Abstract
This research suggests a Denial of Service (DoS) detection method based on the collection of interdependent behavior data in a sensor network environment. In order to collect the interdependent behavior data, we use a base station to analyze traffic and behaviors among nodes and introduce methods of detecting changes in the environment with precursor symptoms. The study presents a DoS Detection System based on Global Interdependent Behaviors and shows the result of detecting a sensor carrying out DoS attacks through the test-bed.
Highlights
The number of security breaches is on a sharp increase and so is are the damage and losses. the actual amount of damage from malicious codes has not been fully revealed, it is enormous, and such damage occurs from common services such as in cases of game hacking, messenger phishing, voice phishing, and so on [1]
We have studied varying vulnerability in an existing sensor network and, based on the results, presented an interdependent-based Denial of Service (DoS) detection system that can predict vulnerability
We have suggested a DoS detection system based on global interdependent behaviors, which analyzes traffic and tracks node behaviors in a sensor network environment
Summary
The number of security breaches is on a sharp increase and so is are the damage and losses. Sensor networks have already been used along with a smartphone, offering various applications in fields as diverse as the medical, military, environmental and entertainment services in a multitude of areas and, DoS attacks using the environment are likely to cause tremendous damage. We need to analyze cases of DoS attacks showing various patterns and develop a detection method to respond to attacks using the sensor networks. Most research on sensor network security focuses on key distribution and management, authentication, network structure, routing, and so on, but there is lack of research on precursor symptom detection. We have studied varying vulnerability in an existing sensor network and, based on the results, presented an interdependent-based DoS detection system that can predict vulnerability. Traffic changes and packet data were analyzed by means of node data management
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