Quarter Sphere Based Distributed Anomaly Detection in Wireless Sensor Networks

By Sutharshan Rajasegarar, C. Leckie, Marimuthu Swami Palaniswami, James C. Bezdek. 2007

213 citations recorded by the source corpus.

Anomaly detection is an important challenge for tasks such as fault diagnosis and intrusion detection in energy constrained wireless sensor networks. A key problem is how to minimise the communication overhead in the network while performing in-network computation when detecting anomalies. Our approach to this problem is based on a formulation that uses distributed, one-class quarter-sphere support vector machines to identify anomalous measurements in the data. We demonstrate using sensor data from the Great Duck Island Project that our distributed approach is energy efficient in terms of communication overhead while achieving comparable accuracy to a centralised scheme.

Subfields: Artificial Intelligence, Computer Networks and Communications

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Citations in the corpus

Security analysis of online centroid anomaly detection (2010, background): “, 2006; Rieck and Laskov, 2007), wireless sensor networks (Rajasegarar et al., 2007) and jet engine vibration data analysis (Nairac et al.”
State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems (2017, methodology): “Additionally, security, particularly intrusion detection in the WSN, has been widely studied using Machine Intelligence theory that has appeared in [127]–[130].”
Hierarchical trust management for wireless sensor networks and its application to trust-based routing (2011, background): “The trust value that node i evaluates towards node j at time t , (cid:1846) (cid:3036)(cid:3037) (cid:1872) , is represented as a real number in the range of [0 where 1 indicates 1 2 3 4”
A Survey of Intrusion Detection Systems in Wireless Sensor Networks (2014, methodology): “In [68], the same authors proposed a solution to the problem of minimizing the communication overhead in the”
Anomaly detection in wireless sensor networks: A survey (2011, methodology): “After reviewing these schemes, we know that D3 and MGDD in Subramaniam et al. (2006) are carried out in hierarchical WSNs, and Chi (Liu et al.”
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications (2014, methodology): “For movement detection, SVM will be executed to provide the new location.”
High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning (2016, methodology): “In [35], a distributed approach is presented using the QSSVM.”
Outlier Detection Techniques for Wireless Sensor Networks: A Survey (2008, methodology): “[42] propose a SVM-based technique for outlier detection in sensor data.”