Article
Chemistry, Multidisciplinary
Shailendra Mishra
Summary: Cyber threats encompass unauthorized access, alteration, or removal of private information, ransom demands, and business disruption. Cybercrime includes identity theft, malware threats, email and online fraud, and bank fraud. To protect data centers and digital systems, businesses and individuals employ security measures and call for more efficient approaches to tackle scalability issues and advanced threats. Cybercriminals employ AI and data poisoning, leveraging model theft strategies for automated attacks.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Alexandros Zacharis, Constantinos Patsakis
Summary: Content generation using machine learning techniques and a novel ontology called CESO has been explored for cyber security exercises. Unstructured information sources were processed to generate structured content, which was then classified and matched with threat actors' tactics using graph comparison methodologies. The methodology was assessed by experts in a real-world awareness tabletop exercise.
INTERNATIONAL JOURNAL OF INFORMATION SECURITY
(2023)
Review
Computer Science, Artificial Intelligence
Ashok Yadav, Atul Kumar, Vrijendra Singh
Summary: The amount of publicly available data generated by the digitally connected world today is enormous. Open-source intelligence (OSINT) is a method of extracting and gathering intelligence from various publicly available data sources, including web archives, public databases, and social networks such as Facebook, Twitter, LinkedIn, Emails, and Telegrams. OSINT is expanding rapidly and brings new artificial intelligence-based approaches to address issues in national security, political campaigns, the cyber industry, criminal profiling, society, and cyber threats and crimes. This paper provides an overview of the current state of OSINT tools/techniques and discusses its various applications in cybersecurity, as well as the challenges and future directions for developing autonomous models using machine learning, deep learning, and artificial intelligence with OSINT.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Green & Sustainable Science & Technology
Shailendra Pratap Singh, Youseef Alotaibi, Gyanendra Kumar, Sur Singh Rawat
Summary: This research proposes an extended form of differential evolution (DE) with an intelligent mutation operator to protect the large volume of data produced by internet systems from unauthorized users and devices. Experimental findings show that this method outperforms the recent evolutionary algorithm (EA) in terms of confidentiality, integrity, authentication, and availability.
Review
Chemistry, Analytical
Chaitanya Gupta, Ishita Johri, Kathiravan Srinivasan, Yuh-Chung Hu, Saeed Mian Qaisar, Kuo-Yi Huang
Summary: The advancements in wireless communication technologies have led to a significant increase in data generation. Our information is part of a global network that connects various devices. As electronic devices become more capable, more information is being generated and shared. However, the increasing complexity of mobile network topologies has also resulted in a higher incidence of security breaches, impacting the adoption of smart mobile apps and services. Protecting data and preventing misuse is essential. Research suggests that an artificial intelligence-based security model should ensure the secrecy, integrity, and authenticity of the system, its equipment, and the network protocols. This addresses the challenges faced by mobile networks, such as unauthorized network scanning and fraud links.
Article
Computer Science, Artificial Intelligence
Tianqi Zhou, Jian Shen, Debiao He, Pandi Vijayakumar, Neeraj Kumar
Summary: This paper focuses on designing a human-in-the-loop-aided scheme to preserve privacy in smart healthcare. The proposed scheme utilizes block design technique to obfuscate health indicators and introduces human-in-the-loop access for privacy protection.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2022)
Article
Education & Educational Research
S. Siva Shankar, Bui Thanh Hung, Prasun Chakrabarti, Tulika Chakrabarti, Gayatri Parasa
Summary: The increasing influence of networks on modern life necessitates a focus on cybersecurity. Networks are vulnerable to various cyber-attacks, resulting in data breaches and harm to companies. An optimized Artificial Intelligence approach, known as GEO-SMPIF, effectively identifies intrusions and improves privacy and security in professional networks.
