An Ensemble Framework for Flow-Based Application Layer DDoS Attack Detection Using Data Mining Techniques

2020 ◽  
pp. 9-19 ◽  
Author(s):  
K. Munivara Prasad ◽  
V. Samba Siva ◽  
J. Nagamuneiah ◽  
Siddaiah Nelaballi
Keyword(s):  
Data Mining ◽  
Attack Detection ◽  
Application Layer ◽  
Data Mining Techniques ◽  
Ddos Attack ◽  
Using Data ◽  
Ddos Attack Detection

scholarly journals Review on EM-CURE Algorithm for Detection DDOS Attack

2018 ◽  
Vol 7 (01) ◽  
pp. 23386-23489
Author(s):  
Miss Priyanka P. Narode ◽  
Prof I.R. Shaikh
Keyword(s):  
Data Mining ◽  
Network Flow ◽  
Denial Of Service ◽  
Attack Detection ◽  
Cyber Attack ◽  
Data Mining Technique ◽  
Network Resource ◽  
Data Mining Techniques ◽  
Ddos Attack ◽  
Efficient Detection

Distributed Denial of Service attack (DoS attack) is a cyber attack where the perpetrator seeks to make a machine or network resource unavailable to its intended users by temporarily or indefinitely disrupting services of a host connected to the internet. Denial of service is typically accomplished by flooding the targeted machine or resource with superfluous requests in an attempt to overload systems and prevent some or all legitimate requests from being fulfilled. It is necessary to analyze the fundamental features of DDoS attacks because these attacks can easily vary the used port/protocol, or operation method because they are designed to restricted applications on limited environments.DDoS attack detection very difficult because the non-existence of predefined rules to correctly identify the genuine network flow. A combination of unsupervised data mining techniques as IDS are introduced. The Entropy Method concept in term of windowing the incoming packets is applied with data mining technique using Clustering Using Representative (CURE) as cluster analysis to detect the DDoS attack in network flow. The data is mainly collected from datasets. The CURE DDoS attack detection technique based on entropy gives a promising way to analyze this attack and construct an efficient detection model using a clustering data mining techniques. This approach has been evaluated and compared with several existing approaches in terms of accuracy, false alarm rate, detection rate, F. measure and Phi coefficient.

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