Volume 23 , Issue 3 , PP: 233-244, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Ali Alqazzaz 1 * , Ibrahim Alrashdi 2
Doi: https://doi.org/10.54216/IJNS.230320
While an Automated Intrusion Response System (AIRS) chooses and initiates a suitable reaction from the pool of response groups based on specific response choice requirements to reduce the intrusion immediately, an Intrusion Detection System (IDS) finds the intrusions and generates alerts. The accurate assessment of the critical weight of all responses chosen and the prioritization of the incursion response set are the biggest hurdles when creating an AIRS. This study suggested a multi-criteria decision-making (MCDM) method for ranking intrusion responses. The TOPSIS method is an MCDM method used to rank the alternatives. The TOPSIS method integrated with the single-valued neutrosophic set (SVNS) to overcome uncertainty. This study used 16 criteria and 10 alternatives to be evaluated by experts and decision-makers. The sensitivity analysis shows the rank of other options under different cases. The criteria weights are changed under 17 cases. The results of sensitivity analysis show the rank of alternatives is stable. The suggested method was compared with other MCDM methods to show its effectiveness and robustness.
Intrusion Detection System , Security , Multi-Criteria Decision Making , Neutrosophic Logic , Intrusion Response.
[1] H. Alyami et al., “Effectiveness evaluation of different IDSs using integrated fuzzy MCDM model,” Electronics, vol. 11, no. 6, p. 859, 2022.
[2] Y. B. Abushark et al., “Cyber security analysis and evaluation for intrusion detection systems,” Comput. Mater. Contin, vol. 72, pp. 1765–1783, 2022.
[3] M. A. Q. Martinez, D. T. L. Rugel, C. J. E. Alcivar, and M. Y. L. Vazquez, “A framework for selecting classification models in the intruder detection system using TOPSIS,” in Human Interaction, Emerging Technologies and Future Applications III: Proceedings of the 3rd International Conference on Human Interaction and Emerging Technologies: Future Applications (IHIET 2020), August 27-29, 2020, Paris, France, Springer, 2021, pp. 173–179.
[4] A. Heidari and M. A. Jabraeil Jamali, “Internet of Things intrusion detection systems: a comprehensive review and future directions,” Cluster Computing, vol. 26, no. 6, pp. 3753–3780, 2023.
[5] K. He, D. D. Kim, and M. R. Asghar, “Adversarial machine learning for network intrusion detection systems: a comprehensive survey,” IEEE Communications Surveys & Tutorials, 2023.
[6] A. Alamleh et al., “Multi-attribute decision-making for intrusion detection systems: A systematic review,” International Journal of Information Technology & Decision Making, vol. 22, no. 01, pp. 589–636, 2023.
[7] A. Thakkar and R. Lohiya, “Fusion of statistical importance for feature selection in Deep Neural Network-based Intrusion Detection System,” Information Fusion, vol. 90, pp. 353–363, 2023.
[8] C. Hazman, A. Guezzaz, S. Benkirane, and M. Azrour, “lIDS-SIoEL: intrusion detection framework for IoT-based smart environments security using ensemble learning,” Cluster Computing, vol. 26, no. 6, pp. 4069–4083, 2023.
[9] D. Karabašević et al., “A novel extension of the TOPSIS method adapted for the use of single-valued neutrosophic sets and hamming distance for e-commerce development strategies selection,” Symmetry, vol. 12, no. 8, p. 1263, 2020.
[10] Y. Sun and Y. Cai, “A flexible decision-making method for green supplier selection integrating TOPSIS and GRA under the single-valued neutrosophic environment,” IEEE Access, vol. 9, pp. 83025–83040, 2021.
[11] Ş. Rıdvan, A. Fuat, and K. Gökçe Dilek, “A single‐valued neutrosophic multicriteria group decision approach with DPL‐TOPSIS method based on optimization,” International Journal of Intelligent Systems, vol. 36, no. 7, pp. 3339–3366, 2021.
[12] A. Abdel-Monem, N. A. Nabeeh, and M. Abouhawwash, “An Integrated Neutrosophic Regional Management Ranking Method for Agricultural Water Management,” Neutrosophic Systems with Applications, vol. 1, pp. 22–28, 2023.
[13] R. Mohamed and M. M. Ismail, “Harness Ambition of Soft Computing in Multi-Factors of Decision-Making Toward Sustainable Supply Chain in the Realm of Unpredictability,” Multicriteria Algorithms with Applications, vol. 2, pp. 29–42, 2024.
[14] S. Pramanik, P. P. Dey, and B. C. Giri, “TOPSIS for single valued neutrosophic soft expert set based multi-attribute decision-making problems,” Neutrosophic Sets and Systems, vol. 10, pp. 88–95, 2015.
[15] J. Ye, “An extended TOPSIS method for multiple attribute group decision making based on single valued neutrosophic linguistic numbers,” Journal of Intelligent & Fuzzy Systems, vol. 28, no. 1, pp. 247–255, 2015.
