IoT-enabled MTS, there is a need for reliable and timely analysis of the massive data generated. Cloud-based MTS allows users to upload data without worrying about price, capacity, or location, but also introduces security issues, especially concerning the integrity protection of outsourced data. To address this problem, an identity-based dynamic data integrity auditing scheme is proposed, which reduces the burden of key management, improves auditing efficiency through batch auditing, and supports dynamic operations on the outsourced data. Security analysis shows that the scheme ensures storage correctness and resists common attacks, while performance comparison results demonstrate its low computational cost and reduced communication overhead in the auditing phase.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Chengpei Xu, Wenjing Jia, Tingcheng Cui, Ruomei Wang, Yuan-fang Zhang, Xiangjian He
Summary: This paper improves classic bottom-up text detection frameworks by fusing visual-relational features, developing effective false positive/negative suppression mechanisms, and introducing a new shape-approximation strategy, resulting in enhanced performance compared to state-of-the-art methods.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Article
Computer Science, Information Systems
Chengpei Xu, Wenjing Jia, Ruomei Wang, Xiaonan Luo, Xiangjian He
Summary: Bottom-up text detection methods play an important role in arbitrary-shape scene text detection. However, this paper proposes a novel approach named MorphText to capture the regularity of texts using deep morphology. By designing two deep morphological modules, text segments can be regularized and reliable connections can be determined. Experimental results show that MorphText outperforms existing methods on multiple benchmark datasets.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Article
Engineering, Civil
Muhammad Adil, Mian Ahmad Jan, Yongxin Liu, Hussein Abulkasim, Ahmed Farouk, Houbing Song
Summary: This paper provides a comprehensive survey of security concerns in UAV-aided IoT applications. It outlines various security threats and countermeasure techniques, emphasizes the current challenges and requirements, and highlights open security challenges for future research.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
