Related references
Note: Only part of the references are listed.
Article
Computer Science, Artificial Intelligence
Zhenyu Guan et al.
Summary: This paper proposes a novel multiple image hiding framework DeepMIH based on invertible neural network. An invertible hiding neural network (IHNN) is developed to model the image concealing and revealing as its forward and backward processes innovatively, making them fully coupled and reversible. In addition, an importance map (IM) module is designed to guide the current image hiding and enhance the invisibility. Experimental results show that DeepMIH significantly outperforms other state-of-the-art methods in terms of hiding invisibility, security and recovery accuracy.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Amir Javadpour et al.
Summary: Cloud Internet of Things (CIoT) environments are often targeted by adversaries, leading to the need for effective intrusion detection and prevention systems (IDPSs). However, existing IDPSs have limitations in detecting unknown attacks and being susceptible to single point of failure. This paper proposes a novel distributed multi-agent IDPS (DMAIDPS) that overcomes these limitations and achieves significant improvements in Recall, Accuracy, and F-Score metrics.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Xin Deng et al.
Summary: Multi-modal image registration aims to align two images from different modalities by separating alignment features from non-alignment features. The proposed DCSC model and InMIR-Net use deep learning to improve registration accuracy and efficiency. The accompanying guidance network further enhances feature extraction. Extensive experiments demonstrate the effectiveness of the method on various multi-modal image datasets.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2023)
Article
Automation & Control Systems
Xingxia Dai et al.
Summary: This article investigates the application of mobile edge computing and device-to-device offloading in the industrial Internet of Things, proposing a co-offloading framework and a learning-based algorithm to address challenges such as mobility, selfishness, and incomplete information.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Civil
Yi Han et al.
Summary: This paper introduces an optimized lidar and camera sensor fusion method for road environment sensing of intelligent vehicles. The method achieves superior results in road boundary detection and lane line classification.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Telecommunications
Heba A. Hassan et al.
Summary: This research focuses on the application of Intrusion Detection Systems in Software-Defined Network-based IoT networks, and explores the potential of blockchain technology in enhancing network security.
WIRELESS PERSONAL COMMUNICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Deepak Kumar Jain et al.
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
Keerthi Kethineni et al.
Summary: Smart agriculture is gaining popularity and a deep learning-based framework is proposed for intrusion detection in smart farming systems. The framework consists of a three-tier architecture, with sensor placement in the agricultural areas, implementation of intrusion detection system in fog nodes, and cloud computing providing storage and services. The proposed model uses a fused CNN model with Bi-GRU and an attention mechanism to detect and classify intrusions. The classification accuracy is improved through the use of a meta-heuristic optimization algorithm. The proposed system outperforms existing methods in terms of accuracy, detection rate, precision, and F-Score for APA DDoS attack and ToN-IoT datasets.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Engineering, Multidisciplinary
Chao Lu et al.
Summary: This study tackles the distributed hybrid flowshop scheduling problem (DHFSP) with the objective of minimizing the makespan. To solve the DHFSP, a mixed-integer linear programming model is formulated and an improved iterated greedy (IIG) algorithm is proposed. The IIG algorithm incorporates a new initialization strategy, a hybrid operator, and a new local search method to enhance the quality of the initial solution, improve global search ability, and strengthen exploitation capability. Experimental results demonstrate the effectiveness of the IIG algorithm in addressing DHFSP.
ENGINEERING OPTIMIZATION
(2023)
Article
Computer Science, Theory & Methods
Hossein Asgharzadeh et al.
Summary: The growth and prevalence of IoT devices have increased attacks, making it vital to detect anomalies in IoT devices to reduce attacks and strengthen security. Researchers have used machine learning and deep learning methods for anomaly-based intrusion detection, with the main challenge being the inability to extract new features. Convolutional neural networks, with their ability to extract features and perform faster calculations, are considered powerful in this field. This study proposes the IoTFECNN and BMECapSA methods, and the combination of the two achieves better results compared to existing systems. The proposed method reaches high accuracy by identifying effective features on two datasets, NSL-KDD and TON-IoT.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2023)
Article
Computer Science, Information Systems
Zenggang Xiong et al.
Summary: This paper proposes a comprehensive confirmation-based selfish node detection algorithm (CCSDA) which constructs a reputation evaluation model and introduces node centrality to determine the selfish attributes of nodes. It also monitors node behavior using an interaction information-based detection mechanism to optimize network performance.
JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY
(2023)
Article
Engineering, Mechanical
Xianyong Ma et al.
