Volume 25 , Issue 2 , PP: 268-278, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Thangam .S 1 * , Jana .S 2
Doi: https://doi.org/10.54216/IJNS.250223
Neutrosophic Logic is an offspring study region in which every intention is projected to hold the proportion of indeterminacy in a subset I, the percentage of truth in a subset T, and the percentage of falsity in subset F. Neutrosophic set (NS) has been effectively used for indeterminate data processing, and establishes benefits to handle with the indeterminacy information of data and is quite a method stimulated for classification application and data analysis. NS delivers an effective and precise method to describe imbalanced data as per the features of the data. Recently, the usage of the Internet of Things (IoT) has enlarged rapidly, and cyber security effects have enlarged beside it. On the state-of-the-art of cyber security is Artificial Intelligence (AI), which employed for the progress of intricate techniques to defense systems and networks, containing IoT systems. Though, cyber-attackers have determined how to develop AI and have started to utilize adversarial AI for accomplishing cybersecurity threats. Therefore, this study designs a new Interval-Valued Neutrosophic Set using Optimization Algorithm-Based Intrusion Detection System (IVNSOA-IDS) technique in IoT cybersecurity. The key objective of the IVNSOA-IDS method rests in the automatic identification of intrusion detection in IoT cybersecurity. In the IVNSOA-IDS technique, data pre-processing is executed to convert the raw data into a compatible format. Besides, the interval-valued neutrosophic set (IVNS) model has been utilized for the automated identification of intrusion detection. Finally, an improved whale optimization algorithm (IWOA) is employed for the better hyperparameter tuning of the IVNS classifier. To demonstrate the enhanced performance of the IVNSOA-IDS technique, an extensive of simulations take place and the performances are inspected under distinct aspects. The experimental outcome reported the advancement of the IVNSOA-IDS methodology under various metrics.
Intrusion Detection System , Interval-Valued Neutrosophic Set, Whale Optimization Algorithm , Neutrosophic set , Neutrosophic Logic
[1] Ibrahim, M.A., Agboola, A.A.A, Badmus, B.S., and Akinleye, S.A., "On Refined Neutrosophic Vector Spaces I", International Journal of Neutrosophic Science, Vol. 7, pp. 97-109, 2020.
[2] Smarandache F., and Abobala, M., " n-Refined Neutrosophic Vector Spaces", International Journal of Neutrosophic Science, Vol. 7, pp. 47-54, 2020.
[3] Tuqa A. H. Al-Tamimi, Luay A. A. Al-Swidi , Ali H. M. Al-Obaidi. "Partner Sets for Generalizations of MultiNeutrosophic Sets." International Journal of Neutrosophic Science, Vol. 24, No. 1, 2024 ,PP. 08-13
[4] Parimala, M., Karthika, M. and Smarandache, F., 2020. A review of fuzzy soft topological spaces, intuitionistic fuzzy soft topological spaces and neutrosophic soft topological spaces. International Journal of Neutrosophic Science, Vol. 10, No. 2, 2020 ,PP. 96-104.
[5] Ashraf, S. and Abdullah, S., 2020. Decision support modeling for agriculture land selection based on sine trigonometric single valued neutrosophic information. International Journal of Neutrosophic Science (IJNS), 9(2), pp.60-73.
[6] Alqahtani, H., Sarker, I.H., Kalim, A., Hossain, M., Md, S., Ikhlaq, S., & Hossain, S. (2020, March). Cyber intrusion detection using machine learning classification techniques. In International Conference on Computing Science, Communication and Security (pp. 121-131). Springer, Singapore.
[7] Shin, Y., & Kim, K. (2020). Comparison of anomaly detection accuracy of host-based intrusion detection systems based on different machine learning algorithms. International Journal of Advanced Computer Science and Applications, 11(2).
[8] Alzahrani, A.O., & Alenazi, M.J. (2021). Designing a network intrusion detection system based on machine learning for software defined networks. Future Internet, 13(5), 111.
[9] Rokade, M.D., & Sharma, Y.K. (2021, March). MLIDS: A Machine Learning Approach for Intrusion Detection for Real Time Network Dataset. In 2021 International Conference on Emerging Smart Computing and Informatics (ESCI) (pp. 533-536). IEEE.
[10] Sangkatsanee, P., Wattanapongsakorn, N., & Charnsripinyo, C. (2021). Practical real-time intrusion detection using machine learning approaches. Computer Communications, 34(18), 2227- 2235.
[11] Amoo, O.O., Osasona, F., Atadoga, A., Ayinla, B.S., Farayola, O.A. and Abrahams, T.O., 2024. Cybersecurity threats in the age of IoT: A review of protective measures. International Journal of Science and Research Archive, 11(1), pp.1304-1310.
[12] Ahakonye, L.A.C., Nwakanma, C.I. and Kim, D.S., 2024. Tides of Blockchain in IoT Cybersecurity. Sensors, 24(10), p.3111.
[13] Nadella, G.S. and Gonaygunta, H., 2024. Enhancing Cybersecurity with Artificial Intelligence: Predictive Techniques and Challenges in the Age of IoT. International Journal of Science and Engineering Applications, 13(04), pp.30-33.
[14] Adewuyi, A., Oladele, A.A., Enyiorji, P.U., Ajayi, O.O., Tsambatare, T.E., Oloke, K. and Abijo, I., 2024. The convergence of cybersecurity, Internet of Things (IoT), and data analytics: Safeguarding smart ecosystems. World Journal of Advanced Research and Reviews, 23(1), pp.379-394.
[15] Lai, T., Farid, F., Bello, A. and Sabrina, F., 2024. Ensemble learning based anomaly detection for IoT cybersecurity via Bayesian hyperparameters sensitivity analysis. Cybersecurity, 7(1), p.44.
[16] Maghrabi, L.A., Shabanah, S., Althaqafi, T., Alsalman, D., Algarni, S., Abdullah, A.L. and Ragab, M., 2024. Enhancing cybersecurity in the internet of things environment using bald eagle search optimization with hybrid deep learning. IEEE Access.
[17] Broumi, S., Nagarajan, D., Bakali, A., Talea, M., Smarandache, F. and Lathamaheswari, M., 2019. The shortest path problem in interval valued trapezoidal and triangular neutrosophic environment. Complex & Intelligent Systems, 5, pp.391-402.
[18] Saleh, I., Borhan, N., Yunus, A. and Rahiman, W., 2024. Comprehensive Technical Review of Recent Bio-Inspired Population-Based Optimization (BPO) Algorithms for Mobile Robot Path Planning. IEEE Access.
[19] https://www.kaggle.com/datasets/solarmainframe/ids-intrusion-csv
[20] Najafi Mohsenabad, H. and Tut, M.A., 2024. Optimizing cybersecurity attack detection in computer networks: A comparative analysis of bio-inspired optimization algorithms using the CSE-CIC-IDS 2018 dataset. Applied Sciences, 14(3), p.1044.
[21] Yang, H., Xu, J., Xiao, Y. and Hu, L., 2023. SPE-ACGAN: A resampling approach for class imbalance problem in network intrusion detection systems. Electronics, 12(15), p.3323.
