Intrusion Detection Systems: A Revisit of Performance Evaluation Parameters
DOI:
https://doi.org/10.59675/E212Keywords:
Computer systems security, Intrusion detection system, Network Security, Performance evaluation metrics, Cyber-Physical Systems.Abstract
The effectiveness efficiency of various intrusion detection systems (IDSs) have always been evaluated on various datasets using different techniques. This evaluation normally covers a range of metrics, ranging from those that evaluated their accuracy to those that focus on their usability and performance. The evaluation of several attributes of IDS depends on the use of several metrics which have been proposed and used in various studies. Regrettably, there are no existing benchmark metric for the network intrusion detection systems as studies are still ongoing in this regard. Quantitative evaluation of the performance of IDS is mainly based on the use of several performance but in this article, most of the existing performance metrics were reviewed and used for the evaluation of the performance of various IDSs using different benchmark datasets. Furthermore, this study will aid in better understanding of the various performance metrics used for the evaluation of the performance of novel IDSs.; it will also further the insights to those that have found interest in the uses and the development of IDSs and related areas.
References
Abushark YB, Khan AI, Alsolami F, Almalawi A, Alam MM, Agrawal A, et al. Cyber Security Analysis and Evaluation for Intrusion Detection Systems. Comput Mater Contin. 2022;72(1):1765–83.
Ozkan-Okay M, Samet R, Aslan O, Gupta D. A Comprehensive Systematic Literature Review on Intrusion Detection Systems. IEEE Access. 2021;9:157727–60.
Eskandari M, Janjua ZH, Vecchio M, Antonelli F. Passban IDS: An Intelligent Anomaly-Based Intrusion Detection System for IoT Edge Devices. IEEE Internet Things J. 2020;7(8):6882–97.
Lansky J, Ali S, Mohammadi M, Majeed MK, Karim SHT, Rashidi S, et al. Deep Learning-Based Intrusion Detection Systems: A Systematic Review. IEEE Access. 2021;9:101574–99.
Golrang A, Golrang AM, Yayilgan SY, Elezaj O. A novel hybrid ids based on modified NSGAII-ANN and random forest. Electron. 2020;9(4):1–19.
Gu G, Fogla P, Dagon D, Lee W, Skorić B. Measuring intrusion detection capability. 2006;90–101.
Huang S, Lei K. IGAN-IDS: An imbalanced generative adversarial network towards intrusion detection system in ad-hoc networks. Ad Hoc Networks. 2020;105.
Liu L, Ma Z, Meng W. Detection of multiple-mix-attack malicious nodes using perceptron-based trust in IoT networks. Futur Gener Comput Syst [Internet]. 2019;101:865–79. Available from: https://doi.org/10.1016/j.future.2019.07.021
Kumar S, Gupta S, Arora S. Research Trends in Network-Based Intrusion Detection Systems: A Review. IEEE Access. 2021;9:157761–79.
Gendreau AA, Moorman M. Survey of intrusion detection systems towards an end to end secure internet of things. Proc - 2016 IEEE 4th Int Conf Futur Internet Things Cloud, FiCloud 2016. 2016;84–90.
Nam K, Kim K. A Study on SDN security enhancement using open source IDS/IPS Suricata. 9th Int Conf Inf Commun Technol Converg ICT Converg Powered by Smart Intell ICTC 2018. 2018;1124–6.
Mohamed TS, Aydin S. IoT-Based Intrusion Detection Systems: A Review. Smart Sci [Internet]. 2022;10(4):265–82. Available from: https://doi.org/10.1080/23080477.2021.1972914
Yaacoub JPA, Salman O, Noura HN, Kaaniche N, Chehab A, Malli M. Cyber-physical systems security: Limitations, issues and future trends. Microprocess Microsyst [Internet]. 2020;77:103201. Available from: https://doi.org/10.1016/j.micpro.2020.103201
Imoize AL, Oyedare T, Otuokere ME, Shetty S. Software Intrusion Detection Evaluation System: A Cost-Based Evaluation of Intrusion Detection Capability. Commun Netw. 2018;10(04):211–29.
Guruprasad S, Rio D’Souza GL. Chaos multiobjective evolutionary based technique to obtain accurate solutions in intrusion detection systems. Indian J Comput Sci Eng. 2020;11(2):188–94.
Klangjorhor J, Phanphaisarn A, Teeyakasem P, Chaiyawat P, Phinyo P, Settakorn J, et al. In vitro drug sensitivity (IDS) of patient-derived primary osteosarcoma cells as an early predictor of the clinical outcomes of osteosarcoma patients. Cancer Chemother Pharmacol [Internet]. 2020;85(6):1165–76. Available from: https://doi.org/10.1007/s00280-020-04081-5
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Academic international journal of engineering science
This work is licensed under a Creative Commons Attribution 4.0 International License.