Enhancing Intrusion Detection Systems Using Metaheuristic Algorithms
Keywords:
Intrusion Detection System; metaheuristic algorithms; Lion Optimization Algorithm ; Grey Wolf Optimization; Hyperparameter; Feature Selection; Deep LearningAbstract
In the current network security framework, Intrusion Detection Systems (IDSs) happen to be among the major players in ensuring that the network activity is being monitored round the clock for any intrusions which may occur. The rising degree of cyber threats’ intricacy enforces the constant development of IDS methodologies to maintain effectiveness in detecting and reversing the emergence of any extra risks. Therefore, to settle the matter featured by, this research studies try to incorporate the most powerful metaheuristic algorithms, Lion Optimization Algorithm (LOA) and Grey Wolf Optimizer (GWO) in particular, to develop better detection accuracy and efficiency. The core obstacle recognized in this article is the fact that many systems of IDS send out false alarms and their mechanisms of detection of the true anomalies need to be improved immensely. In a nutshell, the change would unveil a fresh way of using LOA and GWO using them to promote the enhancement of internet defences systems in real-time. These schemes can discover previously unknown weaknesses or stealthy attacks. The core of this undertaking would consist in the conception and implementing of a Hybrid Network Intrusion Detection System, which will be created by blending the Lion Optimization Feature Selection (LOFS) and GWO smelters, denoted as LOFSGWO. Critically, the main purpose is to incorporate the GWO as a tool in the operations to cut down the dangerous parameters favourable towards an intrusion mechanism in the framework of a Hybrid CNN-LSTM Deep Learning system. Model tests reveal over 99.26% accuracy of low negative samples into out of a box that are served as testing as well as NSL-KDD dataset, which are similar to the simulation of WUSTL-EOM 2020 system. The obtained outcomes verify the relevance and efficiency of the suggested strategy, which may be used in the resolution of the issues faced in a network security today.
Downloads
References
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, Feb. 2023.
M. B. Pranto, M. H. A. Ratul, M. M. Rahman, I. J. Diya, and Z. Bin Zahir, “Performance of Machine Learning Techniques in Anomaly Detection with Basic Feature Selection Strategy-A Network Intrusion Detection System,” Journal of Advances in Information Technology, vol. 13, no. 1, 2022.
I. Katib and M. Ragab, “Blockchain-Assisted Hybrid Harris Hawks Optimization Based Deep DDoS Attack Detection in the IoT Environment,” Mathematics, vol. 11, no. 8, p. 1887, Apr. 2023.
M. Moizuddin and M. V. Jose, “A bio-inspired hybrid deep learning model for network intrusion detection,” Knowledge-Based Systems, vol. 238, p. 107894, Feb. 2022.
M. Ahsan, R. Gomes, M. M. Chowdhury, and K. E. Nygard, “Enhancing Machine Learning Prediction in Cybersecurity Using Dynamic Feature Selector,” Journal of Cybersecurity and Privacy, vol. 1, no. 1, 2021.
M. A. Talukder et al., “A dependable hybrid machine learning model for network intrusion detection,” Journal of Information Security and Applications, vol. 72, 2023.
G. Sah, S. Banerjee, and S. Singh, “Intrusion detection system over real-time data traffic using machine learning methods with feature selection approaches,” International Journal of Information Security, vol. 22, no. 1, pp. 1–27, Oct. 2022.
M. Maabreh, I. Obeidat, E. A. Elsoud, A. Alnajjai, R. Alzyoud, and O. Darwish, “Towards Data-Driven Network Intrusion Detection Systems: Features Dimensionality Reduction and Machine Learning,” International Journal of Interactive Mobile Technologies, vol. 16, no. 14, 2022.
M. Ragab, S. M. Alshammari, and A. S. Al-Malaise Al-Ghamdi, “Modified Metaheuristics with Weighted Majority Voting Ensemble Deep Learning Model for Intrusion Detection System,” Computer Systems Science and Engineering, vol. 47, no. 2, 2023.
M. Ragab and M. Farouk S. Sabir, “Outlier detection with optimal hybrid deep learning enabled intrusion detection system for ubiquitous and smart environment,” Sustainable Energy Technologies and Assessments, vol. 52, 2022.
G. Kocher and G. Kumar, “Analysis of Machine Learning Algorithms with Feature Selection for Intrusion Detection using UNSW-NB15 Dataset,” International Journal of Network Security & Its Applications, vol. 13, no. 1, pp. 21–31, Jan. 2021.
B. Sharma, L. Sharma, C. Lal, and S. Roy, “Anomaly based network intrusion detection for IoT attacks using deep learning technique,” Computers and Electrical Engineering, vol. 107, p. 108626, Apr. 2023.
M. Mohy-eddine, A. Guezzaz, S. Benkirane, and M. Azrour, “An efficient network intrusion detection model for IoT security using K-NN classifier and feature selection,” Multimedia Tools and Applications, vol. 82, no. 15, pp. 23615–23633, Feb. 2023.
