Volume 24 , Issue 4 , PP: 205-222, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Mohamed Elhoseny 1 * , Mahmoud Abdel-salam 2 , Ibrahim M. Elhasnony 3
Doi: https://doi.org/10.54216/IJNS.240415
Intrusion Detection is crucial in contemporary cybersecurity landscapes to proactively thwart and identify possible threats. The risk of data breaches, malicious activities, and unauthorized access escalates as organizations increasingly rely on interconnected systems. Intrusion Detection Systems (IDS) are imperative for the continuous monitoring of system and network activities, quickly identifying patterns or anomalies indicative of cyber threats. IDS acts as a frontline defense mechanism with the ability to identify abnormal behaviors and known attack signatures. Prompt recognition allows for safeguarding sensitive data, timely response, fortifying the overall resilience of IT infrastructures, and reducing the effect of security incidents. The implementation of robust IDS is vital in an era marked by evolving cyber threats to ensure the confidentiality, availability, and integrity of digital assets. This study develops an improved Arithmetic Optimization Algorithm with an Extended Fuzzy Neutrosophic Classifier technique (AOA-EFNSC) for Accurate Intrusion Detection and Classification. The main goal of proposing this model is to recognize the presence of intrusions effectually. A min-max scalar is applied to normalize the input data before using the improved AOA as a feature selection method. For intrusion detection, the proposed model uses the FNSC technique for the recognition and classification of the intrusions. A sequence of experimentations was involved to validate the superior performance of the proposed model. The experimental value pointed out that our proposed approach outperforms the previous models and enhances the intrusion detection results.
Intrusion Detection System , Cyberattack , Arithmetic Optimization Algorithm , Neutrosophic Classifier , Fuzzy Logic
[1] Om Prakash, P.G., Maram, B., Nalinipriya, G. and Cristin, R., 2021. Harmony search Hawks optimization-based Deep reinforcement learning for intrusion detection in IoT using nonnegative matrix factorization. International Journal of Wavelets, Multiresolution and Information Processing, 19(04), p.2050093.
[2] Hsu, Y.F. and Matsuoka, M., 2020, November. A deep reinforcement learning approach for anomaly network intrusion detection system. In 2020 IEEE 9th International Conference on Cloud Networking (CloudNet) (pp. 1-6). IEEE.
[3] Dang, Q.V. and Vo, T.H., 2022. Reinforcement learning for the problem of detecting intrusion in a computer system. In Proceedings of Sixth International Congress on Information and Communication Technology (pp. 755-762). Springer, Singapore.
[4] Anil Audumbar Pise, Saurabh Singh, Hemachandran K., Shraddhesh Gadilkar, Zakka Benisemeni Esther, Ganesh Shivaji Pise, Jude Imuede, Investigating Recent Advances In Coded Diffraction Patterns using Deep Learning, Journal of International Journal of Wireless and Ad Hoc Communication, Vol. 7 , No. 1 , (2023) : 62-71 (Doi : https://doi.org/10.54216/IJWAC.070106)
[5] Venturi, A., Apruzzese, G., Andreolini, M., Colajanni, M. and Marchetti, M., 2021. Drelab-deep reinforcement learning adversarial botnet: A benchmark dataset for adversarial attacks against botnet intrusion detection systems. Data in Brief, 34, p.106631.
[6] Priya, S. and PradeepMohankumar, K., 2021, December. Intelligent Outlier Detection with Optimal Deep Reinforcement Learning Model for Intrusion Detection. In 2021 4th International Conference on Computing and Communications Technologies (ICCCT) (pp. 336-341). IEEE.
[7] Alavizadeh, H., Alavizadeh, H. and Jang-Jaccard, J., 2022. Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion Detection. Computers, 11(3), p.41.
[8] Gupta, G.P., 2022. Intrusion Detection Framework Using an Improved Deep Reinforcement Learning Technique for IoT Network. In Soft Computing for Security Applications (pp. 765-779). Springer, Singapore.
[9] Alawsi, A.S.S. and Kurnaz, S., 2022. Quality of service system that is self-updating by intrusion detection systems using reinforcement learning. Applied Nanoscience, pp.1-8.
[10] Bouhamed, O., Bouachir, O., Aloqaily, M. and Al Ridhawi, I., 2021, May. Lightweight ids for uav networks: A periodic deep reinforcement learning-based approach. In 2021 IFIP/IEEE International Symposium on Integrated Network Management (IM) (pp. 1032-1037). IEEE.
