A Real-Time Node-Based Traffic Anomaly Detection Algorithm for Wireless Sensor Networks
ICW '05 Proceedings of the 2005 Systems Communications
Linear-Time Computation of Similarity Measures for Sequential Data
The Journal of Machine Learning Research
LIDeA: a distributed lightweight intrusion detection architecture for sensor networks
Proceedings of the 4th international conference on Security and privacy in communication netowrks
Attacks and defenses of ubiquitous sensor networks
Attacks and defenses of ubiquitous sensor networks
Hybrid intrusion detection system for wireless sensor networks
ICCSA'07 Proceedings of the 2007 international conference on Computational science and Its applications - Volume Part II
Integrated Computer-Aided Engineering
| Hi-index | 0.00 |
Wireless sensor networks are usually deployed in unattended environments. This is the main reason why the update of security policies upon identifying new attacks cannot be done in a timely fashion, which gives enough time to attackers to make significant damage. Thus, it is of great importance to provide protection from unknown attacks. However, existing solutions are mostly concentrated on known attacks. In order to tackle this issue, we propose a machine learning solution for anomaly detection along with the feature extraction process that tries to detect temporal and spatial inconsistencies in the sequences of sensed values and the routing paths used to forward these values to the base station. The data produced in the presence of an attacker are treated as outliers, and detected using clustering techniques. The techniques are coupled with a reputation system, isolating in this way the compromised nodes. The proposal exhibits good performances in detecting and confining previously unseen attacks.