EDUCATION AND INFORMATION TECHNOLOGIES
(2023)
Article
Computer Science, Information Systems
Thierno Gueye, Yanen Wang, Mudassar Rehman, Ray Tahir Mushtaq, Sadaf Zahoor
Summary: This paper proposes a neural network-based intrusion detection model that utilizes an embedding layer to model register values. It effectively distinguishes whether an attack occurred and the type of attack, outperforming state-of-the-art methods. The experiments confirm the superior performance of the proposed architecture.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Chengyong Cui, Guojiang Shen, Yu Wang, Yile Xu, Hao Du, Wenyi Zhang, Xiangjie Kong
Summary: With the increasing complexity of in-vehicle information systems (IVIS), the need for innovative artificial intelligence-based interaction methods to enhance cybersecurity is crucial. In-air gestures offer a promising solution due to their intuitiveness and individual uniqueness. However, the impact of in-air gestures on driver distraction and the scarcity of in-air gesture recognition methods in IVIS remain largely unexplored. To address these challenges, we developed a skeleton-based framework specifically tailored for IVIS that recognizes and classifies in-air gestures. Our findings indicate that in-air gestures provide a more efficient and less distracting interaction solution for IVIS, significantly improving driving performance by 65%.
Article
Computer Science, Information Systems
Preetam Amrit, Amit Kumar Singh
Summary: This article presents a comprehensive study on watermarking using trending technologies such as artificial intelligence, machine learning and deep learning. It discusses the introduction of watermarking, background information, interesting applications, and highlights the major role of trending technologies in watermarking. The contribution of the surveyed scheme is summarized and compared, and recent challenges and directions of potential research are highlighted.
COMPUTER COMMUNICATIONS
(2022)
Article
Chemistry, Analytical
Kyoungmin Kim, Youngsup Shin, Justin Lee, Kyungho Lee
Summary: Mobile attacks have become an important attack vector for APT groups in the past decade, prompting experts to propose automated systems for detection and attribution. By adopting MITRE's ATT & CK framework, the study was able to effectively detect threat actors and malware, reducing false positives through Indicator of Compromise (IoC) comparisons.
Article
Computer Science, Hardware & Architecture
Achref Haddaji, Samiha Ayed, Lamia Chaari Fourati
Summary: This paper presents a comprehensive survey of AI-based techniques for security issues in vehicular networks. It provides background information on vehicular networks and their vulnerabilities, evaluates the impact of AI fundamentals on vehicular security, classifies and compares AI-based solutions related to security in vehicular networks, and analyzes the works included in the survey.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Article
Mathematical & Computational Biology
Yuchun Xiao, Zhuo Bi, Zhibin Chen
Summary: Individual and team performance can be improved through the Internet of Things (IoT) in sports by utilizing smart devices and applications connected through networks. IoT in sports is closely related to security and privacy in sports and has a direct impact on the sports industry and athletes. Establishing a safe and reliable IoT infrastructure is therefore crucial.
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
(2022)
Article
Computer Science, Hardware & Architecture
Chadni Islam, M. Ali Babar, Roland Croft, Helge Janicke
Summary: SmartValidator is an AI-based framework that leverages ML techniques to automate alert validation. It dynamically constructs prediction models based on SOC's requirements and CTI, resulting in efficient validation and response to evolving threats. The framework can be adopted by various industries to accelerate and automate alert validation based on their CTI and SOC's preferences.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2022)
Article
Mathematics
Aparna Kumari, Rushil Kaushikkumar Patel, Urvi Chintukumar Sukharamwala, Sudeep Tanwar, Maria Simona Raboaca, Aldosary Saad, Amr Tolba
Summary: This paper proposes an AI-based attack-detection and prevention mechanism for the smart grid system, which ensures data security and integrity using a cryptography-driven recommender system. The proposed scheme outperforms existing approaches and achieves a relatively high accuracy of 99.12%.
Article
Management
Naga Vemprala, Charles Liu, Kim-Kwang Raymond Choo
Summary: During emergency situations, it is challenging to process and summarize a large volume of social media messages to improve situation awareness. This study presents a streamlined protocol that uses clustering and text summarization algorithms to derive informative summaries from millions of social media messages.