[11] Park, S.B., Jo, H.J. and Lee, D.H., 2023. G-idcs: Graph-based intrusion detection and classification system for can protocol. IEEE Access.
[12] Fayed, N.S., Elmogy, M.M., Atwan, A. and El-Daydamony, E., 2022. Efficient Occupancy Detection System Based on Neutrosophic Weighted Sensors Data Fusion. IEEE Access, 10, pp.13400-13427.
[13] Hassan, G.M., Gumaei, A., Alanazi, A. and Alzanin, S.M., 2023. A Network Intrusion Detection Approach Using Extreme Gradient Boosting with Max-Depth Optimization and Feature Selection. International Journal of Interactive Mobile Technologies, 17(15).
[14] Zainudin, A., Akter, R., Kim, D.S. and Lee, J.M., 2023. Federated Learning Inspired Low-Complexity Intrusion Detection and Classification Technique for SDN-Based Industrial CPS. IEEE Transactions on Network and Service Management.
[15] Prasad, M., Tripathi, S. and Dahal, K., 2023. A probability estimation-based feature reduction and Bayesian rough set approach for intrusion detection in mobile ad-hoc network. Applied Intelligence, 53(6), pp.7169-7185.
[16] Sajithra Varun, S. and Nagarajan, G., 2023. DeepAID: a design of smart animal intrusion detection and classification using deep hybrid neural networks. Soft Computing, pp.1-12.
[17] Abdelhafeez, A., Mohamed, H.K., Maher, A. and Khalil, N.A., 2023. A novel approach toward skin cancer classification through fused deep features and neutrosophic environment. Frontiers in Public Health, 11, p.1123581.
[18] Alshehri, M.D., 2023. An integrated AHP MCDM based Type-2 Neutrosophic Model for Assessing the Effect of Security in Fog-based IoT Framework. International Journal of Neutrosophic Science (IJNS), 20(2).
[19] Chinnasamy, V. and Rajasekaran, S., 2023. Multi-Valued Neutrosophic Convolutional LSTM for Intrusion Detection. International Journal of Intelligent Engineering & Systems, 16(5).
[20] Mirza, O.M. and Samak, A.H., 2024. Neutrosophic Fuzzy Logic-Based Hybrid CNN-LSTM for Accurate Chest X-ray Classification in COVID-19 Prediction. Appl. Math, 18(1), pp.139-152.
[21] Dias, T.F., Vitorino, J., Fonseca, T., Praça, I., Maia, E. and Viamonte, M.J., 2023, September. Unravelling Network-Based Intrusion Detection: A Neutrosophic Rule Mining and Optimization Framework. In European Symposium on Research in Computer Security (pp. 59-75). Cham: Springer Nature Switzerland.
[22] Henderi, H., Wahyuningsih, T. and Rahwanto, E., 2021. Comparison of Min-Max normalization and Z-Score Normalization in the K-nearest neighbor (kNN) Algorithm to Test the Accuracy of Types of Breast Cancer. International Journal of Informatics and Information Systems, 4(1), pp.13-20.
[23] Dhal, K.G., Sasmal, B., Das, A., Ray, S. and Rai, R., 2023. A comprehensive survey on arithmetic optimization algorithm. Archives of Computational Methods in Engineering, 30(5), pp.3379-3404.
[24] A. A. Salama, H. A. Elagamy, On Neutrosophic Fuzzy Ideal Concepts, Journal of International Journal of Neutrosophic Science, Vol. 14 , No. 2 , (2021) : 98-103 (Doi : https://doi.org/10.54216/IJNS.140203)
[25] Priya, S. and Kumar, K., 2023. Binary bat algorithm based feature selection with deep reinforcement learning technique for intrusion detection system. Soft Computing, pp.1-12.
[26] Eskandari, M., Janjua, Z. H., Vecchio, M., & Antonelli, F. (2020). Passban IDS: An intelligent anomaly-based intrusion detection system for IoT edge devices. IEEE Internet of Things Journal, 7(8), 6882-6897.
[27] Bakro, M., Kumar, R. R., Alabrah, A., Ashraf, Z., Ahmed, M. N., Shameem, M., & Abdelsalam, A. (2023). An improved design for a cloud intrusion detection system using hybrid features selection approach with ML classifier. IEEE Access.
[28] Alkanhel, R., El-kenawy, E. S. M., Abdelhamid, A. A., Ibrahim, A., Alohali, M. A., Abotaleb, M., & Khafaga, D. S. (2023). Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization. Computers, Materials & Continua, 74(2).
