Results 11 to 20 of about 200,258 (113)
A Novel Deep Learning-Based Intrusion Detection System for IoT Networks
De Computis, 2023 The impressive growth rate of the Internet of Things (IoT) has drawn the attention of cybercriminals more than ever. The growing number of cyber-attacks on IoT devices and intermediate communication media backs the claim.
A. Awajan
semanticscholar +1 more source
Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study
Journal of Information Security and Applications, 2020 In this paper, we present a survey of deep learning approaches for cyber security intrusion detection, the datasets used, and a comparative study. Specifically, we provide a review of intrusion detection systems based on deep learning approaches.
M. Ferrag +3 more
semanticscholar +1 more source
Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection [PDF]
Network and Distributed System Security Symposium, 2018 Neural networks have become an increasingly popular solution for network intrusion detection systems (NIDS). Their capability of learning complex patterns and behaviors make them a suitable solution for differentiating between normal traffic and network ...
Yisroel Mirsky +3 more
semanticscholar +1 more source
Transactions on Emerging Telecommunications Technologies, 2020 The rapid advances in the internet and communication fields have resulted in a huge increase in the network size and the corresponding data. As a result, many novel attacks are being generated and have posed challenges for network security to accurately ...
Zeeshan Ahmad +4 more
semanticscholar +1 more source
Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction [PDF]
Journal of Big DataCybersecurity has emerged as a critical global concern. Intrusion Detection Systems (IDS) play a critical role in protecting interconnected networks by detecting malicious actors and activities.
Md. Alamin Talukder +6 more
semanticscholar +1 more source
A Survey of Network-based Intrusion Detection Data Sets [PDF]
Computers & security, 2019 Labeled data sets are necessary to train and evaluate anomaly-based network intrusion detection systems. This work provides a focused literature survey of data sets for network-based intrusion detection and describes the underlying packet- and flow-based
Markus Ring +4 more
semanticscholar +1 more source
Deep Learning Approach for Intelligent Intrusion Detection System
IEEE Access, 2019 Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyberattacks at the network-level and the host-level in a timely and automatic manner.
R. Vinayakumar +5 more
semanticscholar +1 more source
Survey of intrusion detection systems: techniques, datasets and challenges
Cybersecurity, 2019 Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in accurately detecting intrusions. Failure to prevent the intrusions could degrade the credibility of security services, e.g. data confidentiality, integrity, and
Ansam Khraisat +3 more
semanticscholar +1 more source
IEEE Access, 2020 Although the Internet of Things (IoT) can increase efficiency and productivity through intelligent and remote management, it also increases the risk of cyber-attacks.
Abdullah Alsaedi +4 more
semanticscholar +1 more source
Expert systems with applications, 2023 The Internet of Things (IoT) is currently seeing tremendous growth due to new technologies and big data. Research in the field of IoT security is an emerging topic.
Bhawana Sharma +3 more
semanticscholar +1 more source