Article
Computer Science, Hardware & Architecture
Xuru Li, Daojing He, Yun Gao, Ximeng Liu, Sammy Chan, Manghan Pan, Kim-Kwang Raymond Choo
Summary: As embedded integrated electronic systems (EIESs) become more prevalent, the need for secure data exchange in such systems becomes crucial. Designing secure authentication protocols for different embedded systems with varying communication modes is a challenge. This paper presents a lightweight authenticated key-exchange (AKE) protocol for EIESs, designed for resource-constrained devices with minimal interactions. The protocol's security is proven, and a strategy for selecting security parameters based on empirical evaluations is provided. Efficiency analysis demonstrates effective deployment of the protocol in the EIESs environment.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2023)
Article
Computer Science, Information Systems
Zhiqiu Zhang, Zhu Tianqing, Wei Ren, Ping Xiong, Kim-Kwang Raymond Choo
Summary: In federated learning, a global learning model is trained by the server using gradient information shared by multiple clients to protect client data privacy. However, it has been shown that training data can be reconstructed from shared gradients, leading to privacy breaches. This paper proposes two pruning-based defense mechanisms to prevent privacy leaks in the image reconstruction process and demonstrates their effectiveness on various model architectures and datasets.
COMPUTERS & SECURITY
(2023)
Article
Computer Science, Hardware & Architecture
Yizhi Liu, Xiaohan Hao, Wei Ren, Ruoting Xiong, Tianqing Zhu, Kim-Kwang Raymond Choo, Geyong Min
Summary: This paper proposes an innovative blockchain-enabled information sharing solution in zero-trust context, which guarantees anonymity, entity authentication, data privacy, trustworthiness, participant stimulation, and fairness. The solution supports filtering of fabricated information through smart contracts, effective voting, and consensus mechanisms, preventing unauthenticated participants from sharing garbage information.
IEEE TRANSACTIONS ON COMPUTERS
(2023)
Article
Computer Science, Hardware & Architecture
Rui Zhang, Lei Zhang, Kim-Kwang Raymond Choo, Tong Chen
Summary: In this article, a one-round dynamic authenticated asymmetric group key agreement with sender non-repudiation and privacy (DAAGKAwSNP) protocol is proposed. The protocol utilizes a batch multi-signature scheme and achieves secrecy under chosen ciphertext attacks. The performance of DAAGKAwSNP is evaluated to demonstrate its utility.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2023)
Article
Computer Science, Information Systems
Renwan Bi, Jinbo Xiong, Youliang Tian, Qi Li, Kim-Kwang Raymond Choo
Summary: This article proposes a privacy-preserving object detection framework for connected autonomous vehicles (CAVs) to protect the privacy of images captured by onboard sensors. By leveraging edge computing, CAVs split and upload images to two noncollusive edge servers, which cooperatively detect objects without exposing sensitive information. The experimental findings demonstrate the effectiveness of the framework in protecting the privacy of image objects for CAVs.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Othmane Friha, Mohamed Amine Ferrag, Mohamed Benbouzid, Tarek Berghout, Burak Kantarci, Kim-Kwang Raymond Choo
Summary: In this paper, a secure, decentralized, and differentially private FL-based IDS (2DF-IDS) is proposed to secure smart industrial facilities. The system utilizes a key exchange protocol, a differentially private gradient exchange scheme, and a decentralized FL approach to achieve high-performance intrusion detection in industrial IoT systems.
