Review
Computer Science, Hardware & Architecture
Cristiano Antonio de Souza, Carlos Becker Westphall, Renato Bobsin Machado, Leandro Loffi, Carla Merkle Westphall, Guilherme Arthur Geronimo
Summary: The Internet of Things and fog computing play important roles in smart environments, but security is a major challenge. Therefore, research on intrusion detection and prevention is necessary. This article conducts a systematic literature review to evaluate existing technologies and propose possible directions for future research.
Article
Automation & Control Systems
Mohammed M. Alani, Ali Ismail Awad
Summary: The Internet of Things (IoT) has become a driving paradigm in various applications, but its security vulnerabilities and threats have negative impacts on deployment and operation. This article presents an intelligent two-layer intrusion detection system for IoT, using machine learning techniques to handle flow and packet features and minimizing time overhead by selecting significant features for accurate intrusion detection.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Hardware & Architecture
Wenjuan Li, Christian Stidsen, Tobias Adam
Summary: A collaborative intrusion detection system (CIDS) is crucial for protecting decentralized computing platforms such as smart cities and IoT networks. Traditional CIDS often rely on centralized computing servers, which compromises the integrity of shared information. Blockchain technology provides a solution to this problem and has shown promising benefits in CIDS. This work introduces a blockchain-assisted security management framework for CIDS, demonstrating its effectiveness in both simulated and real CIDS setups.
COMPUTERS & ELECTRICAL ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Gauri Kalnoor, S. Gowrishankar
Summary: The proposed work aims to design an intelligent intrusion detection system using machine learning models to identify and protect IoT networks from attacks. Experimental results show that the Markov model performs well in the I-IDS IoT network, achieving a 100% detection rate and low false alarm rate.
Article
Computer Science, Information Systems
Jasleen Kaur, Alka Agrawal, Raees Ahmad Khan
Summary: With the growth of the digital population, managing users' private data flowing across the web has become challenging. Fog computing has addressed certain issues but also raised concerns about privacy. The authors propose an encryfuscation model that employs obfuscation and encryption techniques, selecting suitable privacy preservation techniques based on offloading decisions. They also propose obfuscation techniques for data and location.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Mathematics
Iyad Katib, Mahmoud Ragab
Summary: The centralized storage structure in IoT applications poses security, privacy, and single point of failure issues, which can be addressed by using blockchain technology. However, DDoS attacks have revealed limitations in blockchain-assisted IoT networks. This study proposes a hybrid Harris Hawks with sine cosine and deep learning-based intrusion detection system (H3SC-DLIDS) to recognize DDoS attacks in a blockchain-supported IoT environment.
Article
Mathematics
Nazia Butt, Ana Shahid, Kashif Naseer Qureshi, Sajjad Haider, Ashraf Osman Ibrahim, Faisal Binzagr, Noman Arshad
Summary: This paper proposes a novel solution for anomaly-based intrusion detection for smart home networks. The proposed solution uses feature selection and hyperparameter tuning and has been experimentally demonstrated to have significant performance improvement.
Article
Computer Science, Information Systems
Osama Alkadi, Nour Moustafa, Benjamin Turnbull, Kim-Kwang Raymond Choo
Summary: Significant research has been done on combining blockchain and intrusion detection for enhanced data privacy and detection of cyberattacks. Learning-based ensemble models can identify complex malicious events while ensuring data privacy, providing additional security during VM migration and IoT network protection. The deep blockchain framework proposed in this study outperforms peer models and has potential as a decision support system for secure data migration.
IEEE INTERNET OF THINGS JOURNAL
(2021)
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, Multidisciplinary
Hanan Naser Alsuqaih, Walaa Hamdan, Haythem Elmessiry, Hussein Abulkasim
Summary: The development of IoT has enabled remote health data analysis, but protecting patients' data privacy is challenging. Blockchain technology is proposed as a solution to enable secure and private exchange of personal health data. This work addresses the inadequacy of previous work in providing safe and privacy-preserving diagnostic enhancement for e-Health platforms.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Computer Science, Information Systems
Henry Vargas, Carlos Lozano-Garzon, German A. Montoya, Yezid Donoso
Summary: This paper integrates Blockchain algorithms and Machine Learning techniques to create a comprehensive protection mechanism for IoT device networks, allowing for threat identification, activation of secure information transfer mechanisms, and adaptation to the computational capabilities of industrial IoT. The proposed solution achieves its objectives and is presented as a viable mechanism for detecting and containing intruders in an IoT network, surpassing traditional detection mechanisms such as an IDS in some cases.
Article
Computer Science, Information Systems
Kim-Hung Le, Minh-Huy Nguyen, Trong-Dat Tran, Ngoc-Duan Tran
Summary: The increasing popularity of IoT has brought convenience and efficiency to our lives, but also exposed the devices to various cyber-attacks. To protect IoT devices, this paper presents IMIDS, an intelligent intrusion detection system. IMIDS utilizes a lightweight convolutional neural network model to classify cyber threats and proposes an attack data generator to address the shortage of training data. The experiment shows that IMIDS outperforms its competitors in detecting cyber-attacks and achieves improved performance with additional training using the generated attack data.
