Robust Network Security: A Deep Learning Approach to Intrusion Detection in IoT
Source abstract
Abstract
The proliferation of Internet of Things (IoT) technology has exponentially increased the number of devices interconnected over networks, thereby escalating the potential vectors for cybersecurity threats. In response, this st... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Network Security and Intrusion Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (29)
Show source-provided references
1. The Internet of Things (IoT) in healthcare: Taking stock and moving forward10.1016/j.iot.2023.100721 ↗
2. A review and state of art of Internet of Things (IoT)10.1007/s11831-021-09622-6 ↗
3. Cyber security threats and countermeasures in digital age10.54060/a2zjournals.jase.42 ↗
4. Detecting cyber threats in IoT networks: A machine learning approach
5. Sivagami · Secure Communication in Internet of Things · 253
6. Cyber resilience act 2022: A silver bullet for cybersecurity of IoT devices or a shot in the dark?10.1016/j.clsr.2024.106009 ↗
7. Study for integrating IoT-IDS datasets: Machine and deep learning for secure IoT network system
8. ML-IDS: An efficient ML-enabled intrusion detection system for securing IoT networks and applications10.1007/s00500-023-09452-7 ↗
9. A blockchain-based intrusion detection/pre vention systems in IoT network: A systematic review10.1016/j.procs.2024.05.048 ↗
10. Analysis of extreme learning machines (ELMs) for intelligent intrusion detection systems: A survey
11. Ullah · Future Communication Systems Using Artificial Intelligence, Internet of Things and Data Science · 2024
12. Khalil · Low-Power Wide Area Network for Large Scale Internet of Things · 2024 · 20310.1201/9781003426974-11 ↗
13. Anomaly detection IDS for detecting DoS attacks in IoT networks based on machine learning algorithms10.3390/s24020713 ↗
14. A critical review of artificial intelligence based approaches in intrusion detection: A comprehensive analysis
15. Machine learning in cybersecurity: A review of threat detection and defense mechanisms10.30574/wjarr.2024.21.1.0315 ↗
16. Future horizons: AI-enhanced threat detection in cloud environments: Unveiling opportunities for research
17. The visual detection of threat: A cautionary tale10.3758/s13423-013-0421-4 ↗
18. Searching for threat10.1080/02724980143000659 ↗
19. iNIDS: SWOT analysis and TOWS inferences of state-of-the-art NIDS solutions for the development of intelligent network intrusion detection system10.1016/j.comcom.2022.08.022 ↗
20. A comprehensive study on cybersecurity challenges and opportunities in the IoT world
21. Retracted article: Best features based intrusion detection system by RBM model for detecting DDoS in cloud environment10.1007/s12652-019-01611-9 ↗
22. An efficient metaheuristic algorithm based feature selection and recurrent neural network for DoS attack detection in cloud computing environment10.1016/j.asoc.2020.106997 ↗
23. Transient search optimization: A new meta-heuristic optimization algorithm10.1007/s10489-020-01727-y ↗
24. A self organizing map intrusion detection system for RPL protocol attacks
25. Security and privacy in IoT using machine learning and blockchain: Threats and countermeasures10.1145/3417987 ↗
26. Optimizing IoT intrusion detection system: Feature selection versus feature extraction in machine learning10.1186/s40537-024-00892-y ↗
27. A novel data preprocessing model for lightweight sensory IoT intrusion detection
28. DDoS intrusions detection in low power SD-IoT devices leveraging effective machine learning
29. UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)