TY - JOUR
T1 - Random neural network based intelligent intrusion detection for wireless sensor networks
AU - Saeed, Ahmed
AU - Ahmadinia, Ali
AU - Javed, Abbas
AU - Larijani, Hadi
N1 - Accepted: 18-3-16 (in SAN)
Online pub: 01-06-2016
OA publication (note - journal article but from conference)
Duplicate record created by HL 23-3-17, no FT etc.
PY - 2016
Y1 - 2016
N2 - Security and privacy of data are one of the prime concerns in today's embedded devices. Primitive security techniques like signature-based detection of malware and regular update of signature database are not feasible solutions as they cannot secure such systems, having limited resources, effectively. Furthermore, energy efficient wireless sensor modes running on batteries cannot afford the implementation of cryptography algorithms as such techniques have significant impact on the system power consumption. Therefore, in order to operate wireless embedded devices in a secure manner, the system must be able to detect and prevent any kind of intrusions before the network (i.e. sensor nodes and base station) is destabilized by the attackers. In this paper, we have presented an intrusion detection mechanism by implementing an intelligent security architecture using Random Neural Networks (RNN). To validate the feasibility of the proposed security solution, it is implemented for an existing wireless sensor network system and its functionality is practically demonstrated by successfully detecting the presence of any suspicious sensor node and anomalous activity in the base station with high accuracy and minimal performance overhead.
AB - Security and privacy of data are one of the prime concerns in today's embedded devices. Primitive security techniques like signature-based detection of malware and regular update of signature database are not feasible solutions as they cannot secure such systems, having limited resources, effectively. Furthermore, energy efficient wireless sensor modes running on batteries cannot afford the implementation of cryptography algorithms as such techniques have significant impact on the system power consumption. Therefore, in order to operate wireless embedded devices in a secure manner, the system must be able to detect and prevent any kind of intrusions before the network (i.e. sensor nodes and base station) is destabilized by the attackers. In this paper, we have presented an intrusion detection mechanism by implementing an intelligent security architecture using Random Neural Networks (RNN). To validate the feasibility of the proposed security solution, it is implemented for an existing wireless sensor network system and its functionality is practically demonstrated by successfully detecting the presence of any suspicious sensor node and anomalous activity in the base station with high accuracy and minimal performance overhead.
KW - random neural network
KW - wireless sensor networks
U2 - 10.1016/j.procs.2016.05.453
DO - 10.1016/j.procs.2016.05.453
M3 - Article
VL - 80
SP - 2372
EP - 2376
JO - Procedia Computer Science
JF - Procedia Computer Science
SN - 1877-0509
ER -