Summary: This study proposes an innovative real-time assessment method of asphalt pavement moduli and traffic loads based on structural health monitoring, which can simultaneously obtain the information of traffic loads and pavement moduli. The validity of the proposed method was theoretically verified, and the effects of the viscoelastic property of asphalt materials, measurement errors, and layer thickness on the identification accuracy were comprehensively discussed. The proposed assessment method has application prospects for understanding pavement damage behavior, improving pavement design methods, and guiding maintenance decisions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Computer Science, Artificial Intelligence
V Shanmuganathan et al.
Summary: The seamless integration of wireless and IoT devices in daily life and smart homes has increased the need for security and privacy. Attacks and anomalies in open IoT systems can lead to false alarms and information processing delays. An effective attack and anomaly alert system is critical in IoT-based environments due to the sensitive data carried by these power-hungry devices. This paper proposes an anomaly detection algorithm that handles anomalies and false information in the network, providing efficient anomaly detection in real-time sensor data. The proposed methodology achieves a 96.03% effective anomaly detection rate with 92.48% training accuracy, outperforming the KNN algorithm by 6.54% in anomaly rejection and 5.13% in training accuracy.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Information Systems
Chaimae Hazman et al.
Summary: This paper presents a novel intrusion detection framework for IoT-based smart environments, which utilizes machine learning and deep learning techniques for improved protection. The framework demonstrates good performance in experiments and is capable of accurately detecting anomalies.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Haigen Min et al.
Summary: Fault diagnosis for autonomous vehicles is important to provide information about the vehicle's operation status and avoid potential risks. This study proposes a fault diagnosis framework with sensor self-diagnosis, using a residual consistency checking algorithm and a denoising shrinkage autoencoder (DSAE) for anomaly detection. Experimental results show that the proposed algorithm effectively detects and isolates failed sensors, and the DSAE achieves the best anomaly detection performance compared to other machine learning methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Theory & Methods
Yujia Song et al.
Summary: This paper proposes a deep learning-based anomaly detection method for multivariate time series in real-world operational clouds. The method utilizes two parallel graph neural networks (GNN) to learn the time and feature inter-dependencies, resulting in fewer false positives. The evaluation shows that the proposed method outperforms seven baseline methods with an average F1-Score of 0.871, improving by 19.9% compared to state-of-the-art methods. Additionally, the method correctly identifies the root cause of detected anomalies with up to 74.1% accuracy.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Song Han et al.
Summary: Privacy-preserving federated learning is a promising approach for IoT in healthcare, and this article proposes two practical noninteractive federated learning schemes, HE-NFRT and Mask-NFRT, which can protect the privacy of data owners' training data, resist collusion attacks, and support strong verification.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Theory & Methods
Jiliang Zhang et al.
Summary: To prevent model stealing, researchers propose a method called APMSA, which adds small noise to each input query to make the model's returned confidence distribution consistent, thus reducing information leakage to attackers and effectively defending against model stealing attacks.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2023)
Article
Computer Science, Artificial Intelligence
Xinxin Zhou et al.
Summary: This paper proposes a method for human detection in crowded scenarios by using a feature pyramid network (FPN), multi-scale feature fusion technology, and attention mechanisms. The method, called SA-FPN, consists of a Scale-FPN structure, an attention-based lateral connection (ALC) module, and a bottom-up path augmentation (BPA) module. Experimental results show the effectiveness of SA-FPN in improving human detection performance.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Hardware & Architecture
Eid Rehman et al.
Summary: Data security and privacy are crucial in the era of Internet of Things (IoT), and machine learning techniques provide effective means for detecting and preventing cyber attacks.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Information Systems
Amir Basati et al.
Summary: This paper presents a new and lightweight architecture for intrusion detection in IoT devices based on Parallel Deep Auto-Encoder (PDAE). By separating features using local and surrounding information, the accuracy of the model is improved while reducing the number of parameters and resource requirements. The effectiveness of the proposed model is evaluated and it outperforms state-of-the-art algorithms in terms of both accuracy and performance.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Yilong Ren et al.
Summary: Traffic prediction is crucial for traffic guidance and control, and it serves as a valuable decision-making tool for travelers to plan their routes and avoid congested roads. To tackle the challenge of short-term traffic prediction for special events, a traffic burst-sensitive model (TBSM) is proposed, which outperforms traditional machine learning and deep learning approaches according to experimental results.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Telecommunications
Ya Li et al.
