A scalable distributed machine learning approach for attack detection in edge computing environments

2018 ◽  
Vol 119 ◽  
pp. 18-26 ◽  
Author(s):  
Rafał Kozik ◽  
Michał Choraś ◽  
Massimo Ficco ◽  
Francesco Palmieri
Keyword(s):  
Machine Learning ◽  
Attack Detection ◽  
Edge Computing ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Computing Environments ◽  
Distributed Machine Learning

scholarly journals A Machine Learning Approach for Backoff Manipulation Attack Detection in Cognitive Radio

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 227349-227359
Author(s):  
Wassim Fassi Fihri ◽  
Hassan El Ghazi ◽  
Badr Abou El Majd ◽  
Faissal El Bouanani
Keyword(s):  
Machine Learning ◽  
Cognitive Radio ◽  
Attack Detection ◽  
Learning Approach ◽  
Machine Learning Approach

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.

MADP-IIME: malware attack detection protocol in IoT-enabled industrial multimedia environment using machine learning approach

2021 ◽  
Author(s):  
Sumit Pundir ◽  
Mohammad S. Obaidat ◽  
Mohammad Wazid ◽  
Ashok Kumar Das ◽  
Devesh Pratap Singh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Attack Detection ◽  
Learning Approach ◽  
Multimedia Environment ◽  
Machine Learning Approach

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.

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