Detection of DDoS Attacks Using Triangle Expectation with MapReduce

[ 31 October 2024 | vol. 16 | no. 2 | pp. 23-36 ]

About Authors:

Miroslaw Swiercz
-Bialystok University of Technology, Poland

Abstract:

With the rapid expansion of Internet usage across the globe, the importance of Internet security has grown significantly. Among the various cyber threats, Distributed Denial-of-Service (DDoS) attacks pose a serious challenge, as they can disrupt services by overwhelming a target system with traffic, leading to considerable operational disruptions and financial losses. In this study, we introduce a novel technique known as "triangle expectation," designed specifically to detect and locate the sources of DDoS attacks. By identifying these sources, the technique aims to facilitate timely intervention and blocking of malicious traffic, thereby enhancing overall network security. To handle the extensive data generated from network connections, we employed an advanced sampling technique, allowing us to efficiently analyze large datasets without compromising the accuracy of our findings. The proposed method's effectiveness and reliability have been rigorously validated through a series of experiments, demonstrating its potential as a robust tool in the fight against DDoS attacks.

Keywords:

DDoS attacks, Triangle Expectation, Triangle Counting, Hadoop, Sampling

 

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