Enhancing Network Security through Machine Learning Based Intrusion Detection Systems
Keywords:
Network security, Intrusion detection systems, Machine learning, Cyber threats, Network architecturesAbstract
The increasing complexity and sophistication of cyber threats have necessitated the development of robust and intelligent security mechanisms to safeguard network infrastructures. In recent years, machine learning (ML) techniques have emerged as a powerful tool for enhancing network security, particularly in the realm of intrusion detection systems (IDS). This research paper explores the application of machine learning algorithms in the context of IDS to enhance network security. It investigates various ML techniques, their benefits, and challenges, and provides insights into the integration of ML-based IDS in modern network architectures. The study also highlights the potential limitations and future research directions in this evolving field.
Downloads
References
Alazab, M., Hobbs, M., Abawajy, J., & Alazab, M. (2019). Machine learning-based intrusion detection systems: A comprehensive survey. Computers & Security, 78, 398-422.
Kolias, C., Kambourakis, G., Stavrou, A., & Voas, J. (2017). Intrusion detection in 21st century: A survey. Journal of Network and Computer Applications, 75, 1-18.
Xu, Z., & Zhang, G. (2020). Deep learning-based network intrusion detection: A comprehensive review. IEEE Access, 8, 165900-165917.
Garcia-Teodoro, P., Diaz-Verdejo, J., Maciá-Fernández, G., & Vázquez, E. (2009). Anomaly-based network intrusion detection: Techniques, systems and challenges. Computers & Security, 28(1-2), 18-28.
Kaushik, K., Garg, M., Annu, Gupta, A. and Pramanik, S. (2021). Application of Machine Learning and Deep Learning in Cyber security: An Innovative Approach, in Cybersecurity and Digital Forensics: Challenges and Future Trends, M. Ghonge, S. Pramanik, R. Mangrulkar and D. N. Le, Eds, Wiley, 2021.
Pandey, B.K. et al. (2022). Effective and Secure Transmission of Health Information Using Advanced Morphological Component Analysis and Image Hiding. In: Gupta, M., Ghatak, S., Gupta, A., Mukherjee, A.L. (eds) Artificial Intelligence on Medical Data. Lecture Notes in Computational Vision and Biomechanics, vol 37. Springer, Singapore.https://doi.org/10.1007/978-981-19-0151-5_19
Pathania, V. et al. (2022). A Database Application of Monitoring COVID-19 in India. In: Gupta, M., Ghatak, S., Gupta, A., Mukherjee, A.L. (eds) Artificial Intelligence on Medical Data. Lecture Notes in Computational Vision and Biomechanics, vol 37. Springer, Singapore. https://doi.org/10.1007/978-981-19-0151-5_23
Idrees, S., Raza, S., Bakar, K. A., & Ahmed, M. A. (2020). Machine learning-based network intrusion detection systems: A survey. Journal of Network and Computer Applications, 166, 102757.
Puzis, R., Barseghyan, A., Shabtai, A., & Elovici, Y. (2011). Improving network security via combined intrusion detection and prevention systems. IEEE Transactions on Dependable and Secure Computing, 8(6), 826-838.
Kim, K., & Feamster, N. (2013). Improving network security via proactive intrusion detection. IEEE/ACM Transactions on Networking, 21(5), 1412-1425.
Sharafaldin, I., Lashkari, A. H., & Ghorbani, A. A. (2018). Toward generating a new intrusion detection dataset and intrusion traffic characterization. In Proceedings of the 4th International Conference on Information Systems Security and Privacy (ICISSP), 108-116.
Wang, J., Zhang, J., Hu, C., & Chen, X. (2020). Network intrusion detection using machine learning: A systematic review. Future Generation Computer Systems, 102, 798-808.
Moustafa, N., & Slay, J. (2016). The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 dataset and the comparison with the KDD99 dataset. Information Security Journal: A Global Perspective, 25(1-3), 18-31.