Summary: This study utilizes machine learning and artificial intelligence algorithms to design an intrusion detection system that uses an artificial neural network to detect abnormal behaviors in a medical IoT system. By employing the butterfly optimization algorithm to select key features of network traffic data, the accuracy reached 93.27%. The proposed algorithm outperformed other algorithms previously used for the same purpose.
WIRELESS PERSONAL COMMUNICATIONS
(2022)
Article
Engineering, Multidisciplinary
Qiufen Ni et al.
Summary: Community partition is crucial in social networks, particularly in the face of network growth and increased data and applications. This study focuses on the community partition problem under the Linear Threshold (LT) model, aiming to maximize influence propagation within each community. The proposed continuous greedy algorithm, which exploits the properties of the relaxed function, is able to effectively improve influence spread and accuracy of the community partition. Theoretical analysis demonstrates a 1 - 1/e approximation ratio for the algorithm, while extensive experiments on real-world online social networks datasets validate its performance.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Nitish Chopra et al.
Summary: The Golden Jackal Optimization (GJO) algorithm, inspired by the hunting behavior of golden jackals, utilizes prey searching, enclosing, and pouncing steps mathematically to solve challenging engineering problems with unidentified search spaces.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Moutaz Alazab et al.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Kunrui Cao et al.
Summary: This paper investigates the security of semi-grant-free NOMA transmission in the presence of passive and active eavesdropping attacks. Different scheduling schemes are proposed to combat eavesdropping attacks for different user scenarios. The system performance is evaluated by analyzing the secrecy outage probability. Simulation results validate the superiority of the proposed schemes.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Engineering, Biomedical
N. V. L. M. Krishna Munagala et al.
Summary: A continuous heart disease monitoring system is an important application of the Internet of Things. This study proposes a unique IoT-enabled heart disease prediction method using the Fuzzy-Long Short Term Memory (LSTM) model and an improved Harris Hawks Optimization (HHO). The system combines multiple sources and features to improve the efficiency of heart disease healthcare and reduce the death rate.
BIOCYBERNETICS AND BIOMEDICAL ENGINEERING
(2022)
Article
Computer Science, Information Systems
T. V. Ramana et al.
Summary: In recent infrastructures, the Internet of Things (IoT) is crucial for connecting various actuators and sensors, but its security remains a concern. Therefore, an ambient intelligence approach is proposed for intrusion detection system (IDS). By combining reinforcement learning and deep Q-neural network, a RL-DQN model is developed to enhance the decision performance of IDS in IoT.
COMPUTER COMMUNICATIONS
(2022)
Article
Computer Science, Hardware & Architecture
Nilesh Kunhare et al.
Summary: An intrusion detection system is crucial for detecting threats and unauthorized access. This paper proposes a novel feature selection method using a genetic algorithm and hybrid classification with logistic regression and decision tree. Experimental results show that the gray wolf optimization algorithm achieves the best performance with a reduced feature set. The proposed method is compared with existing methods, demonstrating improved performance.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Article
Computer Science, Theory & Methods
Muhammed Amin Abdullah et al.
Future Generation Computer Systems-The International Journal of eScience
(2022)
Article
Automation & Control Systems
Bin Li et al.
Summary: This article proposes two distributionally robust model predictive control algorithms for handling discrete linear systems with unbounded noise. The algorithms consider chance constraints on both state and control, and employ deterministic convex reformulations inspired by distributionally robust optimization. Computational complexity analysis demonstrates the effectiveness of the proposed algorithms, and proofs of recursive feasibility and convergence are provided. Simulation results are presented to show their effectiveness.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Engineering, Civil
Jing Chen et al.
Summary: In this study, a DMF method based on disparity depths is proposed to address the low accuracy issue in transportation detection of long-distance small objects. By mapping different disparity regions to 2D candidate regions, the small-object detection problem is solved. The method clusters disparity maps of different depths and performs feature fusion of different scales. Experimental results on two datasets demonstrate that the DMF method can improve the detection accuracy of small objects.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Information Systems
Bin Li et al.
Summary: This article investigates a dynamic event-triggered security control problem in networked control systems under deception attacks and packet dropouts. A combined cyberattack model is proposed to reflect randomly occurring cyber-attacks. A dynamic event-triggered protocol is constructed to reduce data transmission and relieve bandwidth pressure. An online model predictive control algorithm is established to ensure stochastic stability and expected performance. Two examples are simulated to verify the effectiveness of the proposed design strategy.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Ravinder Kumar et al.
