DDoS attack detection method based on network abnormal behaviour in big data environment

2020 ◽  
Vol 23 (1) ◽  
pp. 22
Author(s):  
Jing Chen ◽  
Xiangyan Tang ◽  
Jieren Cheng ◽  
Fengkai Wang ◽  
Ruomeng Xu
Keyword(s):  
Big Data ◽  
Detection Method ◽  
Attack Detection ◽  
Abnormal Behaviour ◽  
Ddos Attack ◽  
Data Environment ◽  
Ddos Attack Detection

DDoS attack detection method based on network abnormal behaviour in big data environment

2020 ◽  
Vol 23 (1) ◽  
pp. 22
Author(s):  
Jing Chen ◽  
Ruomeng Xu ◽  
Fengkai Wang ◽  
Jieren Cheng ◽  
Xiangyan Tang
Keyword(s):  
Big Data ◽  
Detection Method ◽  
Attack Detection ◽  
Abnormal Behaviour ◽  
Ddos Attack ◽  
Data Environment ◽  
Ddos Attack Detection

scholarly journals A DDoS attack detection system based on spark framework

2017 ◽  
Vol 14 (3) ◽  
pp. 769-788 ◽  
Author(s):  
Dezhi Han ◽  
Kun Bi ◽  
Han Liu ◽  
Jianxin Jia
Keyword(s):  
Big Data ◽  
Detection System ◽  
Attack Detection ◽  
Ddos Attacks ◽  
Ip Address ◽  
Ddos Attack ◽  
Data Environment ◽  
Dynamic Sampling ◽  
Ddos Attack Detection ◽  
Spark Framework

There are many problems in traditional Distributed Denial of Service (DDoS) attack detection such as low accuracy, low detection speed and so on, which is not suitable for the real time detecting and processing of DDoS attacks in big data environment. This paper proposed a novel DDoS attack detection system based on Spark framework including 3 main algorithms. Based on information entropy, the first one can effectively warn all kinds of DDoS attacks in advance according to the information entropy change of data stream source IP address and destination IP address; With the help of designed dynamic sampling K-Means algorithm, this new detection system improves the attack detection accuracy effectively; Through running dynamic sampling K-Means parallelization algorithm, which can quickly and effectively detect a variety of DDoS attacks in big data environment. The experiment results show that this system can not only early warn DDoS attacks effectively, but also can detect all kinds of DDoS attacks in real time, with low false rate.

scholarly journals Adaptive DDoS Attack Detection Method Based on Multiple-Kernel Learning

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Jieren Cheng ◽  
Chen Zhang ◽  
Xiangyan Tang ◽  
Victor S. Sheng ◽  
Zhe Dong ◽  
...  
Keyword(s):  
Detection Method ◽  
Denial Of Service ◽  
Multiple Kernel Learning ◽  
Attack Detection ◽  
Kernel Learning ◽  
Economic Losses ◽  
Ddos Attacks ◽  
Ddos Attack ◽  
Multiple Kernel ◽  
Ddos Attack Detection

Distributed denial of service (DDoS) attacks has caused huge economic losses to society. They have become one of the main threats to Internet security. Most of the current detection methods based on a single feature and fixed model parameters cannot effectively detect early DDoS attacks in cloud and big data environment. In this paper, an adaptive DDoS attack detection method (ADADM) based on multiple-kernel learning (MKL) is proposed. Based on the burstiness of DDoS attack flow, the distribution of addresses, and the interactivity of communication, we define five features to describe the network flow characteristic. Based on the ensemble learning framework, the weight of each dimension is adaptively adjusted by increasing the interclass mean with a gradient ascent and reducing the intraclass variance with a gradient descent, and the classifier is established to identify an early DDoS attack by training simple multiple-kernel learning (SMKL) models with two characteristics including interclass mean squared difference growth (M-SMKL) and intraclass variance descent (S-SMKL). The sliding window mechanism is used to coordinate the S-SMKL and M-SMKL to detect the early DDoS attack. The experimental results indicate that this method can detect DDoS attacks early and accurately.

scholarly journals A DDoS Attack Detection Method Based on Machine Learning

2019 ◽  
Vol 1237 ◽  
pp. 032040
Author(s):  
Jiangtao Pei ◽  
Yunli Chen ◽  
Wei Ji
Keyword(s):  
Machine Learning ◽  
Detection Method ◽  
Attack Detection ◽  
Ddos Attack ◽  
Ddos Attack Detection

scholarly journals A DDoS Attack Detection Method Based on Hybrid Heterogeneous Multiclassifier Ensemble Learning

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Bin Jia ◽  
Xiaohong Huang ◽  
Rujun Liu ◽  
Yan Ma
Keyword(s):  
Ensemble Learning ◽  
Detection Method ◽  
Detection System ◽  
Detection Algorithm ◽  
Attack Detection ◽  
True Negative ◽  
Detection Techniques ◽  
Ddos Attack ◽  
Accuracy And Precision ◽  
Ddos Attack Detection

The explosive growth of network traffic and its multitype on Internet have brought new and severe challenges to DDoS attack detection. To get the higher True Negative Rate (TNR), accuracy, and precision and to guarantee the robustness, stability, and universality of detection system, in this paper, we propose a DDoS attack detection method based on hybrid heterogeneous multiclassifier ensemble learning and design a heuristic detection algorithm based on Singular Value Decomposition (SVD) to construct our detection system. Experimental results show that our detection method is excellent in TNR, accuracy, and precision. Therefore, our algorithm has good detective performance for DDoS attack. Through the comparisons with Random Forest, k-Nearest Neighbor (k-NN), and Bagging comprising the component classifiers when the three algorithms are used alone by SVD and by un-SVD, it is shown that our model is superior to the state-of-the-art attack detection techniques in system generalization ability, detection stability, and overall detection performance.

scholarly journals A Novel DDoS Attack Detection Method Using Optimized Generalized Multiple Kernel Learning

2020 ◽  
Vol 62 (3) ◽  
pp. 1423-1443
Author(s):  
Jieren Cheng ◽  
Junqi Li ◽  
Xiangyan Tang ◽  
Victor S. Sheng ◽  
Chen Zhang ◽  
...  
Keyword(s):  
Detection Method ◽  
Multiple Kernel Learning ◽  
Attack Detection ◽  
Kernel Learning ◽  
Ddos Attack ◽  
Multiple Kernel ◽  
Ddos Attack Detection
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