Real-time DDoS attack detection using FPGA

2017 ◽  
Vol 110 ◽  
pp. 48-58 ◽  
Author(s):  
N. Hoque ◽  
H. Kashyap ◽  
D.K. Bhattacharyya
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Haibin Shi ◽  
Guang Cheng ◽  
Ying Hu ◽  
Fuzhou Wang ◽  
Haoxuan Ding

With the great changes in network scale and network topology, the difficulty of DDoS attack detection increases significantly. Most of the methods proposed in the past rarely considered the real-time, adaptive ability, and other practical issues in the real-world network attack detection environment. In this paper, we proposed a real-time adaptive DDoS attack detection method RT-SAD, based on the response to the external network when attacked. We designed a feature extraction method based on sketch and an adaptive updating algorithm, which makes the method suitable for the high-speed network environment. Experiment results show that our method can detect DDoS attacks using sampled Netflowunder high-speed network environment, with good real-time performance, low resource consumption, and high detection accuracy.


2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Bin Jia ◽  
Yan Ma ◽  
Xiaohong Huang ◽  
Zhaowen Lin ◽  
Yi Sun

In the wake of the rapid development and wide application of information technology and Internet, our society has come into the information explosion era. Meanwhile, it brings in new and severe challenges to the field of network attack behavior detection due to the explosive growth and high complexity of network traffic. Therefore, an effective and efficient detection mechanism that can detect attack behavior from large scale of network traffic plays an important role. In this paper, we focus on how to distinguish the attack traffic from normal data flows in Big Data and propose a novel real-time DDoS attack detection mechanism based on Multivariate Dimensionality Reduction Analysis (MDRA). In this mechanism, we first reduce the dimensionality of multiple characteristic variables in a network traffic record by Principal Component Analysis (PCA). Then, we analyze the correlation of the lower dimensional variables. Finally, the attack traffic can be differentiated from the normal traffic by MDRA and Mahalanobis distance (MD). Compared with previous research methods, our experimental results show that higher precision rate is achieved and it approximates to 100% in True Negative Rate (TNR) for detection; CPU computing time is one-eightieth and memory resource consumption is one-third of the previous detection method based on Multivariate Correlation Analysis (MCA); computing complexity is constant.


Author(s):  
Alexandre da Silveira Ilha ◽  
Angelo Cardoso Lapolli ◽  
Jonatas Adilson Marques ◽  
Luciano Paschoal Gaspary

2017 ◽  
Vol 6 (3) ◽  
pp. 135-142 ◽  
Author(s):  
In Hyuk Seo ◽  
Ki-Taek Lee ◽  
Jinhyun Yu ◽  
Seungjoo Kim

2021 ◽  
Author(s):  
Shriram Rajesh ◽  
Marvin Clement ◽  
Sooraj S. B. ◽  
Al Shifan S. H. ◽  
Jyothi Johnson

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