[16] P. Biswas, S. Pramanik, and B. C. Giri, “NonLinear programming approach for single-valued neutrosophic TOPSIS method,” New Mathematics and Natural Computation, vol. 15, no. 02, pp. 307–326, 2019.
[17] Ş. Özlü and F. Karaaslan, “Hybrid similarity measures of single-valued neutrosophic type-2 fuzzy sets and their application to MCDM based on TOPSIS,” Soft Computing, vol. 26, no. 9, pp. 4059–4080, 2022.
[18] K. M. Sallam and A. W. Mohamed, “Single Valued Neutrosophic Sets for Assessment Quality of Suppliers under Uncertainty Environment,” Multicriteria Algorithms with Applications, vol. 1, no. 1, pp. 1–10, 2023.
[19] X. Peng and J. Dai, “Approaches to single-valued neutrosophic MADM based on MABAC, TOPSIS and new similarity measure with score function,” Neural Computing and Applications, vol. 29, pp. 939–954, 2018.
[20] P. Biswas, S. Pramanik, and B. C. Giri, “TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment,” Neural computing and Applications, vol. 27, pp. 727–737, 2016.
[21] A. M. AbdelMouty and A. Abdel-Monem, “Neutrosophic MCDM Methodology for Assessment Risks of Cyber Security in Power Management,” Neutrosophic Systems with Applications, vol. 3, pp. 53–61, 2023.
[22] Smarandache, F. (2019). Neutrosophic set is a generalization of intuitionistic fuzzy set, inconsistent intuitionistic fuzzy set (picture fuzzy set, ternary fuzzy set), pythagorean fuzzy set, spherical fuzzy set, and q-rung orthopair fuzzy set, while neutrosophication is a generalization of regret theory, grey system theory, and three-ways decision (revisited). Journal of New Theory, (29), 1-31.
[23] D. Liu and D. Xu, “A Novel TOPSIS Method for Multiple Attribute Decision Making Based on Single-Valued Neutrosophic Sets.,” IAENG International Journal of Applied Mathematics, vol. 53, no. 1, 2023.
[24] S. S. Mohamed, A. Abdel-Monem, and A. A. Tantawy, “Neutrosophic MCDM Methodology for Risk Assessment of Autonomous Underwater Vehicles,” Neutrosophic Systems with Applications, vol. 5, pp. 44–52, 2023.
[25] M. A. Talukder et al., “A dependable hybrid machine learning model for network intrusion detection,” Journal of Information Security and Applications, vol. 72, p. 103405, 2023.
[26] M. J. Idrissi et al., “Fed-anids: Federated learning for anomaly-based network intrusion detection systems,” Expert Systems with Applications, vol. 234, p. 121000, 2023.
[27] G. Logeswari, S. Bose, and T. Anitha, “An intrusion detection system for sdn using machine learning,” Intelligent Automation & Soft Computing, vol. 35, no. 1, pp. 867–880, 2023.
[28] S. Wang, W. Xu, and Y. Liu, “Res-TranBiLSTM: An intelligent approach for intrusion detection in the Internet of Things,” Computer Networks, vol. 235, p. 109982, 2023.
[29] M. Mohy-eddine, A. Guezzaz, S. Benkirane, and M. Azrour, “An effective intrusion detection approach based on ensemble learning for IIoT edge computing,” Journal of Computer Virology and Hacking Techniques, vol. 19, no. 4, pp. 469–481, 2023.
[30] M. A. Hossain and M. S. Islam, “Ensuring network security with a robust intrusion detection system using ensemble-based machine learning,” Array, vol. 19, p. 100306, 2023.
[31] H. Attou et al., “Towards an intelligent intrusion detection system to detect malicious activities in cloud computing,” Applied Sciences, vol. 13, no. 17, p. 9588, 2023.
[32] S. Venkatesan, “Design an intrusion detection system based on feature selection using ML algorithms,” Mathematical Statistician and Engineering Applications, vol. 72, no. 1, pp. 702–710, 2023.
[33] S. Hariharan, R. R. Rejimol Robinson, R. R. Prasad, C. Thomas, and N. Balakrishnan, “XAI for intrusion detection system: comparing explanations based on global and local scope,” Journal of Computer Virology and Hacking Techniques, vol. 19, no. 2, pp. 217–239, 2023.
[34] F. Karaaslan and F. Hunu, “Type-2 single-valued neutrosophic sets and their applications in multi-criteria group decision making based on TOPSIS method,” Journal of Ambient Intelligence and Humanized Computing, vol. 11, pp. 4113–4132, 2020.
[35] A. Ashraf and M. A. Butt, “Extension of TOPSIS method under single-valued neutrosophic N-soft environment,” Neutrosophic sets and systems, vol. 41, pp. 286–303, 2021.
[36] H. Garg, Algorithms for single-valued neutrosophic decision making based on TOPSIS and clustering methods with new distance measure. Infinite Study, 2020.
[37] J. Chen, S. Zeng, and C. Zhang, “An OWA distance-based, single-valued neutrosophic linguistic topsis approach for green supplier evaluation and selection in low-carbon supply chains,” International journal of environmental research and public health, vol. 15, no. 7, p. 1439, 2018.
