Abstract
Wireless Sensor Networks (WSNs), in recent times, have become one of the most promising network solutions with a wide variety of applications in the areas of agriculture, environment, healthcare and the military. Notwithstanding these promising applications, sensor nodes in WSNs are vulnerable to different security attacks due to their deployment in hostile and unattended areas and their resource constraints. One of such attacks is the DoS jamming attack that interferes and disrupts the normal functions of sensor nodes in a WSN by emitting radio frequency signals to jam legitimate signals to cause a denial of service. In this work we propose a step-wise approach using a statistical process control technique to detect these attacks. We deploy an exponentially weighted moving average (EWMA) to detect anomalous changes in the intensity of a jamming attack event by using the packet inter-arrival feature of the received packets from the sensor nodes. Results obtained from a trace-driven simulation show that the proposed solution can efficiently and accurately detect jamming attacks in WSNs with little or no overhead.
Highlights
Wireless Sensor Networks (WSNs) in recent times have expanded their range of applications from their initial deployment for battlefield intelligence surveillance to areas such as emergency response support, meteorological weather forecasting, security applications and factory automation, just to mention a few
Our jamming detection technique consists of two phases; the first phase is the training phase that involves the capture of normal inter-arrival time (IAT) from legitimate member nodes to the cluster head and from the cluster heads to the base station to initialize its parameters and obtain a normal profile
We proposed a stepwise approach for detecting different forms of jamming attacks, where the jamming detector algorithm is deployed on the cluster head to detect attacks in the member nodes and on the base stations to detect attacks in the cluster heads using the packet IAT
Summary
WSNs in recent times have expanded their range of applications from their initial deployment for battlefield intelligence surveillance to areas such as emergency response support, meteorological weather forecasting, security applications and factory automation, just to mention a few. WSNs consiss of small and inexpensive sensor nodes without an existing infrastructure. They are often used to sense, process, transmit and receive information from the area they are deployed before it is conveyed to a base station. A typical WSN consists of hundreds to thousands of sensor nodes which can be categorized according to their structure (topology) and the environment in which they are deployed. WSNs can be categorized according to the placement of the sensor nodes in the deployed environment [1]. These nodes can be of equal capacity, while others have varying capacity, depending on the architecture. The environments where the sensor nodes are deployed in a WSN can be grouped into five classes, namely: underground WSNs, terrestrial WSNs, underwater WSNs, multi-media WSNs and mobile WSNs [3]
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