Enhanced AIS Based Intrusion Detection System Using Natural Killer Cells

Keywords: Intrusion, IDS, AIS, anomaly detection, natural killer cells


Intrusion detection system is used to monitor the system and network activities to identify anomalies and attacks so that integrity, availability, and confidentiality can be preserved. Here an intrusion detection system based on Artificial Immune System is proposed based on Natural Killer (NK) cells with immunological memory. NK cells are created and each NK cells detection radius is determined using the negative selection algorithm and is trained to detect various attacks. Effective cells with high fairness values are proliferated and distributed to the network using clonal selection algorithm. In this paper, two types of NK cell are used-a Heavyweight NK cell (HWNK) and a number of Lightweight NK cells (LWNK). The incoming data is vectorized and Major Histocompatibility Complex Class I (MHC1) is created. Then based on this MHC1, any of the receptors i.e. Activating Receptor or Inhibiting Receptor is activated. If it is the signature of an attack, Activating Receptor is activated. Activating receptor activation results in either cytokine release or apoptosis. Here cytokine release means an alarm is generated informing the administrator and apoptosis stands for dropping of the packet. If Inhibiting Receptor is activated, it’s a normal packet there is no action taken. The technique proposed yields high accuracy, better detection rate and quick response time.


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Author Biographies

B. J. Bejoy, Department of CSE, Christ (Deemed to be University), India

B. J. Bejoy is currently working as an Assistant Professor in the Department of Computer Science and Engineering at CHRIST (Deemed to be University) Bangalore. He completed his Ph.D in Banking Technology (An interdisciplinary in CSE and Banking) in thesis titled “Co-operative framework for distributed intrusion detection using Artificial Immune System” from Pondicherry University in 2019. He completed his ME in Computer Science and Engineering and BTech in Information Technology from Anna University Chennai in 2008 and 2006 respectively. He is a Life Member of ISTE. He has thirteen years of teaching and research experience. His current research areas include Artificial Immune System, Intrusion Detection System, Wireless Sensor Networks, Hardware Trojans Detection, Big Data Analytics and Software Defined Networking.

S. Janakiraman, Department of Banking Technology, Pondicherry University, India

S. Janakiraman received his Ph.D. (Computer Science and Engineering) degree from the Faculty of Information and Communication Engineering, Anna University, Chennai, Tamilnadu, India in the year 2010. He has obtained both of his Post Graduate degree, M.E. (Computer Science and Engineering) and Graduate degree B.E., (Electrical and Electronics Engineering) from Madurai Kamaraj University, Madurai, Tamilnadu, India. He is currently serving as Assistant Professor, Department of Banking Technology at Pondicherry University, Pondicherry. He has nineteen years of teaching and research experience. He is a Life Member of ISTE, Institution of Engineers (India). He is a reviewer for reputed journals publications which includes IEEE, IET, Elsevier publications. He is serving as a programme committee member and advisory committee member in international/national conferences like IEEE, Springer conferences. His area of research interest is Machine Learning and pattern recognition, Big Data Analytics, Banking Technology, Computer Networks, Security, and Image Processing. He has published more than 36 papers in international journals and presented 44 papers in international and national conferences.


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