Summary: Smart devices connected to the internet, known as the Internet of Things (IoT), have advantages in improving interaction but also raise privacy and security concerns. This study proposes a hybrid Hunger Games Search and Remora Optimization Algorithm (HHGS-ROA) to address security problems in IoT networks, achieving better performance than existing methods.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Mohammed Alweshah et al.
Journal of Ambient Intelligence and Humanized Computing
(2022)
Article
Computer Science, Information Systems
Omar Alfandi et al.
Summary: The paper comprehensively reviews recent security and privacy challenges related to IoT, categorizing them according to the IoT layered architecture: perception, network, and application layer. The study investigates blockchain technology as a key pillar to overcome many of the IoT security and privacy problems, while also exploring the added value of combining blockchain with other new technologies such as machine learning, especially in intrusion detection systems. Additionally, challenges and privacy issues resulting from the integration of blockchain in IoT applications are highlighted, and a framework of IoT security and privacy requirements via blockchain technology is proposed.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Interdisciplinary Applications
Benjamin Lindemann et al.
Summary: This article surveys state-of-the-art anomaly detection techniques, focusing on using deep neural networks and especially long short-term memory networks for anomaly detection. The approaches are evaluated based on application scenarios, data, anomaly types, and other metrics, highlighting the potential of upcoming anomaly detection techniques, including graph-based and transfer learning approaches.
COMPUTERS IN INDUSTRY
(2021)
Article
Computer Science, Artificial Intelligence
Prabhat Kumar et al.
Summary: With the development of IoT, fog computing plays a crucial role in enhancing data collection and processing, while also improving security against malicious attacks.
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2021)
Article
Computer Science, Information Systems
Anjum Nazir et al.
Summary: The rapid advancements in communication technologies and services have brought new challenges in cybersecurity. Traditional intrusion detection techniques are not sufficient to protect against modern attacks, leading to the need for innovative solutions like machine learning algorithms. Feature selection methods like 'Tabu Search - Random Forest' have shown promising results in improving classification accuracy and reducing false positives in Network Intrusion Detection Systems.
COMPUTERS & SECURITY
(2021)
Article
Engineering, Electrical & Electronic
Guanghua Liu
Summary: The research introduces the concept of MI-WPUSN, integrating MI communication techniques and wireless power transfer mechanisms to provide high network reliability potential with challenging data collection. A systematic research roadmap is outlined for data collection, spanning from sensor deployment to frequency-selective routing establishment.
IEEE COMMUNICATIONS MAGAZINE
(2021)
Article
Telecommunications
Xingbing Fu et al.
Summary: With the rise of IoT, network attacks have become more diversified and intelligent, highlighting the importance of IDS for network security. This study tested the robustness of three IDS models and found that CNN was the most robust to adversarial examples under normal training, while GRU and LSTM significantly improved their robustness after adversarial training.
ANNALS OF TELECOMMUNICATIONS
(2021)
Article
Computer Science, Information Systems
Sydney Mambwe Kasongo
Summary: The study proposes an intrusion detection system for Industrial IoT using genetic algorithm and random forest model, providing security, privacy, and integrity for the network. Experimental results show that the system outperformed existing intrusion detection frameworks.
Article
Computer Science, Hardware & Architecture
Yuyang Zhou et al.
Article
Computer Science, Hardware & Architecture
Shubhra Dwivedi et al.
Article
Multidisciplinary Sciences
Omar Almomani
Article
Computer Science, Hardware & Architecture
Bin Cao et al.
Proceedings Paper
Computer Science, Theory & Methods
Monika Roopak et al.
2020 10TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE (CCWC)
(2020)
Article
Automation & Control Systems
Jiangping Hu et al.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2019)
Article
Engineering, Electrical & Electronic
Abebe Diro et al.
IEEE COMMUNICATIONS MAGAZINE
(2018)
Proceedings Paper
Computer Science, Theory & Methods
Yaping Chang et al.
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING (CSE) AND IEEE/IFIP INTERNATIONAL CONFERENCE ON EMBEDDED AND UBIQUITOUS COMPUTING (EUC), VOL 1
(2017)
Article
Computer Science, Interdisciplinary Applications
Seyedali Mirjalili et al.
ADVANCES IN ENGINEERING SOFTWARE
(2016)
Review
Computer Science, Hardware & Architecture
Mohiuddin Ahmed et al.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2016)
Article
Computer Science, Artificial Intelligence
Seyedali Mirjalili
KNOWLEDGE-BASED SYSTEMS
(2016)
Article
Computer Science, Information Systems
Bo Cheng et al.
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
(2016)
Article
Computer Science, Information Systems
Bo Cheng et al.
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
(2016)
