Related references
Note: Only part of the references are listed.
Article
Computer Science, Artificial Intelligence
Ankit Thakkar et al.
Summary: This study aims to design a Deep Neural Network-based Intrusion Detection System (IDS) and enhance its performance by proposing a novel feature selection technique. The technique selects features based on the fusion of statistical importance and is evaluated using multiple evaluation metrics and datasets.
INFORMATION FUSION
(2023)
Article
Computer Science, Information Systems
Sahraoui Dhelim et al.
Summary: Trust Management System (TMS) is crucial in IoT networks to ensure network security, data integrity, and promote legitimate devices while punishing malicious activities. Trust scores assigned by TMSs reflect devices' reputations, which help predict future behaviors and assess reliability in IoT networks. This article proposes Trust2Vec, a TMS for large-scale IoT systems that leverages a random-walk network exploration algorithm and network embeddings community detection algorithm to manage trust relationships and mitigate large-scale trust attacks by malicious devices.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Muhammad Zeeshan et al.
Summary: The Internet of Things (IoT) as a breakthrough technology is revolutionizing business and society. To prevent unauthorized access to critical resources, the need for deep intrusion detection is increasing. By utilizing deep learning techniques, a classification accuracy of 96.3% has been achieved.
Article
Computer Science, Hardware & Architecture
Hind Bangui et al.
Summary: In this paper, a lightweight distributed IDS is proposed which utilizes centralized platforms to train and learn from large amounts of data. The benefits of two promising bioinspired optimization algorithms, Ant Lion Optimization and Ant Colony Optimization, are investigated to find the optimal data representation for the classification process. Using Deep Forest as a classifier, the proposed approach enhances the reliability of lightweight intrusion detection systems in terms of accuracy and execution time.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Editorial Material
Computer Science, Information Systems
Muhammad Shafiq et al.
SECURITY AND COMMUNICATION NETWORKS
(2022)
Article
Mathematical & Computational Biology
Abdelghani Dahou et al.
Summary: This study proposes a novel framework to improve intrusion detection system performance by utilizing data from the Internet of things environments. The framework uses deep learning and metaheuristic optimization algorithms for feature extraction and selection. A convolutional neural network is implemented as the core feature extractor, and a feature selection mechanism based on the Reptile Search Algorithm is proposed. The framework achieved competitive performance in classification metrics compared to other optimization methods.
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
(2022)
Review
Computer Science, Artificial Intelligence
Javier Maldonado et al.
Summary: This paper presents a review of recent advances in wrapper feature selection techniques in the field of intrusion detection. It is difficult to determine the current level of research in this area due to the large number of published papers. By providing a classification taxonomy, evaluation metrics, and discussion on attack scenarios, this paper offers a comprehensive overview of the existing research, as well as identifies open challenges and new directions.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Dawei Wei et al.
Summary: This paper presents a novel approach for identifying unknown Internet traffic, utilizing surge period-based feature extraction and JigClu identification model, achieving an accuracy of no less than 74%. It is of great significance in addressing the challenges in Internet traffic identification.
Article
Computer Science, Information Systems
Pushparaj Nimbalkar et al.
Summary: This paper proposes a feature selection method for intrusion detection systems using Information Gain and Gain Ratio, achieving higher performance in detecting DoS and DDoS attacks on IoT-BoT and KDD Cup 1999 datasets.
Article
Chemistry, Analytical
Andrew Churcher et al.
Summary: In recent years, there has been a significant increase in IoT devices and generated data, leading to potential security vulnerabilities. Traditional IDS struggle to efficiently handle the growing number of network attacks. This study focuses on the application of machine learning methods in IDS.
Article
Chemistry, Analytical
Parushi Malhotra et al.
Summary: The rapid growth of IoT has unlocked the vision of a smart world but also raised significant security concerns. The research focuses on intrusion detection systems, analyzes various threats, discusses healthcare applications and security issues in IoT, and addresses research challenges for dealing with anomalies effectively.
Article
Computer Science, Information Systems
Muhammad Shafiq et al.
Summary: Researchers have proposed a new feature selection method and algorithm to accurately detect malicious traffic in IoT networks. By integrating TOPSIS and Shannon entropy methods to validate the selected features for malicious traffic identification in IoT networks, the experimental results have shown that this method is efficient and can achieve over 96% accuracy on average.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Chemistry, Analytical
Segun I. Popoola et al.
