CL2ES-KDBC: A Novel Covariance Embedded Selection Based on Kernel Distributed Bayes Classifier for Detection of Cyber-Attacks in IoT Systems
Source abstract
Abstract
The Internet of Things (IoT) is a growing technology that allows the sharing of data with other devices across wireless networks. Specifically, IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks. In this framework, a Covariance Linear Learning Embedding Selection (CL2ES) methodology is used at first to extract the features highly associated with the IoT intrusions. Then, the Kernel Distributed Bayes Classifier (KDBC) is created to forecast attacks based on the probability distribution value precisely. In addition, a unique Mongolian Gazellas Optimization (MGO) algorithm is used to optimize the weight value for the learning of the classifier. The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets, The obtained results are then compared with current classification methods regarding accuracy (97%), precision (96.5%), and other factors. Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed, which provides valuable insight into its performance, efficiency, and suitability for securing IoT networks.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Network Security and Intrusion Detection · Advanced Malware Detection Techniques · Anomaly Detection Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (33)
Show source-provided references
1. An intrusion detection system using BoT-IoT10.3390/app13095427 ↗
2. A novel deep learning-based intrusion detection dystem for IoT networks10.3390/computers12020034 ↗
3. A time-efficient approach toward DDoS attack detection in IoT network using SDN10.1109/JIOT.2021.3098029 ↗
4. Design and development of a deep learning-based model for anomaly detection in IoT networks10.1109/ACCESS.2021.3094024 ↗
5. A deep learning methodology for predicting cybersecurity attacks on the Internet of Things10.3390/info14100550 ↗
6. Network intrusion detection using machine learning techniques10.12913/22998624/149934 ↗
7. Design and development of RNN anomaly detection model for IoT networks10.1109/ACCESS.2022.3176317 ↗
8. Intrusion detection systems for Internet of Thing based big data: A review
9. Hybrid CNN+LSTM based intrusion detection system for industrial IoT networks
10. Deep learning-based intrusion detection systems: A systematic review10.1109/ACCESS.2021.3097247 ↗
11. Survey on IoT security: Challenges and solution using machine learning, artificial intelligence and blockchain technology10.1016/j.iot.2020.100227 ↗
12. Machine learning based intrusion detection systems for IoT applications
13. Deep learning algorithms for intrusion detection systems in Internet of Things using CIC-IDS, 2017 dataset
14. A novel deep learning-based intrusion detection system for IoT networks10.3390/computers12020034 ↗
15. Detection of real-time malicious intrusions and attacks in IoT empowered cybersecurity infrastructures10.1109/ACCESS.2023.3238664 ↗
16. IoT-based cyber security identification model through machine learning technique
17. A machine learning based IoT for providing an intrusion detection system for security10.1016/j.micpro.2020.103741 ↗
18. Anomaly-based intrusion detection system for IoT networks through deep learning model10.1016/j.compeleceng.2022.107810 ↗
19. Intrusion detection in the IoT under data and concept drifts: Online deep learning approach10.1109/JIOT.2022.3167005 ↗
20. A locality-sensitive hashing-based jamming detection system for IoT networks
21. Anomaly-based intrusion detection system for IoT application
22. Anomaly-based intrusion detection system for IoT networks through deep learning model10.1016/j.compeleceng.2022.107810 ↗
23. A novel jammer detection framework for cluster-based wireless sensor networks10.1186/s13638-016-0528-1 ↗
24. Deep transfer learning for IoT attack detection10.1109/ACCESS.2020.3000476 ↗
25. Hybrid intrusion detection system for Internet of Things (IoT)10.36548/jismac.2020.4.002 ↗
26. Enhance intrusion detection in computer networks based on deep extreme learning machine
27. DL-IDS: A deep learning-based intrusion detection framework for securing IoT10.1002/ett.3803 ↗
28. Fog-based attack detection framework for Internet of Things using deep learning10.1109/ACCESS.2020.2988854 ↗
29. Towards a machine learning-based framework for DDOS attack detection in software-defined IoT (SD-IoT) networks10.1016/j.engappai.2023.106432 ↗
30. Machine learning based attacks detection and countermeasures in IoT
31. Towards an effective deep learning-based intrusion detection system in the Internet of Things10.1016/j.teler.2022.100009 ↗
32. Deep-learning based detection for cyber-attacks in IoT networks: A distributed attack detection framework10.1007/s10922-023-09722-7 ↗
33. Building an efficient intrusion detection system based on feature selection and ensemble classifier10.1016/j.comnet.2020.107247 ↗