Measuring the impact of denial-of-service attacks on wireless sensor networks

Abstract

Wireless sensor networks (WSNs) are especially susceptible to denial-of-service attacks due to the resourceconstrained nature of motes. We follow a systematic approach to analyze the impacts of these attacks on the network behavior; therefore, we first identify a large number of metrics easily obtained and calculated without incurring too much overhead. Next, we statistically test these metrics to assess whether they exhibit significantly different values under attack when compared to those of the baseline operation. The metrics look into different aspects of the motes and the network, for example, MCU and radio activities, network traffic statistics, and routing related information. Then, to show the applicability of the metrics to different WSNs, we vary several parameters, such as traffic intensity and transmission power. We consider the most common topologies in wireless sensor networks such as central data collection and meshed multi-hop networks by using the collection tree and the mesh protocol. Finally, the metrics are grouped according to their capability of distinction into different classes. In this work, we focus on jamming and blackhole attacks. Our experiments reveal that certain metrics are able to detect a jamming attack on all motes in the testbed, irrespective of the parameter combination, and at the highest significance value. To illustrate these facts, we use a standard testbed consisting of the widely-employed TelosB motes.

An Ensemble model for automated attack classification in large scale wireless sensor network using Mobile Agent based Intrusion Detection System

“…A linear regression model is applied to predict energy consumption and deployed results under the denial-ofservice attacks. Consider flooding can be detected with high accuracy while keeping the number of false positives very low [14]. Consider the importance of the security of networks.…”

Impact Analysis of Network Layer Attacks in Real-Time Wireless Sensor Network Testbed

“…However, it is an extremely costly solution for resource-constrained networks. Riecker et al [4] measured the impact of Denial-of-Service Attacks: Jamming and Blackhole, using a testbed consisting of TelosB motes. The authors identified the performance metrics classes based on their capability to detect attacks.…”

Primary User Emulation in Cognitive Radio-Enabled WSNs for Structural Health Monitoring: Modeling and Attack Detection

“…For example, in [20], the authors show authentication mechanisms to legitimate primary users from the available spectrum usage by means of physical layer network coding techniques. In [21], the authors focus on jamming and blackhole attacks. They propose a mechanism based on a 2…”

“…However, no cognitive radio is considered. In contrast to [21], we propose a mathematical model as a tool for attack detection instead of practical measurements on the system's performance. The mathematical analysis is more tractable than measuring the system operation in normal conditions.…”