[29] Hassan, I. H., Abdullahi, M., Aliyu, M. M., Yusuf, S. A., & Abdulrahim, A. (2022). An improved binary manta ray foraging optimization algorithm based feature selection and random forest classifier for network intrusion detection. Intelligent Systems with Applications, 16, 200114.
[30] Kareem, S. S., Mostafa, R. R., Hashim, F. A., & El-Bakry, H. M. (2022). An effective feature selection model using hybrid metaheuristic algorithms for iot intrusion detection. Sensors, 22(4), 1396.
[31] Subramani, S., & Selvi, M. (2023). Multi-objective PSO based feature selection for intrusion detection in IoT based wireless sensor networks. Optik, 273, 170419.
[32] Kamalesh, S., & Muthukrishnan, A. (2023). Optimized dictionary-based sparse regression learning for health care monitoring in IoT-based context-aware architecture. IETE Journal of Research, 1-16.
[33] Ebrahimpour, M. K., & Eftekhari, M. (2017). Ensemble of feature selection methods: A hesitant fuzzy sets approach. Applied Soft Computing, 50, 300-312.
[34] Zarshenas, A., & Suzuki, K. (2016). Binary coordinate ascent: An efficient optimization technique for feature subset selection for machine learning. Knowledge-Based Systems, 110, 191-201.
[35] Han, C., Zhou, G., & Zhou, Y. (2019). Binary symbiotic organism search algorithm for feature selection and analysis. IEEE Access, 7, 166833-166859.
[36] Islam, M. J., Li, X., & Mei, Y. (2017). A time-varying transfer function for balancing the exploration and exploitation ability of a binary PSO. Applied Soft Computing, 59, 182-196.
[37] Yi, J. H., Wang, J., & Wang, G. G. (2016). Improved probabilistic neural networks with self-adaptive strategies for transformer fault diagnosis problem. Advances in Mechanical Engineering, 8(1), 1687814015624832.
[38] Mirjalili, S., & Lewis, A. (2013). S-shaped versus V-shaped transfer functions for binary particle swarm optimization. Swarm and Evolutionary Computation, 9, 1-14.
[39] Faraoun, K. M., & Boukelif, A. (2006). Neural networks learning improvement using the K-means clustering algorithm to detect network intrusions. INFOCOMP Journal of Computer Science, 5(3), 28-36.
[40] Yang, L., Moubayed, A., & Shami, A. (2021). MTH-IDS: A multitiered hybrid intrusion detection system for internet of vehicles. IEEE Internet of Things Journal, 9(1), 616-632.
[41] Na, S., Xumin, L., & Yong, G. (2010, April). Research on k-means clustering algorithm: An improved k-means clustering algorithm. In 2010 Third International Symposium on intelligent information technology and security informatics (pp. 63-67). Ieee.
[42] Moubayed, A., Injadat, M., Shami, A., & Lutfiyya, H. (2018, December). Dns typo-squatting domain detection: A data analytics & machine learning based approach. In 2018 IEEE Global Communications Conference (GLOBECOM) (pp. 1-7). IEEE.
[43] Moubayed, A., Injadat, M., Shami, A., & Lutfiyya, H. (2020). Student engagement level in an e-learning environment: Clustering using k-means. American Journal of Distance Education, 34(2), 137-156.
[44] Chen, Z., Yan, Q., Han, H., Wang, S., Peng, L., Wang, L., & Yang, B. (2018). Machine learning based mobile malware detection using highly imbalanced network traffic. Information Sciences, 433, 346-364.
[45] Onah, J. O., Abdullahi, M., Hassan, I. H., & Al-Ghusham, A. (2021). Genetic Algorithm based feature selection and Naïve Bayes for anomaly detection in fog computing environment. Machine Learning with applications, 6, 100156.
[46] Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321-357.
[47] Mazini, M., Shirazi, B., & Mahdavi, I. (2019). Anomaly network-based intrusion detection system using a reliable hybrid artificial bee colony and AdaBoost algorithms. Journal of King Saud University-Computer and Information Sciences, 31(4), 541-553.
[48] Talita, A. S., Nataza, O. S., & Rustam, Z. (2021, February). Naïve bayes classifier and particle swarm optimization feature selection method for classifying intrusion detection system dataset. In Journal of physics: conference series (Vol. 1752, No. 1, p. 012021). IOP Publishing.
