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.

DDOS Attack Detection Strategies in Cloud A Comparative Stud

2017 ◽  
pp. 35-42
Author(s):  
◽  
◽  
Keyword(s):  
Information Technology ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Huge Number ◽  
Ddos Attack ◽  
Ddos Attack Detection ◽  
Detection Strategies ◽  
Hot Target

Cloud is known as a highly-available platform that has become most popular among businesses for all information technology needs. Being a widely used platform, it’s also a hot target for cyber-attacks. Distributed Denial of Services (DDoS) is a great threat to a cloud in which cloud bandwidth, resources, and applications are attacked to cause service unavailability. In a DDoS attack, multiple botnets attack victim using spoofed IPs with a huge number of requests to a server. Since its discovery in 1980, numerous methods have been proposed for detection and prevention of network anomalies. This study provides a background of DDoS attack detection methods in past decade and a survey of some of the latest proposed strategies to detect DDoS attacks in the cloud, the methods are further compared for their detection accuracy.

scholarly journals A Feature Extraction Method for P2P Botnet Detection Using Graphic Symmetry Concept

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 326 ◽  
Author(s):  
Zhixian Yang ◽  
Buhong Wang
Keyword(s):  
Machine Learning ◽  
Information Entropy ◽  
Feature Detection ◽  
Denial Of Service ◽  
Attack Detection ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
Feature Extraction Method ◽  
Denial Of Service Attack ◽  
Ddos Attack

A DDoS (Distributed Denial of Service) attack makes use of a botnet to launch attacks and cause node congestion of wireless sensor networks, which is a common and serious threat. Due to the various kinds of features required in a Peer-to-Peer (P2P) botnet for DDoS attack detection via current machine learning methods and the failure to effectively detect encrypted botnets, this paper extracts the data packet size and the symmetric intervals in flow according to the concept of graphic symmetry. Combined with flow information entropy and session features, the frequency domain features can be sorted so as to obtain features with better correlations, which solves the problem of multiple types of features required for detection. Information entropy corresponding to the flow size can distinguish an encrypted botnet. This method is implemented through machine learning techniques. Experimental results show that the proposed method can detect the P2P botnet for DDoS attack and the detection accuracy is higher than that of traditional feature detection.

scholarly journals DDoS Attack Detection and Classification using Machine Learning Models with Real-Time Dataset Created

2021 ◽  
Vol 9 (5) ◽  
pp. 145-153
Author(s):  
Harrsheeta Sasikumar
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Denial Of Service ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Detection Accuracy ◽  
Ddos Attack ◽  
Real Time Traffic ◽  
Server Breakdown ◽  
Networked Devices

Distributed Denial of Service (DDoS) attack is one of the common attack that is predominant in the cyber world. DDoS attack poses a serious threat to the internet users and affects the availability of services to legitimate users. DDOS attack is characterized by the blocking a particular service by paralyzing the victim’s resources so that they cannot be used to legitimate purpose leading to server breakdown. DDoS uses networked devices into remotely controlled bots and generates attack. The proposed system detects the DDoS attack and malware with high detection accuracy using machine learning algorithms. The real time traffic is generated using virtual instances running in a private cloud. The DDoS attack is detected by considering the various SNMP parameters and classifying using machine learning technique like bagging, boosting and ensemble models. Also, the various types of malware on the networked devices are prevent from being used as a bot for DDOS attack generation.

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.

Euclid: A Fully In-Network, P4-based Approach for Real-Time DDoS Attack Detection and Mitigation

2020 ◽  
pp. 1-1
Author(s):  
Alexandre da Silveira Ilha ◽  
Angelo Cardoso Lapolli ◽  
Jonatas Adilson Marques ◽  
Luciano Paschoal Gaspary
Keyword(s):  
Real Time ◽  
Attack Detection ◽  
Ddos Attack ◽  
Ddos Attack Detection

Real-time DDoS attack detection using FPGA

2017 ◽  
Vol 110 ◽  
pp. 48-58 ◽  
Author(s):  
N. Hoque ◽  
H. Kashyap ◽  
D.K. Bhattacharyya
Keyword(s):  
Real Time ◽  
Attack Detection ◽  
Ddos Attack ◽  
Ddos Attack Detection
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