Article
Computer Science, Information Systems
Weiping Ding, Mohamed Abdel-Basset, Hossam Hawash, Sara Abdel-Razek, Chuansheng Liu
Summary: This paper presents a privacy-preserving federated learning framework, called Fed-ESD, for epileptic seizure detection from EEG signals in the fog-computing-based IoMT. It introduces a lightweight and efficient spatiotemporal transformer network for collaborative learning of spatial and temporal representations. Experimental evaluations demonstrate the efficiency of the proposed Fed-ESD in terms of detection performance, resource-efficiency, stability, and scalability for deployment in the IoMT.
INFORMATION SCIENCES
(2023)
Article
Mathematics
Abdulaziz Aldribi, Aman Singh
Summary: This study proposes a blockchain-empowered decentralized and scalable solution for a sustainable smart-city network using technologies such as IoT, fog nodes, permissioned trust chain, smart contract, blockchain, and IPFS. It improves performance, scalability, and distribution for a sustainable smart-city network.
Article
Computer Science, Hardware & Architecture
Cristiano Antonio de Souza, Carlos Becker Westphall, Renato Bobsin Machado
Summary: Due to resource limitations in Internet of Things devices, security is often overlooked. This study proposes a two-step approach for intrusion detection and identification, which includes traffic analysis and ensemble methods. The proposed approach is evaluated on multiple intrusion datasets, demonstrating its robustness.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Article
Chemistry, Analytical
Nikhil Kamath, Roshan Fernandes, Anisha P. Rodrigues, Mufti Mahmud, P. Vijaya, Thippa Reddy Gadekallu, M. Shamim Kaiser
Summary: This paper proposes a navigation method based on traffic knowledge to enable precise navigation of connected and autonomous vehicles (CAVs) in urban traffic environments. By analyzing sensor-oriented traffic data, a precise navigation path is generated, and traffic knowledge is shared with other vehicles to enable CAVs navigation. Experimental results confirm the benefits of this method in accurately navigating CAVs in urban traffic environments.
Article
Telecommunications
Chien -Ming Chen, Zhen Li, Saru Kumari, Gautam Srivastava, Kuruva Lakshmanna, Thippa Reddy Gadekallu
Summary: In recent years, the Social Internet of Vehicles (SIoV) has significantly improved people's lifestyles in transportation by collecting and processing real-time traffic information, alleviating congestion, and enabling smart transportation in smart cities. However, the data transmitted on public channels by vehicles moving with servers is susceptible to interception and tampering by attackers, posing a security risk to driver's sensitive information. Additionally, the massive amount of real-time data generated by vehicles, devices, drivers, passengers, and social relationships creates a heavy load on servers. This paper proposes a secure authentication protocol using confidential computing environments and introduces an improved key transfer phase to reduce computation pressure on cloud servers. The protocol is analyzed using the Real-Oracle Random model, demonstrating its provable security. Experimental evaluation shows excellent computational and communication performance of the proposed protocol.
VEHICULAR COMMUNICATIONS
(2023)
Article
Computer Science, Information Systems
Y. Supriya, Thippa Reddy Gadekallu
Summary: Federated Learning is a distributed, privacy-preserving machine learning model that is gaining attention. It has broad applications in various fields, but faces challenges such as communication costs, privacy concerns, and data management. The survey provides a definition and analysis of federated learning systems, offering insights for future research. The review explores the aggregation of federated learning systems with soft computing techniques, as well as the integration of nature-inspired techniques to mitigate its flaws. The paper also discusses potential future developments of combining federated learning and soft computing techniques.
ACM JOURNAL OF DATA AND INFORMATION QUALITY
(2023)
Article
Computer Science, Information Systems
Chun-Hao Chen, Cheng-Yu Lu, Rui-Dong Chiang, Gautam Srivastava, Jerry Chun-Wei Lin
Summary: This paper proposes an intelligent optimization algorithm to obtain a more profitable diverse group stock portfolio with active and inactive stocks. The coding scheme considers not only grouping, stocks, and weighting, but also active stock parts. Two evaluation functions are developed based on factors such as group balance, modified portfolio satisfaction, price balance, unit balance, and extended diversity factor. Empirical studies on two financial datasets are conducted to demonstrate the merits of the proposed algorithm.
ENTERPRISE INFORMATION SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Mehul Sharma, Shrid Pant, Priety Yadav, Deepak Kumar Sharma, Nitin Gupta, Gautam Srivastava
Summary: This paper proposes a novel deep progressive algorithm that utilizes progressive learning and deep neural networks to address the security threats faced by IIoT devices. The proposed Deep Progressive Neural Network (DPNN) accurately classifies numerous attacks and efficiently adds new classes to the previously trained network. Experimental results demonstrate that the DPNN model outperforms the contemporary KNN model in terms of attack classification accuracy.
