A novel jammer detection framework for cluster-based wireless sensor networks

Abstract

Jamming attack is a serious security threat in wireless sensor networks. Therefore, it is important to frame a mechanism to protect wireless sensor networks from various jamming attacks. Jammer intrusion detection and jamming detection are two separate issues. In this paper, a novel jammer detection framework to detect the intrusion of jammer and the presence of jamming in a cluster-based wireless sensor network is proposed. The proposed framework is novel in three aspects: whenever the cluster head receives a packet, it first verifies whether the source node is a legitimate, new node, or a jammer node. Second, when the source node is declared as a new one in the first step, then the framework validates whether the new node is legitimate node in the previous cluster or a jammer node by using cluster head code. Third, the framework observes the behavior of the newly joined node and the existing nodes to identify whether the nodes in the cluster is jammed or not. Additionally, it also classifies the types of jamming, if the presence of jamming is detected. Simulation result shows that the proposed framework performs extremely well and achieves jamming detection rate as high as 99.88 %.

Fuzzy logic–based jamming detection algorithm for cluster‐based wireless sensor network

“…Now, the performance evaluation metric TDR of the proposed system FLJDA is compared with JDT, JDF, and existing JM . The 1‐way ANOVA is used to determine whether there are any significant differences between the means of FLJDA, JDT, JDF, and JM.…”

“…True detection ratio (TDR) vs undetection ratio (UDR) for different types of jamming with respect to various configurations work, the CH alone estimates the metrics (PDR and RSSI) and makes a decision about "jammed situation" or "non jammed situation" (CM is not burdened, since CM does not involve in collection of metrics and make the decision about whether the node is jammed or not). Now, the performance evaluation metric TDR of the proposed system FLJDA is compared with JDT, 26 JDF, 27 and existing JM. 13 The 1-way ANOVA is used to determine whether there are any significant differences between the means of FLJDA, JDT, JDF, and JM.…”

“…This setup has several advantages such as increased scalability, low energy consumption, small size of routing table and limited communication bandwidth, decreased collision, and communication overhead. In the proposed system, statistical test analysis of variance (ANOVA) using Tukey post hoc test is done to compare the performance of the proposed system from the existing systems. (1) The 1‐way ANOVA test is performed to state whether there is difference among the 4 population means of proposed system (FLJDA) and existing systems (jamming detection technique [JDT], jammer detection framework [JDF], and jamming detection mechanism [JM]). That is, the difference among the TDR of FLJDA, JDT, JDF, and JM is determined.…”

Performance Comparison of LEACH and LEACH-C Protocols in Wireless Sensor Networks

“…This network comprises several clusters, also known as clumps, where each has its own cluster head (CH), which is responsible for data transmission to all sensors in that cluster [13]. Nodes that are not cluster heads are called cluster members (CMs) [2]. Intra-cluster and inter-cluster coordination are the responsibility of CHs [21].…”

“…A wireless sensor network (WSN) comprises sensors that are spatially distributed [1] and deployed in an area called the sensor field [2]. Each sensor collects data and sends it to a central aggregation unit known as the base station (BS) or sink [3].…”