DETECTING DENIAL OF SERVICE ATTACK IN WIRELESS SENSOR NETWORKS

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Abstract:

Wireless Sensor Networks (WSNs) play a critical role in various applications, such as environmental monitoring, surveillance, and healthcare. However, the distributed and resource-constrained nature of WSNs makes them vulnerable to various security threats, including Denial of Service (DoS) attacks. DoS attacks can disrupt the normal operation of WSNs by overwhelming the network resources, thereby rendering the sensor nodes incapable of performing their intended tasks.

This abstract presents an overview of research focused on detecting DoS attacks in WSNs. The primary objective is to develop robust and efficient mechanisms to identify and mitigate such attacks, enhancing the reliability and security of WSN deployments.

The detection of DoS attacks in WSNs involves the exploration and analysis of various network-level and node-level characteristics. Network-level detection techniques typically involve monitoring traffic patterns, packet loss, and energy consumption to identify abnormal behavior that indicates a potential attack. Node-level detection methods, on the other hand, focus on identifying compromised or malicious nodes within the network.

Several detection approaches have been proposed in the literature, including statistical-based methods, machine learning algorithms, and anomaly detection techniques. Statistical-based methods leverage statistical properties of network traffic to distinguish between normal and malicious behavior. Machine learning algorithms utilize trained models to classify network traffic as either legitimate or malicious based on a set of predefined features. Anomaly detection techniques aim to identify deviations from normal behavior by establishing a baseline of normal network activity.

Furthermore, to enhance the effectiveness of DoS detection in WSNs, collaborative and distributed approaches have been investigated. These approaches involve cooperation among sensor nodes to collectively detect and mitigate attacks, allowing for improved detection accuracy and reduced false positives.

This abstract concludes by highlighting the importance of ongoing research in the field of DoS detection in WSNs. As WSNs continue to evolve and find applications in critical domains, the development of robust and adaptive defense mechanisms is crucial to ensure the integrity, availability, and confidentiality of the network and its associated data.

Keywords: Wireless Sensor Networks, Denial of Service Attacks, DoS Detection, Network-level Detection, Node-level Detection, Statistical-based Methods, Machine Learning, Anomaly Detection, Collaborative Detection.

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