The rapid growth of cloud-based and large-scale network infrastructures has increased the complexity and frequency of cyber-attacks, demanding efficient and scalable intrusion detection systems (IDS). This paper will also attempt to create a better Network Intrusion Detection System (NIDS) by incorporating a Hybrid Median Attribute Clustering model with Particle Swarm Optimization (MAHC-PSO) to achieve better detection accuracy, less false alarms, and better computation efficiency in high dimensional network space. The suggested MAHC-PSO model utilizes Information Gain in the feature selection on the KDD Cup’99 dataset to minimize dimensions without losing important network features. The median attribute analysis is used to make data representation in a way that is effective and hybrid hierarchical clustering is used to cluster network traffic patterns. Clustering quality is optimized with the help of particle Swarm Optimization that improves the position of the particles depending on the accuracy of detection and the degree of compactness of the cluster. The performance assessment is done with different network sizes between 10, 000 and 60,000 nodes and compared with the current NWFSF-IDMLM and HO-CNN-LSTM-IDS models. The experimental findings indicate that MAHC-PSO model is always better in all the metrics than the benchmark models. The accuracy of feature extraction, hybrid clustering, and cluster set generation were 98.5%, 98.2% and 98.6%, respectively, with 60,000 nodes. PSO fitness value estimation rate was at its highest of 98.8 and median attribute estimation time was lower, at 18 units and pattern analysis time was lower, at 12 units. The overall intrusion detection accuracy was 98.6 which is very high compared to models. The MAHC-PSO model provides a scalable, efficient, and powerful intrusion detection system in large networks in real-time. Its superior accuracy and reduced processing time make it suitable for deployment in cloud-based, enterprise, and future distributed IT environments.
Han Y, Wang Y, Cao Y, Geng Z, Zhu Q. A Novel Wrapped Feature Selection Framework for Developing Power System Intrusion Detection Based on Machine Learning Methods. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2023;53(11):7066–76.
2.
Deore B, Bhosale S. Hybrid Optimization Enabled Robust CNN-LSTM Technique for Network Intrusion Detection. IEEE Access. 2022;10:65611–22.
3.
Alsaleh A, Binsaeedan W. The Influence of Salp Swarm Algorithm-Based Feature Selection on Network Anomaly Intrusion Detection. IEEE Access. 2021;9:112466–77.
4.
Zhiheng W, Jianhua L. Flamingo Search Algorithm: A New Swarm Intelligence Optimization Algorithm. IEEE Access. 2021;9:88564–82.
5.
Tang Y, Li C. An Online Network Intrusion Detection Model Based on Improved Regularized Extreme Learning Machine. IEEE Access. 2021;9:94826–44.
6.
Injadat M, Moubayed A, Nassif AB, Shami A. Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection. IEEE Transactions on Network and Service Management. 2021;18(2):1803–16.
7.
Donkol AAEB, Hafez AG, Hussein AI, Mabrook MM. Optimization of Intrusion Detection Using Likely Point PSO and Enhanced LSTM-RNN Hybrid Technique in Communication Networks. IEEE Access. 2023;11:9469–82.
8.
Wu Y, Lee WW, Xu Z, Ni M. Large-Scale and Robust Intrusion Detection Model Combining Improved Deep Belief Network With Feature-Weighted SVM. IEEE Access. 2020;8:98600–11.
9.
Salinas O, Soto R, Crawford B, Olivares R. An Integral Cybersecurity Approach Using a Many-Objective Optimization Strategy. IEEE Access. 2023;11:91913–36.
10.
Taher F, Elhoseny M, Hassan MK, El-Hasnony IM. A Novel Tunicate Swarm Algorithm With Hybrid Deep Learning Enabled Attack Detection for Secure IoT Environment. IEEE Access. 2022;10:127192–204.
11.
Ibrahim MS, Dong W, Yang Q. Machine learning driven smart electric power systems: Current trends and new perspectives. Applied Energy. 2020;272:115237.
12.
Sumaiya Thaseen I, Saira Banu J, Lavanya K, Rukunuddin Ghalib M, Abhishek K. An integrated intrusion detection system using correlation‐based attribute selection and artificial neural network. Transactions on Emerging Telecommunications Technologies. 2020;32(2).
13.
Elmasry W, Akbulut A, Zaim AH. Evolving deep learning architectures for network intrusion detection using a double PSO metaheuristic. Computer Networks. 2020;168:107042.
14.
Wang H, Cao Z, Hong B. A network intrusion detection system based on convolutional neural network. Journal of Intelligent & Fuzzy Systems. 2020;38(6):7623–37.
15.
Ferrag MA, Maglaras L, Moschoyiannis S, Janicke H. Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study. Journal of Information Security and Applications. 2020;50:102419.
16.
Al-Jame F, Ali. Finite Element Evaluation of Mechanical Performance of Hybrid Composite Aerospace Structures. Journal of Reconfigurable Hardware Architectures and Embedded Systems. 2024;12–7.
17.
Jiang H, He Z, Ye G, Zhang H. Network Intrusion Detection Based on PSO-Xgboost Model. IEEE Access. 2020;8:58392–401.
18.
Bovenzi G, Aceto G, Ciuonzo D, Persico V, Pescape A. A Hierarchical Hybrid Intrusion Detection Approach in IoT Scenarios. GLOBECOM 2020 - 2020 IEEE Global Communications Conference. IEEE; 2020. p. 1–7.
19.
Shyla S, Bhatnagar V, Bali V, Bali S. Optimization of Intrusion Detection Systems Determined by Ameliorated HNADAM-SGD Algorithm. Electronics. 2022;11(4):507.
20.
Powers D. Evaluation: from Precision, Recall and F-Measure to ROC.
21.
Khraisat A, Alazab A. A critical review of intrusion detection systems in the internet of things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges. Cybersecurity. 2021;4(1).
22.
Ulkilan A. Optimization of Power Flow in Hybrid Microgrids Using AI-Based Algorithms. Journal of Scalable Data Engineering and Intelligent Computing. 2024;(1):41–7.
23.
Chaganti R, Varadarajan V, Gorantla VS, Gadekallu TR, Ravi V. Blockchain-Based Cloud-Enabled Security Monitoring Using Internet of Things in Smart Agriculture. Future Internet. 2022;14(9):250.
24.
Rahman F. Artificial Intelligence-Driven Cybersecurity Framework for Industrial Control Networks. Transactions on Secure Communication Networks and Protocol Engineering. 2025;(1):1–8.
25.
Li M, Liu Y, Tian Z, Shan C. Privacy Protection Method Based On Multidimensional Feature Fusion Under 6G Networks. IEEE Transactions on Network Science and Engineering. 2023;10(3):1462–71.
The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.
,
