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http://dx.doi.org/10.5762/KAIS.2014.15.8.5256

Study of The Abnormal Traffic Detection Technique Using Forecasting Model Based Trend Model  

Jang, Sang-Soo (Korea Internet and Security Agency)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.15, no.8, 2014 , pp. 5256-5262 More about this Journal
Abstract
Recently, Distributed Denial of Service (DDoS) attacks, such as spreading malicious code, cyber-terrorism, have occurred in government agencies, the press and the financial sector. DDoS attacks are the simplest Internet-based infringement attacks techniques that have fatal consequences. DDoS attacks have caused bandwidth consumption at the network layer. These attacks are difficult to detect defend against because the attack packets are not significantly different from normal traffic. Abnormal traffic is threatening the stability of the network. Therefore, the abnormal traffic by generating indications will need to be detected in advance. This study examined the abnormal traffic detection technique using a forecasting model-based trend model.
Keywords
Abnormal Traffic; DDoS; Malicious Code; Forecasting; Time Series; Trend Model;
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