Analysis of Machine Learning Techniques for Lightweight DDoS Attack Detection on IoT Networks

2021 ◽  
pp. 96-110
Author(s):  
Eric McCullough ◽  
Razib Iqbal ◽  
Ajay Katangur
Keyword(s):  
Machine Learning ◽  
Attack Detection ◽  
Machine Learning Techniques ◽  
Ddos Attack ◽  
Learning Techniques ◽  
Ddos Attack Detection

scholarly journals Machine-learning and statistical methods for DDoS attack detection and defense system in software defined networks

2021 ◽  
Author(s):  
Merlin James Rukshan Dennis
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Statistical Approach ◽  
Denial Of Service ◽  
Attack Detection ◽  
Learning Approach ◽  
Ddos Attack ◽  
Machine Learning Approach ◽  
Ddos Detection ◽  
Ddos Attack Detection

Distributed Denial of Service (DDoS) attack is a serious threat on today’s Internet. As the traffic across the Internet increases day by day, it is a challenge to distinguish between legitimate and malicious traffic. This thesis proposes two different approaches to build an efficient DDoS attack detection system in the Software Defined Networking environment. SDN is the latest networking approach which implements centralized controller, which is programmable. The central control and the programming capability of the controller are used in this thesis to implement the detection and mitigation mechanisms. In this thesis, two designed approaches, statistical approach and machine-learning approach, are proposed for the DDoS detection. The statistical approach implements entropy computation and flow statistics analysis. It uses the mean and standard deviation of destination entropy, new flow arrival rate, packets per flow and flow duration to compute various thresholds. These thresholds are then used to distinguish normal and attack traffic. The machine learning approach uses Random Forest classifier to detect the DDoS attack. We fine-tune the Random Forest algorithm to make it more accurate in DDoS detection. In particular, we introduce the weighted voting instead of the standard majority voting to improve the accuracy. Our result shows that the proposed machine-learning approach outperforms the statistical approach. Furthermore, it also outperforms other machine-learning approach found in the literature.

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 Industrial Datasets with ICS Testbed and Attack Detection Using Machine Learning Techniques

2022 ◽  
Vol 31 (3) ◽  
pp. 1345-1360
Author(s):  
Sinil Mubarak ◽  
Mohamed Hadi Habaebi ◽  
Md Rafiqul Islam ◽  
Asaad Balla ◽  
Mohammad Tahir ◽  
...  
Keyword(s):  
Machine Learning ◽  
Attack Detection ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Machine-learning-assisted DDoS attack detection with P4 language

2020 ◽  
Author(s):  
Francesco Musumeci ◽  
Valentina Ionata ◽  
Francesco Paolucci ◽  
Filippo Cugini ◽  
Massimo Tornatore
Keyword(s):  
Machine Learning ◽  
Attack Detection ◽  
Ddos Attack ◽  
Ddos Attack Detection

A New Framework for Classification of Distributed Denial of Service (DDOS) Attack in Cloud Computing by Machine Learning Techniques

2014 ◽  
Vol 20 (1) ◽  
pp. 175-178
Author(s):  
Mostafa Behzadi ◽  
Ramlan Mahmod ◽  
Mehdi Barati ◽  
Azizol Bin Hj Abdullah ◽  
Mahda Noura
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Denial Of Service ◽  
Machine Learning Techniques ◽  
Distributed Denial Of Service ◽  
Ddos Attack ◽  
Learning Techniques ◽  
New Framework
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