S. Hosseini and S. R. Sardo, “Network intrusion detection based on deep learning method in internet of thing,” Journal of Reliable Intelligent Environments, vol. 9, no. 2, pp. 147–159, Feb. 2022.
N. F. Syed, M. Ge, and Z. Baig, “Fog-cloud based intrusion detection system using Recurrent Neural Networks and feature selection for IoT networks,” Computer Networks, vol. 225, 2023.
M. L. Hernandez-Jaimes, A. Martinez-Cruz, K. A. Ramírez-Gutiérrez, and C. Feregrino-Uribe, “Artificial intelligence for IoMT security: A review of intrusion detection systems, attacks, datasets and Cloud–Fog–Edge architectures,” Internet of Things (Netherlands), vol. 23. 2023.
C. Chen, Y. Gao, S. Huang, and X. Yan, “Avoid attacks: A Federated Data Sanitization Defense in IoMT Systems,” IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), May 2023.
A. Binbusayyis, H. Alaskar, T. Vaiyapuri, and M. Dinesh, “An investigation and comparison of machine learning approaches for intrusion detection in IoMT network,” The Journal of Supercomputing, vol. 78, no. 15, pp. 17403–17422, May 2022.
Y. K. Saheed and M. O. Arowolo, “Efficient Cyber Attack Detection on the Internet of Medical Things-Smart Environment Based on Deep Recurrent Neural Network and Machine Learning Algorithms,” IEEE Access, vol. 9, 2021.
Y. Manchala, J. Nayak, and H. S. Behera, “Detection of Malicious Traffic in IoMT Environment Using Intelligent XGboost Approach,” 2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development (OTCON), Feb. 2023.
J. Jithish, S. Sankaran, and K. Achuthan, “A Hybrid Machine Learning Approach for Intrusion Detection in Cyber-Physical Manufacturing Systems,” Intelligent Security Solutions for Cyber-Physical Systems, pp. 156–168, Mar. 2024.
E. M. Maseno and Z. Wang, “Hybrid wrapper feature selection method based on genetic algorithm and extreme learning machine for intrusion detection,” Journal of Big Data, vol. 11, no. 1, Feb. 2024.
A. Hady, A. Ghubaish, T. Salman, D. Unal, and R. Jain, “Intrusion Detection System for Healthcare Systems Using Medical and Network Data: A Comparison Study,” IEEE Access, vol. 8, pp. 106576–106584, 2020.
M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, “A detailed analysis of the KDD CUP 99 data set,” in IEEE Symposium on Computational Intelligence for Security and Defense Applications, CISDA 2009, 2009.
A. Ghubaish, “WUSTL EHMS 2020 Dataset for Internet of Medical Things (IoMT) Cybersecurity Research,” Wustl.edu, 2020. https://www.cse.wustl.edu/~jain/ehms/index.html
“NSL-KDD | Datasets | Research | Canadian Institute for Cybersecurity | UNB,” www.unb.ca. https://www.unb.ca/cic/datasets/nsl.html
J. Mchugh, “Testing Intrusion Detection Systems: A Critique of the 1998 and 1999 DARPA Intrusion Detection System Evaluations as Performed by Lincoln Laboratory,” ACM Transactions on Information and System Security, vol. 3, no. 4, 2000.
M. Yazdani and F. Jolai, “Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm,” Journal of Computational Design and Engineering, vol. 3, no. 1, pp. 24–36, Jun. 2015.
S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Advances in Engineering Software, vol. 69, 2014.
H. M. Fadhil, M. N. Abdullah, and M. I. Younis, “TWGH: A Tripartite Whale–Gray Wolf–Harmony Algorithm to Minimize Combinatorial Test Suite Problem,” Electronics, vol. 11, no. 18, p. 2885, Sep. 2022.
H. M. Fadhil, N. Q. Makhool, M. M. Hummady, and Z. O. Dawood, “Machine Learning-based Information Security Model for Botnet Detection,” Journal of Cybersecurity and Information Management (JCIM), vol. 9, no. 1, pp. 68–79, 2022.
A. Wang, W. Wang, H. Zhou, and J. Zhang, “Network Intrusion Detection Algorithm Combined with Group Convolution Network and Snapshot Ensemble,” Symmetry, vol. 13, no. 10, pp. 1814, Sep. 2021.
S. Priya and K. P. M. Kumar, “Binary bat algorithm-based feature selection with deep reinforcement learning technique for intrusion detection system,” Soft Computing, vol. 27, no. 15, pp. 10777–10788, Jun. 2023.
Published
How to Cite
Issue
Section
Copyright (c) 2024 Heba Mohammed Fadhil, Zinah Osamah Dawood, Ammar Al Mhdawi
This work is licensed under a Creative Commons Attribution 4.0 International License.