E. Brynjolfsson, T. Mitchell, What can machine learning do? Workforce implications. Science 358(6370), 1530–1534 (2017)
Y. Xin, L. Kong, Z. Liu, Y. Chen, Y. Li, H. Zhu, M. Gao, H. Hou, C. Wang, Machine learning and deep learning methods for cybersecurity. IEEE Access 6, 35365–35381 (2018)
R. Boutaba, M.A. Salahuddin, N. Limam, S. Ayoubi, N. Shahriar, F. Estrada-Solano, O.M. Caicedo, A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities. J. Int. Serv. Appl. 9(1), 16 (2018)
S. Mohammadi, H. Mirvaziri, M. Ghazizadeh-Ahsaee, H. Karimipour, Cyber intrusion detection by combined feature selection algorithm. J. Inf. Secur. Appl. 44, 80–88 (2019)
H. Wang et al.An effective intrusion detection framework based on svm with feature augmentation Knowl. Based Syst.(2017)
XuC. et al.An intrusion detection system using a deep neural network with gated recurrent units, IEEE Access.(2018)
FujitaH. et al.Resilience analysis of critical infrastructures: a cognitive approach based on granular computing, IEEE Trans. Cybern.(2019)
Sasikala P, Sushil Dohare, Mohammed Saleh Al Ansari, Janjhyam Venkata Naga Ramesh, Yousef A.Baker El-Ebiary and E. Thenmozhi, “A Hybrid GAN-BiGRU Model Enhanced by African Buffalo Optimization for Diabetic Retinopathy Detection” International Journal of Advanced Computer Science and Applications(IJACSA), 15(1), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150197
Ramesh, J. V. N., Patibandla, P. R., Shanbhog, M., Ambala, S., Ashraf, M., & Kiran, A. (2023). Ensemble Deep Learning Approach for Turbidity Prediction of Dooskal Lake Using Remote Sensing Data. Remote Sensing in Earth Systems Sciences, 1-10.
Kiran, A., Kalpana, V., Madanan, M., Ramesh, J. V. N., Alfurhood, B. S., & Mubeen, S. (2023). Anticipating network failures and congestion in optical networks a data analytics approach using genetic algorithm optimization. Optical and Quantum Electronics, 55(13), 1193.
Pandiaraj, S., Krishnamoorthy, R., Ushasukhanya, S., Ramesh, J. V. N., Alsowail, R. A., & Selvarajan, S. (2023). Optimization of IoT circuit for flexible optical network system with high speed utilization. Optical and Quantum Electronics, 55(13), 1206.
Ramesh, P. S., Vanteru, M. K., Rajinikanth, E., Ramesh, J. V. N., Bhasker, B., & Begum, A. Y. (2023). Design and optimization of feedback controllers for motion control in the manufacturing system for digital twin. SN Computer Science, 4(6), 782.
Ashok, K., Chaturvedi, A., Agarkar, A. A., Ashraf, M., Ramesh, J. V. N., & Ambala, S. (2023). Fuzzy logic and cooperative hybrid least squares for improving network capacity and connectivity in high-density wireless networks. Optical and Quantum Electronics, 55(12), 1042.
V. Talukdar, D. Dhabliya, B. Kumar, S. B. Talukdar, S. Ahamad and A. Gupta, "Suspicious Activity Detection and Classification in IoT Environment Using Machine Learning Approach," 2022 Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC), Solan, Himachal Pradesh, India, 2022, pp. 531-535, doi: 10.1109/PDGC56933.2022.10053312.
P. R. Kshirsagar, D. H. Reddy, M. Dhingra, D. Dhabliya and A. Gupta, "A Scalable Platform to Collect, Store, Visualize and Analyze Big Data in Real- Time," 2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM), Uttar Pradesh, India, 2023, pp. 1-6, doi: 10.1109/ICIPTM57143.2023.10118183.
