scholarly journals A Novel Real-Time DDoS Attack Detection Mechanism Based on MDRA Algorithm in Big Data

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Bin Jia ◽  
Yan Ma ◽  
Xiaohong Huang ◽  
Zhaowen Lin ◽  
Yi Sun
Keyword(s):  
Big Data ◽  
Real Time ◽  
Network Traffic ◽  
Rapid Development ◽  
Computing Time ◽  
Attack Detection ◽  
Attack Behavior ◽  
Detection Mechanism ◽  
Ddos Attack ◽  
Ddos Attack Detection

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.

scholarly journals RT-SAD: Real-Time Sketch-Based Adaptive DDoS Detection for ISP Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Haibin Shi ◽  
Guang Cheng ◽  
Ying Hu ◽  
Fuzhou Wang ◽  
Haoxuan Ding
Keyword(s):  
Real Time ◽  
High Speed ◽  
Attack Detection ◽  
Detection Accuracy ◽  
Network Environment ◽  
The Real ◽  
Feature Extraction Method ◽  
Ddos Attack ◽  
High Speed Network ◽  
Ddos Attack Detection

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.

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 Feature Selection Techniques Cloud DDOS Attack Detection

2019 ◽  
Vol 8 (12) ◽  
pp. 1257-1260
Keyword(s):  
Cloud Computing ◽  
Network Traffic ◽  
Denial Of Service ◽  
Attack Detection ◽  
False Alarms ◽  
Traffic Classification ◽  
Ddos Attack ◽  
Network Intrusion ◽  
Network Traffic Classification ◽  
Ddos Attack Detection

The ongoing progression of Cloud Computing, it gives different services to together hierarchical as well as singular users, for example, shared computing resources, storage, networking and so on interest. The most well-known sort of attack on Cloud-computing is Distributed Denial of Service- (DDoS) Attack. DDoS attack is an bother which makes resources inaccessible to the client by trading off enormous no of system called bots. This paper proposes systems to create an ideal network traffic feature set for network intrusion detection. The proposed system shows that a reliable set of features are chosen for a given dataset. The outcomes demonstrate that the proposed procedure yields a set of features that, when utilized for network traffic classification, yields low quantities of false alarms.

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