The week's pick
Random Articles
Reseach Article
Exploiting Machine Learning Techniques for Proactive Detection and Prevention of Network Intrusions
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 54 |
| Year of Publication: 2025 |
| Authors: Puspraj Kumar Saket, Md. Vaseem Naiyer, Ankit Temurnikar |
10.5120/ijca2025925927
|
Puspraj Kumar Saket, Md. Vaseem Naiyer, Ankit Temurnikar . Exploiting Machine Learning Techniques for Proactive Detection and Prevention of Network Intrusions. International Journal of Computer Applications. 187, 54 ( Nov 2025), 49-59. DOI=10.5120/ijca2025925927
Abstract
Traditional intrusion detection systems (IDS) are sometimes insufficient when it comes to spotting sophisticated and ever-evolving attack patterns. This is due to the exponential growth in cyber threats as well as the rising complexity of modern networks. An investigation into the application of sophisticated machine learning (ML) methods for the purpose of enabling proactive detection and prevention of network intrusions is presented in this study. The purpose of this research is to improve the accuracy of anomaly detection, decrease the number of false positives, and respond more quickly to threats in real time. This will be accomplished by utilizing supervised, unsupervised, and deep learning models. A detailed study is carried out by utilizing benchmark datasets such as NSL-KDD and CICIDS. This analysis evaluates the performance of several methods, such as Random Forest, Support Vector Machines (SVM), K-Means Clustering, and Long Short-Term Memory (LSTM) networks. Through the identification of zero-day assaults and adaptive threat behaviors, the findings reveal that machine learning-driven intrusion detection systems (IDS) may vastly outperform traditional signature-based systems. In addition to this, the article explores the incorporation of these models into a real-time security framework in order to facilitate automated responses and improve the overall cybersecurity posture. The findings highlight the significant role that machine learning plays in the construction of network intrusion prevention systems that are intelligent, adaptable, and scalable for the future generation of digital networks.
References
- D.P. Gaikwad and Ravindra C. Thool. (2015). Intrusion detection system using bagging with partial decision tree base classifier. Procedia Computer Science 49 (pp. 92-98). Elsevier.)
- W. –C. Lin, Shih-Wen K. Chih-Fong T. (2015). Intrusion detection system based on combining cluster centers and nearest neighbors. Knowledge-Based Systems 78 (pp. 13-21). Elsevier.
- A.S.A. Aziz. (2016). Comparison of classification techniques applied for network intrusion detection and classification. Journal of Applied Logic 24. Elsevier, 109-118.
- Nabila Farnaaz and M.A Jabbar. (2016). Random Forest Modeling for Network Intrusion Detection System. International Multi-conference on information processing (IMCIP) 12 (pp. 213-217). Elsevier.
- Kayvan A. Saadiah Y. Amirali R. and Hazyanti S. (2016). Anomaly Detection Based on Profile Signature in Network using Machine Learning Techniques. IEEE TENSYMP. (pp. 71-76). IEEE.
- Bobba Brao and Kailasam Swathi. (2017). Fast KNN Classifiers for Network Intrusion Detection System. Indian Journal of Science and Technology. 10(14). Researchgate. (1-10).
- Chie-Hong L. Yann-Yean S. Yu-Chun Lin and Shie-Jue L. (2017). Machine Learning Based Network Intrusion Detection. 2nd IEEE International Conference on Computational Intelligence and Applications. (pp. 79-83). IEEE.
- Deyban P. Miguel A. A, David P. A, and Eugenio S. (2017). Intrusion detection in computer networks using hybrid machine learning techniques. XLIII Latin American Computer Conference (CLEI). (pp. 1-10). IEEE
- Alkasassbeh and Almseidin. (2018). Machine Learning Methods for Network Intrusions. International Confrernce on Computing, Communication (ICCCNT). Arxiv Marzia Z. and Chung-Horng L.(2018). Evaluation of Machine Learning Techniques for Network Intrusion Detection. IEEE. (pp. 1- 5)
- Dutt I. et al. (2018). Real Time Hybrid Intrusion Detection System. International Conference on Communication, Devices and Networking (ICCDN). (pp. 885-894). Springer.
