Sleep Deprivation Attack Detection in Wireless Sensor Network

TL;DR

This paper introduces an energy-efficient hierarchical framework for detecting sleep deprivation attacks in wireless sensor networks, aiming to improve accuracy and reduce false positives.

Contribution

It presents a novel distributed collaborative anomaly detection model specifically designed for sleep deprivation attack detection in sensor networks.

Findings

Effective detection of sleep deprivation attacks with reduced false positives.

Improved energy efficiency in intrusion detection process.

Hierarchical framework enhances detection accuracy and scalability.

Abstract

Deployment of sensor network in hostile environment makes it mainly vulnerable to battery drainage attacks because it is impossible to recharge or replace the battery power of sensor nodes. Among different types of security threats, low power sensor nodes are immensely affected by the attacks which cause random drainage of the energy level of sensors, leading to death of the nodes. The most dangerous type of attack in this category is sleep deprivation, where target of the intruder is to maximize the power consumption of sensor nodes, so that their lifetime is minimized. Most of the existing works on sleep deprivation attack detection involve a lot of overhead, leading to poor throughput. The need of the day is to design a model for detecting intrusions accurately in an energy efficient manner. This paper proposes a hierarchical framework based on distributed collaborative mechanism for detecting sleep deprivation torture in wireless sensor network efficiently. Proposed model uses anomaly detection technique in two steps to reduce the probability of false intrusion.