banner
Home > Open Journal Systems

Network intrusion detection using ensemble weighted voting classifier based honeypot framework

Parvathi Pothumani, E Sreenivasa Reddy

Abstract


The Internet of Things (IoT) is a new model that connects physical objects and the Internet and has become one of the most important technological developments in computing. It is estimated that by 2022, one trillion physical objects will be connected to the Internet. The poor accessibility and lack of interoperability of many of these devices in a vast heterogeneous landscape make it difficult to design specific security measures and implement specific defences mechanism in addition, IoT networks are still open and vulnerable to network disruption attacks. Therefore, there is a need for additional security tools related to IoT. Intrusion Detection System could serve this purpose. Intrusion detection is the process of monitoring and analyzing network traffic in order to detect potential security breaches and unauthorized access to a IOT network. It involves the use of various technologies and techniques to identify and respond to potential threats in real-time. Network intrusion detection helps organizations protect their valuable assets, including sensitive data, intellectual property, and financial resources, from cyberattacks. By detecting and responding to potential security breaches in a timely manner, network intrusion detection systems can help organizations prevent or mitigate the impact of security incidents, minimize downtime and financial losses, and maintain the integrity of their operations and reputation. Weighted soft voting is a technique used in network intrusion detection to improve the accuracy and reliability of the detection process. It involves combining the results of multiple intrusion detection systems (IDS) based on decision tree, random forest and XGBoost using a weighted approach that assigns different levels of importance to each system based on its performance and reliability. The basic idea behind weighted soft voting is to give more weight to the predictions of IDS that have higher accuracy and lower false positive rates, and less weight to those that have lower accuracy and higher false positive rates. The proposed approach can help reduce the impact of false alarms and increase the sensitivity and specificity of the intrusion detection process.


Keywords


intrusion detection systems; weighted soft voting; decision tree; random forest; XGBoost

Full Text:

PDF

References


1. Kumari P, Jain AK. A comprehensive study of DDoS attacks over IoT network and their countermeasures. Computers & Security. 2023, 127: 103096. doi: 10.1016/j.cose.2023.103096

2. Maesschalck S, Giotsas V, Green B, et al. Don’t get stung, cover your ICS in honey: How do honeypots fit within industrial control system security. Computers & Security. 2022, 114: 102598. doi: 10.1016/j.cose.2021.102598

3. Khan SU, Eusufzai F, Azharuddin Redwan Md, et al. Artificial Intelligence for Cyber Security: Performance Analysis of Network Intrusion Detection. Explainable Artificial Intelligence for Cyber Security. Published online 2022: 113-139. doi: 10.1007/978-3-030-96630-0_6

4. Heidari A, Jabraeil Jamali MA. Internet of Things intrusion detection systems: A comprehensive review and future directions. Cluster Computing, 2022,11: 1-28.

5. Nazir A, Khan RA. A novel combinatorial optimization based feature selection method for network intrusion detection. Computers & Security. 2021, 102: 102164. doi: 10.1016/j.cose.2020.102164

6. ElSayed MS, Le-Khac NA, Albahar MA, et al. A novel hybrid model for intrusion detection systems in SDNs based on CNN and a new regularization technique. Journal of Network and Computer Applications. 2021, 191: 103160. doi: 10.1016/j.jnca.2021.103160

7. Mohammadzad M, Karimpour J. Using rootkits hiding techniques to conceal honeypot functionality. Journal of Network and Computer Applications. 2023, 214: 103606. doi: 10.1016/j.jnca.2023.103606

8. Al-Mohannadi H, Awan I, Al Hamar J. Analysis of adversary activities using cloud-based web services to enhance cyber threat intelligence. Service Oriented Computing and Applications. 2020, 14(3): 175-187. doi: 10.1007/s11761-019-00285-7

9. Alhajjar E, Maxwell P, Bastian N. Adversarial machine learning in Network Intrusion Detection Systems. Expert Systems with Applications. 2021, 186: 115782. doi: 10.1016/j.eswa.2021.115782

10. Dina AS, Manivannan D. Intrusion detection based on Machine Learning techniques in computer networks. Internet of Things. 2021, 16: 100462. doi: 10.1016/j.iot.2021.100462

11. Bangui H, Ge M, Buhnova B. A hybrid machine learning model for intrusion detection in VANET. Computing. 2021, 104(3): 503-531. doi: 10.1007/s00607-021-01001-0

12. da Costa KAP, Papa JP, Lisboa CO, et al. Internet of Things: A survey on machine learning-based intrusion detection approaches. Computer Networks. 2019, 151: 147-157. doi: 10.1016/j.comnet.2019.01.023

13. El Kamel N, Eddabbah M, Lmoumen Y, et al. A Real-Time Smart Agent for Network Traffic Profiling and Intrusion Detection Based on Combined Machine Learning Algorithms. Smart Innovation, Systems and Technologies. 2021, 301-309. doi: 10.1007/978-981-16-3637-0_21

14. Guarascio M, Cassavia N, Pisani FS, et al. Boosting Cyber-Threat Intelligence via Collaborative Intrusion Detection. Future Generation Computer Systems. 2022, 135: 30-43. doi: 10.1016/j.future.2022.04.028

15. Danilov VD, Ovasapyan TD, Ivanov DV, et al. Generation of Synthetic Data for Honeypot Systems Using Deep Learning Methods. Automatic Control and Computer Sciences. 2022, 56(8): 916-926. doi: 10.3103/s014641162208003x

16. Shahid WB, Aslam B, Abbas H, et al. A deep learning assisted personalized deception system for countering web application attacks. Journal of Information Security and Applications. 2022, 67: 103169. doi: 10.1016/j.jisa.2022.103169

17. Lampe B, Meng W. A survey of deep learning-based intrusion detection in automotive applications. Expert Systems with Applications. 2023, 221: 119771. doi: 10.1016/j.eswa.2023.119771

18. Kilincer IF, Ertam F, Sengur A. Machine learning methods for cyber security intrusion detection: Datasets and comparative study. Computer Networks. 2021, 188: 107840. doi: 10.1016/j.comnet.2021.107840

19. Tang J, Chen M, Chen H, et al. A new dynamic security defense system based on TCP_REPAIR and deep learning. Journal of Cloud Computing. 2023, 12(1). doi: 10.1186/s13677-022-00379-2

20. Srinivasan S, P D. Enhancing the security in cyber-world by detecting the botnets using ensemble classification based machine learning. Measurement: Sensors. 2023, 25: 100624. doi: 10.1016/j.measen.2022.100624

21. Matheen MA, S Sundar. Mitigation of network security attacks in wireless multimedia sensors networks using intrusion detection system. 2024, 7(1). doi: 10.32629/jai.v7i1.751




DOI: https://doi.org/10.32629/jai.v7i3.1081

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Parvathi Pothumani, E Sreenivasa Reddy

License URL: https://creativecommons.org/licenses/by-nc/4.0/