TY - GEN
T1 - Evaluation of detecting malicious nodes using Bayesian model in wireless intrusion detection
AU - Meng, Yuxin
AU - Li, Wenjuan
AU - Kwok, Lam For
PY - 2013
Y1 - 2013
N2 - Wireless sensor network (WSN) is vulnerable to a wide range of attacks due to its natural environment and inherent unreliable transmission. To protect its security, intrusion detection systems (IDSs) have been widely deployed in such a wireless environment. In addition, trust-based mechanism is a promising method in detecting insider attacks (e.g., malicious nodes) in a WSN. In this paper, we thus attempt to develop a trust-based intrusion detection mechanism by means of Bayesian model and evaluate it in the aspect of detecting malicious nodes in a WSN. This Bayesian model enables a hierarchical wireless sensor network to establish a map of trust values among different sensor nodes. The hierarchical structure can reduce network traffic caused by node-to-node communications. To evaluate the performance of the trust-based mechanism, we analyze the impact of a fixed and a dynamic trust threshold on identifying malicious nodes respectively and further conduct an evaluation in a wireless sensor environment. The experimental results indicate that the Bayesian model is encouraging in detecting malicious sensor nodes, and that the trust threshold in a wireless sensor network is more dynamic than that in a wired network.
AB - Wireless sensor network (WSN) is vulnerable to a wide range of attacks due to its natural environment and inherent unreliable transmission. To protect its security, intrusion detection systems (IDSs) have been widely deployed in such a wireless environment. In addition, trust-based mechanism is a promising method in detecting insider attacks (e.g., malicious nodes) in a WSN. In this paper, we thus attempt to develop a trust-based intrusion detection mechanism by means of Bayesian model and evaluate it in the aspect of detecting malicious nodes in a WSN. This Bayesian model enables a hierarchical wireless sensor network to establish a map of trust values among different sensor nodes. The hierarchical structure can reduce network traffic caused by node-to-node communications. To evaluate the performance of the trust-based mechanism, we analyze the impact of a fixed and a dynamic trust threshold on identifying malicious nodes respectively and further conduct an evaluation in a wireless sensor environment. The experimental results indicate that the Bayesian model is encouraging in detecting malicious sensor nodes, and that the trust threshold in a wireless sensor network is more dynamic than that in a wired network.
KW - Bayesian Model
KW - Intrusion Detection
KW - Network Security
KW - Trust Computation
KW - Wireless Sensor Network
UR - http://www.scopus.com/inward/record.url?scp=84883344311&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-38631-2_4
DO - 10.1007/978-3-642-38631-2_4
M3 - Conference article published in proceeding or book
AN - SCOPUS:84883344311
SN - 9783642386305
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 40
EP - 53
BT - Network and System Security - 7th International Conference, NSS 2013, Proceedings
T2 - 7th International Conference on Network and System Security, NSS 2013
Y2 - 3 June 2013 through 4 June 2013
ER -