Article
A Statistical Approach to Detect Jamming Attacks in
Wireless Sensor Networks
Opeyemi Osanaiye
1,
*, Attahiru S. Alfa
1,2
ID
and Gerhard P. Hancke
1
ID
1
Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Lynnwood Road,
Pretoria 0002, South Africa; attahiru.alfa@umanitoba.ca (A.S.A.); gerhard.hancke@up.ac.za (G.P.H.)
2
Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg,
MB R3T 2N2, Canada
* Correspondence: opyosa001@myuct.ac.za
Received: 27 April 2018; Accepted: 21 May 2018; Published: 24 May 2018
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.
Keywords:
wireless sensor networks; jamming attack; exponentially weighted moving average;
inter-arrival time
1. Introduction
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.
Structurally, 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 three main types of WSN structure are flat-based (tree), cluster-based and
hierarchical [
2
]. Furthermore, 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].
The sensor nodes in a WSN are often deployed in remote, harsh and inaccessible areas and
are often characterized by their resource constraints such as limited power, limited storage, limited
bandwidth and short communication range. These, coupled with the vulnerability of the wireless
medium (i.e., open and shared) have made sensor nodes susceptible to different security attacks such
Sensors 2018, 18, 1691; doi:10.3390/s18061691 www.mdpi.com/journal/sensors
