Jamming Detection Technique Using Improved Exponentially Weighted Moving Average (IEWMA) Algorithm for WSN
Keywords:Jamming attack, Statistical Process Control, Improved Exponentially Weighted Moving Average, Break Advent Time
WSN is emerging field with various application areas including health monitoring, military operations, agriculture, and environmental monitoring, have recently come to light as extremely promising solutions. Given the potential widespread use of Wireless Sensor Networks (WSNs), there is a growing concern about the security vulnerabilities faced by WSN sensor nodes, particularly due to their deployment in resource-constrained and hostile environments. One prevalent and disruptive form of attack is the DoS attack, with the jamming attack being a subcategory. The jamming attack is in which radio frequency signals are emitted to interfere with and disrupt the normal functioning of sensor nodes in the WSN, leading to service denial. To tackle this concern, a suggested approach is presented, utilizing a sequential method rooted in the Statistical Process Control (SPC) method for the identification of jamming attacks. For more exact detection of variations sin the strength of jamming attacks, an Improved Exponentially Weighted Moving Average (IEWMA) technique is suggested. This is achieved by analyzing a parameter called the packet Break Advent Time (BAT), which is derived from packets received from sensor nodes. Through simulation experiments, outcomes demonstrate that proposed methodology can accurately and efficiently detect jamming attacks in the sensor network, with minimal overhead, reduced energy consumption, and high precision.
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