Outlier detection and countermeasure for hierarchical wireless sensor networks

Abstract

Outliers in wireless sensor networks (WSNs) are sensor nodes that issue attacks by abnormal behaviours and fake message dissemination. However, existing cryptographic techniques are hard to detect these inside attacks, which cause outlier recognition a critical and challenging issue for reliable and secure data dissemination in WSNs. To efficiently identify and isolate outliers, this study presents a novel outlier detection and countermeasure scheme (ODCS), which consists of three mechanisms: (i) abnormal event observation mechanism for network surveillance; (ii) exceptional message supervision mechanism for distinguishing fake messages by exploiting spatiotemporal correlation and consistency and (iii) abnormal behaviour supervision mechanism for the evaluation of node behaviour. The ODCS provides a heuristic methodology and does not need the knowledge about normal or malicious sensors in advance. This property makes the ODCS not only to distinguish and deal with various dynamic attacks automatically without advance learning, but also to reduce the requirement of capability for constrained nodes. In the ODCS, the communication is limited in a local range, such as one-hop or a cluster, which can reduce the communication frequency and circumscribe the session range further. Moreover, the ODCS provides countermeasures for different types of attacks, such as the rerouting scheme and the rekey security scheme, which can separate outliers from normal sensors and enhance the robustness of network, even when some nodes are compromised by adversary. Simulation results indicate that our approach can effectively detect and defend the outlier attack.

An intrusion detection method for wireless sensor network based on mathematical morphology

“…However, most of them hardly sustain the Intrusion Detection . They are incapable of detecting whether “spy nodes” have been placed in a WSN topology.…”

A Multi-Agent System for Outliers Accommodation in Wireless Sensor Networks

“…Regarding nearest neighbour methods, they rely on particular metrics to compute distances among objects or samples with clear geometric interpretation, according to which a so-called outlier factor is calculated [10]. In clustering-based techniques [11] data sets are grouped into clusters consisting of similar attributes, being an object classified as an outlier when lying outside identified clusters. This kind of techniques is appealing in the sense that they do not require a priori knowledge of the data statistics, and can be used in an incremental model [4].…”

A Granulometric Size Distribution based intrusion detection method for WSN

“…Assume a random signal segment in Fig.1, where S(x) = (10,7,19,8,3,16,8,6,9,2,3,19,20,12,2) Step (a): Translating the original signal curve into step-function and calculate the entire subgraph area SG, then go to step (b).In this example, the subgraph area SG is 144. Fig.2 shows the original curve (the blue dot line) and its stepwise curve (the red line).…”

“…However, most of them hardly sustain the Intrusion Detection [8] [9]. They are incapable of detecting whether the "spy nodes" have been placed in a WSN topology and they are unaware of which node and when the WSN topology has been intruded, which constrains the security of the entire WSN.…”