Summary: This paper proposes an efficient DL-based botnet attack detection algorithm that can handle highly imbalanced network traffic data, using Synthetic Minority Oversampling Technique (SMOTE) to achieve class balance. Experimental results show that this approach outperforms state-of-the-art ML and DL models in detecting botnet attacks in IoT networks.
Article
Computer Science, Information Systems
Naercio Magaia et al.
Summary: The significant evolution of the Internet of Things has led to the development of smart city devices that have replaced manual labor, increasing efficiency and intelligence in cities. However, the increased sensitivity of data, especially in the industrial sector, has attracted hackers targeting Industrial IoT devices or networks, leading to a rise in the number of malware infections. This article discusses the concept and applications of IIoT in smart cities, as well as the security challenges faced in this emerging area, along with available deep learning techniques to enhance IIoT security.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Review
Computer Science, Information Systems
Lerina Aversano et al.
Summary: This study systematically reviewed and analyzed the current research status of applying deep learning techniques to various IoT security scenarios. Contributions were classified according to different perspectives to identify gaps in key research areas, focusing on security aspects, DL network architectures, and datasets. The discussion highlighted research gaps, as well as drawbacks and vulnerabilities of DL approaches in IoT security scenarios.
COMPUTER SCIENCE REVIEW
(2021)
Article
Computer Science, Information Systems
Osama Alkadi et al.
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
Computer Science, Information Systems
Abdenacer Naouri et al.
Summary: This article proposes a three-layer task offloading framework named DCC, which effectively offloads tasks with high computing requirements to the cloud while executing tasks with low computing requirements on end devices to reduce processing delay. Experimental and simulation results demonstrate that DCC outperforms other computational offloading techniques in terms of performance.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Review
Computer Science, Information Systems
Jacek Krupski et al.
Summary: This paper surveys various CNN-based traffic analysis methods, with a focus on the importance of data transformation schemes in this field. Due to the different structures of network traffic data and machine learning data, it is crucial to study how to transform the data.
Article
Computer Science, Information Systems
Enkhtur Tsogbaatar et al.
Summary: IoT devices are vulnerable due to insecure design, implementation, and configuration, which triggers multiple challenges in securing them, such as complex attack detection, data imbalance, and heterogeneity. This study proposes DeL-IoT, a deep ensemble learning framework for IoT anomaly detection and prediction, which uses deep feature extraction and ensemble models to achieve efficient detection and forecasting of device status. The experimental results demonstrate that DeL-IoT outperforms competing models in terms of performance and reliability.
INTERNET OF THINGS
(2021)
Review
Computer Science, Hardware & Architecture
Hamid Tahaei et al.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2020)
Article
Engineering, Multidisciplinary
Nilesh Kunhare et al.
SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES
(2020)
Article
Computer Science, Theory & Methods
Bhuvaneswari N. G. Amma et al.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2020)
Article
Computer Science, Information Systems
N. G. Bhuvaneswari Amma et al.
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Firuz Kamalov et al.
2020 13TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID 2020)
(2020)
Article
Computer Science, Theory & Methods
Sydney M. Kasongo et al.
JOURNAL OF BIG DATA
(2020)
Article
Computer Science, Theory & Methods
Nickolaos Koroniotis et al.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2019)
Article
Computer Science, Artificial Intelligence
Sofiane Maza et al.
APPLIED INTELLIGENCE
(2019)
Article
Computer Science, Information Systems
Haipeng Yao et al.
IEEE INTERNET OF THINGS JOURNAL
(2019)
Proceedings Paper
Computer Science, Information Systems
Olakunle Ibitoye et al.
2019 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)
(2019)
Article
Computer Science, Information Systems
Lionel N. Tidjon et al.
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS
(2019)
Article
Computer Science, Information Systems
Nour Moustafa et al.
INFORMATION SECURITY JOURNAL
(2016)
Article
Computer Science, Artificial Intelligence
Felix Iglesias et al.
Proceedings Paper
Telecommunications
Francisco J. Aparicio-Navarro et al.
2014 IEEE MILITARY COMMUNICATIONS CONFERENCE: AFFORDABLE MISSION SUCCESS: MEETING THE CHALLENGE (MILCOM 2014)
(2014)