M. Dhingra, D. Dhabliya, M. K. Dubey, A. Gupta and D. H. Reddy, "A Review on Comparison of Machine Learning Algorithms for Text Classification," 2022 5th International Conference on Contemporary Computing and Informatics (IC3I), Uttar Pradesh, India, 2022, pp. 1818-1823, doi: 10.1109/IC3I56241.2022.10072502.
D. Mandal, A. Shukla, A. Ghosh, A. Gupta and D. Dhabliya, "Molecular Dynamics Simulation for Serial and Parallel Computation Using Leaf Frog Algorithm," 2022 Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC), Solan, Himachal Pradesh, India, 2022, pp. 552-557, doi: 10.1109/PDGC56933.2022.10053161.
P. R. Kshirsagar, D. H. Reddy, M. Dhingra, D. Dhabliya and A. Gupta, "A Review on Application of Deep Learning in Natural Language Processing," 2022 5th International Conference on Contemporary Computing and Informatics (IC3I), Uttar Pradesh, India, 2022, pp. 1834-1840, doi: 10.1109/IC3I56241.2022.10073309.
P. R. Kshirsagar, D. H. Reddy, M. Dhingra, D. Dhabliya and A. Gupta, "Detection of Liver Disease Using Machine Learning Approach," 2022 5th International Conference on Contemporary Computing and Informatics (IC3I), Uttar Pradesh, India, 2022, pp. 1824-1829, doi: 10.1109/IC3I56241.2022.10073425.
V. V. Chellam, S. Praveenkumar, S. B. Talukdar, V. Talukdar, S. K. Jain and A. Gupta, "Development of a Blockchain-based Platform to Simplify the Sharing of Patient Data," 2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM), Uttar Pradesh, India, 2023, pp. 1-6, doi: 10.1109/ICIPTM57143.2023.10118194.
Lalitha Kumari, P. et al. (2023). Methodology for Classifying Objects in High-Resolution Optical Images Using Deep Learning Techniques. In: Chakravarthy, V., Bhateja, V., Flores Fuentes, W., Anguera, J., Vasavi, K.P. (eds) Advances in Signal Processing, Embedded Systems and IoT . Lecture Notes in Electrical Engineering, vol 992. Springer, Singapore. https://doi.org/10.1007/978-981-19-8865-3_55
Sindhwani, N. et al. (2023). Comparative Analysis of Optimization Algorithms for Antenna Selection in MIMO Systems. In: Chakravarthy, V., Bhateja, V., Flores Fuentes, W., Anguera, J., Vasavi, K.P. (eds) Advances in Signal Processing, Embedded Systems and IoT . Lecture Notes in Electrical Engineering, vol 992. Springer, Singapore. https://doi.org/10.1007/978-981-19-8865-3_54
Di Pietro R, Mancini LV. Intrusion detection systems (Vol. 38). New York: Springer Science & Business Media; 2008.
Ring M, Wunderlich S, Scheuring D, et al. A survey of network-based intrusion detection data sets. Comput Secur. 2019;86:147–167.
Taheri R, Ghahramani M, Javidan R, et al. Similarity-based Android malware detection using hamming distance of static binary features. Future Gener Comput Syst. 2020;105:230–247.
Sushil Dohare, Deeba K, Laxmi Pamulaparthy, Shokhjakhon Abdufattokhov, Janjhyam Venkata Naga Ramesh, Yousef A.Baker El-Ebiary and E. Thenmozhi, “Enhancing Diabetes Management: A Hybrid Adaptive Machine Learning Approach for Intelligent Patient Monitoring in e-Health Systems” International Journal of Advanced Computer Science and Applications(IJACSA), 15(1), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150162
Artika Farhana, Nimmati Satheesh, Ramya M, Janjhyam Venkata Naga Ramesh and Yousef A. Baker El-Ebiary, “Efficient Deep Reinforcement Learning for Smart Buildings: Integrating Energy Storage Systems Through Advanced Energy Management Strategies” International Journal of Advanced Computer Science and Applications(IJACSA), 14(12), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0141257
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