COMPUTERS & SECURITY
(2023)
Article
Engineering, Multidisciplinary
Tao Ye, Min Luo, Yi Yang, Kim-Kwang Raymond Choo, Debiao He
Summary: This survey article focuses on designing redactable blockchain-based solutions. While immutability is essential for blockchain, it can also be misused to disseminate illicit content and violate privacy regulations like GDPR. The article surveys the existing literature on redactable blockchain, discusses limitations and challenges, and highlights future research opportunities.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Information Systems
Posie Aagaard, Bijan Dinyarian, Omar Abduljabbar, Kim-Kwang Raymond Choo
Summary: Smartphones and mobile apps are widely used worldwide, including family locator apps. These apps allow users to share their location with others to ensure the safety of their loved ones. This article focuses on Life360 and demonstrates the types of forensic artifacts and sensitive data that can be acquired using commercial and open source tools from the app on iOS and Android devices.
FORENSIC SCIENCE INTERNATIONAL-DIGITAL INVESTIGATION
(2023)
Article
Computer Science, Artificial Intelligence
Henry Chacon, Vishwa Koppisetti, David Hardage, Kim-Kwang Raymond Choo, Paul Rad
Summary: For call center facilities, accurately forecasting the number of call arrivals is crucial for customer satisfaction and budget management. This study compares classical time series methods with machine/deep learning techniques to forecast call arrival, using real-life call logs from a national US insurance company. The results show that deep learning models perform well in short-term periods with enough seasonal data, but boosting approach outperforms all models, including deep learning, in long-term periods. These findings highlight the importance of considering limited seasonality and the use of benchmark approaches for call arrival forecasting.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Theory & Methods
Shaobo Zhang, Tao Guo, Qin Liu, Entao Luo, Kim-Kwang Raymond Choo, Guojun Wang
Summary: This paper proposes an accuracy-aware location privacy service, named ALPS, based on assisted regions to protect user location privacy while ensuring the accuracy of services. Two novel mechanisms (assisted regions mechanism and query obfuscation mechanism) are devised to protect user location privacy and ensure the accuracy of LSSs based on trilateration. Theoretical analysis and experimental evaluation demonstrate that our scheme can protect location privacy without compromising the accuracy of LSSs.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Computer Science, Theory & Methods
Yinbin Miao, Yutao Yang, Xinghua Li, Zhiquan Liu, Hongwei Li, Kim-Kwang Raymond Choo, Robert H. H. Deng
Summary: With the rapid development of Location-Based Services (LBS), the security issues such as location privacy leakage have become a concern. In this study, an efficient Privacy-preserving Spatial Range Query (PSRQ) scheme is proposed by combining Geohash algorithm with Circular Shift and Coalesce Bloom Filter (CSC-BF) framework and Symmetric-key Hidden Vector Encryption (SHVE). Additionally, a Confused Bloom Filter (CBF) is designed to confuse the inclusion relationship in Bloom filter, and a more secure and practical enhanced scheme PSRQ+ is proposed. The experimental results show significant improvement in query efficiency compared with previous solutions.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2023)
Article
Computer Science, Information Systems
Xiaoqin Feng, Jianfeng Ma, Yinbin Miao, Ximeng Liu, Kim-Kwang Raymond Choo
Summary: We propose a proof-of-hardware-stake (PohS) consensus mechanism and a regulatory mechanism based on a consortium blockchain to address the vulnerabilities of existing PoS protocols. Our design is secure against adversarial stakes less than 51% and is proven to be efficient compared to Ethereum and Ouroboros through simulations.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2023)
Article
Computer Science, Theory & Methods
Yinbin Miao, Wei Zheng, Xinghua Li, Hongwei Li, Kim-Kwang Raymond Choo, Robert H. Deng
Summary: This paper proposes a secure Model-Contrastive Federated Learning with improved Compressive Sensing (MCFL-CS) scheme to address the issues of data heterogeneity and communication burdens in existing methods.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2023)
Article
Engineering, Civil
Keke Gai, Haokun Tang, Guangshun Li, Tianxiu Xie, Shuo Wang, Liehuang Zhu, Kim-Kwang Raymond Choo
Summary: This paper highlights the importance of data-driven applications in modern maritime transportation systems, particularly in communication and safety decision-making. The authors propose using blockchain to protect privacy and ensure data accuracy, and evaluate the security and performance of their approach.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
