Decentralized Hypothesis Testing in Wireless Sensor Networks in the Presence of Misbehaving Nodes

Abstract

Abstract-Wireless sensor networks are prone to node misbehavior arising from tampering by an adversary (Byzantine attack), or due to other factors such as node failure resulting from hardware or software degradation. In this paper we consider the problem of decentralized detection in wireless sensor networks in the presence of one or more classes of misbehaving nodes. Binary hypothesis testing is considered where the honest nodes transmit their binary decisions to the fusion center (FC), while the misbehaving nodes transmit fictitious messages. The goal of the FC is to identify the misbehaving nodes and to detect the state of nature. We identify each class of nodes with an operating point (false alarm and detection probabilities) on the ROC (receiver operating characteristic) curve. Maximum likelihood estimation of the nodes' operating points is then formulated and solved using the expectation maximization (EM) algorithm with the nodes' identities as latent variables. The solution from the EM algorithm is then used to classify the nodes and to solve the decentralized hypothesis testing problem. Numerical results compared with those from the reputation-based schemes show a significant improvement in both classification of the nodes and hypothesis testing results. We also discuss an inherent ambiguity in the node classification problem which can be resolved if the honest nodes are in majority.

Counteracting malicious users in cognitive radio networks over imperfect reporting channels

“…To detect the malicious behaviors of the SUs, we define the trust metrics based on the estimated SU's operating point parameter, i.e., 2 2 , , n…”

“…Collaborative spectrum sensing (CSS) has been proposed to improve detection accuracy by exploiting SUs' spatial diversity. However, collaborative spectrum sensing also induces security vulnerabilities [2], such as spectrum sensing data falsification (SSDF) attack. In an SSDF attack, a malicious user intentionally send falsified local spectrum sensing reports to a fusion center (FC) in an attempt to confuse the FC.…”

Detection of dumb nodes in a stationary wireless sensor network

“…Chen et al presented an approach for detecting malicious behavior of a node by combining Monitor Group (MG) and routing table information in [8]. Another work proposed by Soltanmohammadi et al in [9] is capable of detecting malicious nodes using a binary hypothesis testing framework. In this work, the honest node transmits binary decision to the fusion center, whereas a malicious node transmits fictitious messages to the fusion center and finally the fusion center helps in identifying the misbehaving nodes.…”

Distributed Inference With M-Ary Quantized Data in the Presence of Byzantine Attacks

“…Soltanmohammadi et al in [23] investigated the problem of distributed detection in the presence of different types of Byzantine nodes. Each Byzantine node type corresponds to a different operating point, and, therefore, the authors considered the problem of identifying different Byzantine nodes, along with their operating points.…”