- Verma P, Shadab K, Shayan A. and Sunil B. (2018). Network Intrusion Detection using Clustering and Gradient Boosting. International Conference on Computing, Communication and Networking Technologies (ICCCNT). (pp. 1-7). IEEE.
- Kazi A., Billal M. and Mahbubur R. (2019). Network Intrusion Detection using Supervised Machine Learning Technique with feature selection. International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST). (pp. 643-646). IEEE.
- Yuyang Z., Guang C., Shanqing J. and Mian D. (2019). Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier. Computer Networks. Doi: https://doi.org/10.1016/j.comnet.2020.107247
- Iqbal and Aftab. (2019). A Feed-Forward ANN and Pattern Recognition ANN Model for Network Intrusion Detection. International Journal of Computer Network and Information Security, 4. Researchgate (19-25)
- Vinoth Y. and Kamatchi K. (2020). Anomaly Based Network Intrusion Detection using Ensemble Machine Learning Technique. International Journal of Research in Engineering, Science and Management. IJRESM. (290-296).
- Bhavani T. T, Kameswara M. R and Manohar A. R. (2020). Network Intrusion Detection System using Random Forest and Decision Tree Machine Learning Techniques. International Conference on Sustainable Technologies for Computational Intelligence (ICSTCI). (pp. 637-643). Springer.
- Maniriho et al. (2020). Detecting Intrusions in Computer Network Traffic with Machine Learning Approaches. International Journal of Intelligent Engineering and Systems. INASS. (433-445)
- Ponthapalli R. et al. (2020). Implementation of Machine Learning Algorithms for Detection of Network Intrusion. International Journal of Computer Science Trends and Technology (IJCST). (163-169).
- Rajagopal S., Poornima P. K. and Katiganere S. H. (2020). A Stacking Ensemble for Network Intrusion Detection using Heterogeneous Datasets. Journal of Security and Communication Networks. Hindawi. (1-9).
- Bolon –C.V. (2012) Feature Selection and Classification in Multiple class datasets-An application to KDD Cup 99 dataset. https://doi.org/10.1016/j.eswa.2010.11.028
- ] Farah N. H et al. (2015). Application of Machine Learning Approaches in Intrusions Detection Systems. International Journal of Advanced Research in Artificial Intelligence. IJARAI. (9-18).
- N. F. Haq et al. (2015). An Ensemble framework for anomaly detection using hybridized feature selection approach (HFSA). Intelligent System Conference. (pp. 989-995). IEEE.
- S. Thapa and A.D Mailewa (2020). The Role of Intrusion Detection/Prevension Systems in Modern Computer Networks: A Review. Conference: Midwest Instruction and Computing Symposium (MICS). Wisconsin, USA. Volume: 53. (pp. 1-14).
- Nayana Vaity & Ankit Temurnikar, “Accurate EEG-based Emotion Detection using Feature Optimization and Machine Learning Algorithm”, Jan 2022.
- Ankit Temurnikar, Pushpneel Verma & Jaytrilok Choudhary, “Development of Multi-Hop Clustering Approach for Vehicular Ad-hoc Network”, Jan 2020.
- Ankit Temurnikar, Pushpneel Verma & Jaytrilok Choudhary, “Securing Vehicular Adhoc Network against Malicious Vehicles using Advanced Clustering Technique”, Feb 2020.
- Ankit Temurnikar & Sanjeev Sharma, “Secure and Stable Vehicular Ad hoc Network Architecture Model”, IJCSN Volume 2, 2013. Chapter, “Cyber Security for Secured Smart Home Applications Using Internet of Things, Dark Web, and Blockchain Technology in the Future”, Vinod Mahor et al., May 2022.
Index Terms
Keywords