Sequential Hypothesis Testing Based Approach for Replica Cluster Detection in Wireless Sensor Networks

Abstract

In wireless sensor networks, replica node attacks are very dangerous because the attacker can compromise a single node and generate as many replicas of the compromised node as he wants, and then exploit these replicas to disrupt the normal operations of sensor networks. Several schemes have been proposed to detect replica node attacks in sensor networks. Although these schemes are capable of detecting replicas that are widely spread in the network, they will likely fail to detect replica cluster attacks in which replicas form a cluster in a small region. These attacks are also harmful because the attacker can leverage a replica cluster to harmfully impact on the much of the network. To defend against replica cluster attacks, we propose an efficient and effective replica cluster detection scheme using the Sequential Hypothesis Testing. We evaluate our proposed scheme through analysis and simulation. The evaluation results demonstrate that it accomplishes robust replica cluster detection capability.

Hybrid Multi-Level Detection and Mitigation of Clone Attacks in Mobile Wireless Sensor Network (MWSN)

“…Accordingly, the concept of swapping time position is claimed in the MWSN communication area of every encountered nodes. In Static WSN, the authors [13,14] proposed the clone detection solution using cluster based. Localization based detection approach has been explained in [15].…”

Detecting Sybil Attacks In Wireless Sensor Networks Using Sequential Analysis

“…However, this method also suffers from high communication overhead like DSANI method. Ho et al [26] proposed a replica cluster detection method using sequential analysis. In this, malicious nodes forge identities of benign nodes and launch various attacks by creating duplicate clusters.…”

“…In the existing literature, nodes in a network are categorized into benign or malicious based on certain parameters (or evidences). These evidences could be message authentication [11], random passwords [12], signal strength [16,26], Time Difference of Arrival (TODA) [17], location verification [18][19][20][21][22], trust values [23], identifying common neighbors [25,26], energy and hop count [28], traffic monitoring [29], signal print [30], and others. By having the evidences, Sybil attack detection methods analyze and classify the malicious nodes from benign nodes.…”