A survey of intrusion detection from the perspective of intrusion datasets and machine learning techniques

ABSTRACT

The evolution in the attack scenarios has been such that finding efficient and optimal Network Intrusion Detection Systems (NIDS) with frequent updates has become a big challenge. NIDS implementation using machine learning (ML) techniques and updated intrusion datasets is one of the solutions for effective modeling of NIDS. This article presents a brief description of publicly available labeled intrusion datasets and ML techniques. Later a brief explanation of the literary works is given in which machine learning techniques are applied for NIDS implementation in different networking scenarios, such as traditional networks, cloud networks, Ad-Hoc, WSNs, and IoT networks. Hence, this article brings together publicly available intrusion datasets and machine learning techniques utilized in recent intrusion detection systems to reveal present-day challenges and future directions. This article also explains problems associated with NIDS. This will help researchers to enhance the existing NIDS models as well as to develop new effective models.

KEYWORDS:

• Intrusion detection
• anomaly
• intrusion dataset
• machine learning
• security

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Geeta Singh

Ms. Geeta Singh is an Assistant Professor and Ph.D. candidate in the School of Computer Science and Engineering at Vellore Institute of Technology, Vellore, Tamil Nadu, India. She received her master’s degree in Computer Science from Barkatullah University, Bhopal in 2006. She received her master’s degree in Software Systems from RGPV, Bhopal in 2014. Her research interest covers Information Security and Machine Learning Techniques.

Neelu Khare

Dr. Neelu Khare is presently working as Associate Professor in the School of Information Technology and Engineering at VIT University, Vellore, Tamil Nadu, India. She completed her Ph.D. degree from MANIT Bhopal, India. She has published 41 papers in International Journals and conferences. She guided 4 Ph.D. students. Her areas of interest are Data Mining: Association, Classification, Soft computing techniques, Security, Machine Learning, IoT, and Bio-informatics.