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.

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 Detecting High and Low Intensity Distributed Denial of Service (DDoS) Attacks

2021 ◽  
Author(s):  
◽  
Abigail Koay
Keyword(s):  
Information Sharing ◽  
Detection System ◽  
Denial Of Service ◽  
Attack Detection ◽  
Traffic Classification ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Low Intensity ◽  
Ddos Attack ◽  
Ddos Attack Detection

<p>High and low-intensity attacks are two common Distributed Denial of Service (DDoS) attacks that disrupt Internet users and their daily operations. Detecting these attacks is important to ensure that communication, business operations, and education facilities can run smoothly. Many DDoS attack detection systems have been proposed in the past but still lack performance, scalability, and information sharing ability to detect both high and low-intensity DDoS attacks accurately and early. To combat these issues, this thesis studies the use of Software-Defined Networking technology, entropy-based features, and machine learning classifiers to develop three useful components, namely a good system architecture, a useful set of features, and an accurate and generalised traffic classification scheme. The findings from the experimental analysis and evaluation results of the three components provide important insights for researchers to improve the overall performance, scalability, and information sharing ability for building an accurate and early DDoS attack detection system.</p>

scholarly journals On the use of information theory metrics for detecting DDoS attacks and flash events: an empirical analysis, comparison, and future directions

2021 ◽  
Vol 48 (4) ◽  
Author(s):  
Jagdeep Singh ◽  
◽  
Navjot Jyoti ◽  
Sunny Behal ◽  
◽  
...  
Keyword(s):  
Information Theory ◽  
Detection System ◽  
Denial Of Service ◽  
Attack Detection ◽  
High Rate ◽  
Ddos Attacks ◽  
Operating Characteristics ◽  
Classification Rate ◽  
Ddos Attack ◽  
Ddos Attack Detection

A Distributed Denial of Service (DDoS) attack is one of the lethal threats that can cripple down the computing and communication resources of a web server hosting Internet-based services and applications. It has motivated the researchers over the years to find diversified and robust solutions to combat against DDoS attacks and characterization of flash events (a sudden surge in the legitimate traffic) from HR-DDoS (High-Rate DDoS) attacks. In recent times, the volume of legitimate traffic has also magnified manifolds. It results in behavioral similarities of attack traffic and legitimate traffic that make it very difficult and crucial to differentiate between the two. Predominantly, Netflow-based techniques are in use for detecting and differentiating legitimate and attack traffic flows. Over the last decade, fellow researchers have extensively used distinct information theory metrics for Netflow-based DDoS defense solutions. However, a comprehensive analysis and comparison of these diversified information theory metrics used for particularly DDoS attack detection are needed for a better understanding of the defense systems based on information theory. This paper elucidates the efficacy and effectiveness of information theory-based various entropy and divergence measures in the field of DDoS attack detection. As part of the work, a generalized NetFlow-based methodology has been proposed. The proposed detection methodology has been validated using the traffic traces of various real benchmarked datasets on a set of detection system evaluation metrics such as Detection rate (Recall), Precision, F-Measure, FPR, Classification rate, and Receiver-Operating Characteristics (ROC) curves. It has concluded that generalized divergence-based information theory metrics produce more accuracy in detecting different types of attack flows in contrast to entropy-based information theory metrics.

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

scholarly journals Detecting High and Low Intensity Distributed Denial of Service (DDoS) Attacks

2021 ◽  
Author(s):  
◽  
Abigail Koay
Keyword(s):  
Information Sharing ◽  
Detection System ◽  
Denial Of Service ◽  
Attack Detection ◽  
Traffic Classification ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Low Intensity ◽  
Ddos Attack ◽  
Ddos Attack Detection

<p>High and low-intensity attacks are two common Distributed Denial of Service (DDoS) attacks that disrupt Internet users and their daily operations. Detecting these attacks is important to ensure that communication, business operations, and education facilities can run smoothly. Many DDoS attack detection systems have been proposed in the past but still lack performance, scalability, and information sharing ability to detect both high and low-intensity DDoS attacks accurately and early. To combat these issues, this thesis studies the use of Software-Defined Networking technology, entropy-based features, and machine learning classifiers to develop three useful components, namely a good system architecture, a useful set of features, and an accurate and generalised traffic classification scheme. The findings from the experimental analysis and evaluation results of the three components provide important insights for researchers to improve the overall performance, scalability, and information sharing ability for building an accurate and early DDoS attack detection system.</p>

High Performance DDoS Attack Detection System Based on Distribution Statistics

2019 ◽  
pp. 132-142
Author(s):  
Xia Xie ◽  
Jinpeng Li ◽  
Xiaoyang Hu ◽  
Hai Jin ◽  
Hanhua Chen ◽  
...  
Keyword(s):  
High Performance ◽  
Detection System ◽  
Attack Detection ◽  
Ddos Attack ◽  
Ddos Attack Detection

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.

Design of DDoS attack detection system based on intelligent bee colony algorithm

2019 ◽  
Vol 19 (2) ◽  
pp. 223
Author(s):  
Gongjun Yin ◽  
Qiuting Tian ◽  
Zhenxin Du ◽  
Xueshan Yu ◽  
Dezhi Han
Keyword(s):  
Detection System ◽  
Attack Detection ◽  
Ddos Attack ◽  
Bee Colony ◽  
Ddos Attack Detection
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