A survey on energy efficient intrusion detection in wireless sensor networks

Article type: Research Article

Authors: Ghosal, Amrita^{; *} | Halder, Subir

Affiliations: Department of Computer Science and Engineering, Dr. B. C. Roy Engineering College, Durgapur 713206, India

Correspondence: [*] Corresponding author. E-mail: ghosal_amrita@yahoo.com.

Abstract: Wireless sensor networks (WSNs) have emerged as an important research paradigm since the last decade, thereby motivating researches to take up new theoretical and practical challenges. WSNs need to be provided with efficient security features generally due to their deployment in inaccessible terrain and also communication being in the wireless domain. Therefore the question of providing security to such networks arises but the major constraints are the limited resources present in the sensor nodes. Prior importance is given to the energy parameter as it is the most vital component of the sensor nodes. So the objective of any intrusion detection framework would be to design robust mechanisms capable of handling attacks in energy efficient manner. Intrusion detection is used in WSNs because of their ability to detect unknown attacks and finding means to thwart them for preserving energy. Therefore energy efficient intrusion detection has become a significant research area for researchers. Keeping this in mind, we survey the major topics of energy efficient intrusion detection in WSNs. The survey work presents topics such as the fundamentals of intrusion detection techniques, as well as the various energy saving mechanisms used in different architectural models. The earlier achievements in energy efficient intrusion detection in WSNs are also summarized and existing problems are discussed. We also give an insight into the possible directions for future work in intrusion detection by highlighting open research areas.

Keywords: Anomaly based detection, intrusion detection system, intrusion prevention, signature based detection, wireless sensor network

DOI: 10.3233/AIS-170426

Journal: Journal of Ambient Intelligence and Smart Environments, vol. 9, no. 2, pp. 239-261, 2017