International Journal of Drug Delivery Technology
Volume 16, Issue 3s

An Efficient Machine Learning Based Approach for Early Detection of Network Intrusion Attacks

Dr. Dharma Teja Lanka1, Dr. Seetha Jayaraman2, Ganesan M3, Anantharamaiah Vengala4, Dr. Sreenivas Mekala5, Dr. Goteti Chaitanya6

1Assistant Professor, Department of Electronics and Communication Engineering, VNR Vignana Jyothi Institute of Engineering & Technology, Hyderabad, Telangana, INDIA - 500118

ORCID ID: 0000-0001-8490-3431
2Professor, Department of Computer Science and Business Systems, Panimalar Engineering College, Chennai, India

https://orcid.org/0000-0002-9606-1785
3Assistant Professor, Department of CSE-CYBER SECURITY, Easwari Engineering College, Chennai, INDIA-600089

ORCID iD: 0009-0009-6940-4138
4Assistant Professor, Department of CSE, KL University (KLEF), Vaddeswaram, Andhra Pradesh, INDIA - 522502
5Associate Professor, Department of CSE, School of Agricultural Engineering & Technology, Kaveri University, Gowraram(v), Wargal(M), Siddipet(D), Telangana, INDIA - 502279

Orcid-id: 0000-0002-9708-2027
6B. Tech, M. E, Ph.D. Associate Professor, Department of Mechanical Engineering, R.V.R and J.C College of Engineering, Guntur, A.P, India

ABSTRACT

With the rapid growth of computer networks, intrusion detection has become a critical challenge in ensuring network security. This paper proposes an efficient machine learning-based intrusion detection system using supervised learning algorithms. Publicly available benchmark datasets were utilized to train and test the proposed model. Feature selection techniques were applied to improve classification accuracy and reduce computational complexity. Experimental results demonstrate that the proposed approach achieves high detection accuracy and reduced false alarm rates compared to traditional methods. The study highlights the effectiveness of machine learning techniques in enhancing network security systems.

Keywords: Anomaly Detection, Feature Selection, Intrusion Detection System, Machine Learning, Network Security

How to cite this article: Lanka DT, Jayaraman S, Ganesan M, Vengala A, Mekala S, Chaitanya G, An Efficient Machine Learning Based Approach for Early Detection of Network Intrusion Attacks. Int J Drug Deliv Technol. 2026;16(3s): 229-235; DOI: 10.25258/ijddt.16.3s.30