MOBILE NETWORKS & APPLICATIONS
(2023)
Article
Chemistry, Analytical
Balraj Kumar, Neeraj Sharma, Bhisham Sharma, Norbert Herencsar, Gautam Srivastava
Summary: Recommender systems are important but face challenges in producing quality recommendations due to sparsity issues. This study introduces a hierarchical Bayesian hybrid recommendation model, RCTR-SMF, for recommending music artists to users. The model integrates social matrix factorization and link probability functions into collaborative topic regression-based recommender systems, utilizing auxiliary domain knowledge for better prediction accuracy. The model addresses sparsity and cold-start problems, and shows superior performance compared to other state-of-the-art algorithms, as demonstrated on a large social media dataset.
Article
Ecology
Tawfiq Hasanin, Hariprasath Manoharan, Hassan A. Alterazi, Gautam Srivastava, Shitharth Selvarajan, Jerry Chun-Wei Lin
Summary: Extending the visibility factor to a higher depth in the development phase of a subterranean communication system is challenging. The proposed method integrates a fiber optic communication system with a specific energy source and external panels to optimize the visibility parameter. By minimizing the reflection index and using generative adversarial network (GAN), the method enhances the effectiveness of the system in all subsurface areas.
FRONTIERS IN ECOLOGY AND EVOLUTION
(2023)
Editorial Material
Computer Science, Information Systems
Gautam Srivastava, Jerry Chun-Wei Lin, Zhihan Lv
ACM JOURNAL OF DATA AND INFORMATION QUALITY
(2023)
Article
Computer Science, Hardware & Architecture
Feng Chen, Thippa Reddy Gadekallu
Summary: To effectively extract multidimensional data and accurately evaluate the quality of network distance teaching in universities, an optimal classification algorithm is proposed. The algorithm utilizes various perspectives and evaluation indices to classify the data and improve the accuracy of the evaluation. Experimental results demonstrate high classification accuracy and evaluation precision, indicating the effectiveness of the algorithm in extracting multidimensional teaching quality evaluation from the distance teaching data and improving the accuracy of network distance teaching quality evaluation.
MOBILE NETWORKS & APPLICATIONS
(2023)
Article
Automation & Control Systems
Kehua Guo, Zheng Wu, Weizheng Wang, Sheng Ren, Xiaokang Zhou, Thippa Reddy Gadekallu, Entao Luo, Chao Liu
Summary: Traffic sign recognition is an important aspect of autonomous vehicle research, and deep learning techniques have contributed significantly to its progress. However, the distribution of traffic sign information in complex road conditions is long-tailed, posing a significant challenge for autonomous vehicle applications. This paper introduces the gradient rebalanced traffic sign recognition (GRTR) method, which evaluates the classifier's prediction and classification biases, and dynamically adjusts correction and compensation factors to prevent distribution imbalance and enhance the performance of the traffic sign classifier under difficult road conditions.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Information Systems
Muhammad Rehan, Abdul Rehman Javed, Natalia Kryvinska, Thippa Reddy Gadekallu, Gautam Srivastava, Zunera Jalil
Summary: Supply chain management (SCM) is pivotal in the industrial life cycle, with on-time delivery and transportation mode importance for safe and secure supply. The use of Industrial Internet of Things (IIoT) and blockchain platforms (like Ethereum) improves tracking, security, and transaction speed. This research proposes SCMIIOT, a novel approach that maintains traceability, data integrity, and achieves fast transaction through the use of sensor devices, 5G, and distributed database systems. Experimental results show the effectiveness of SCMIIOT, with fast file copying, block creation, and peer-to-peer connection times.
HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Ziqing Xia, Zhangyang Gao, Anfeng Liu, Neal N. Xiong
Summary: In this paper, an asymmetric quorum-based neighbor discovery (AQND) protocol is proposed to reduce delay, improve energy utilization and lifetime, and outperform previous strategies in main performance indicators.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiaohuan Liu, Anfeng Liu, Shaobo Zhang, Tian Wang, Neal N. Xiong
Summary: This paper proposes a delay differentiated services routing (DDSR) scheme to reduce the deployment costs for wireless sensor networks (WSNs) with wake-up radio (WuR) functionality, while meeting the delay requirement of forwarding urgent data and maintaining a long lifetime.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Artificial Intelligence
Qinghua Gu, Yan Wang, Peipei Wang, Xuexian Li, Lu Chen, Neal N. Xiong, Di Liu
Summary: This paper proposes a new ensemble clustering method that combines the influence of cluster level and the base clustering level in a unified framework. The method inserts a global weighting strategy into a local ensemble cluster learning framework, improving the robustness and stability of clustering.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Giuseppe Varone, Wadii Boulila, Maha Driss, Saru Kumari, Muhammad Khurram Khan, Thippa Reddy Gadekallu, Amir Hussain
Summary: This study introduces an unsupervised EEG preprocessing pipeline and fusion-based CNN method for distinguishing Motor Imagery (MI) from finger movements (Mex). The proposed approach achieves significant progress in distinguishing MI and Mex activities, demonstrating its potential to serve as a benchmark for future real-time BCI systems. This research contributes to the global interdisciplinary research community and enables the development of more effective and user-friendly BCI systems.
INFORMATION FUSION
(2024)
