Development Machine Learning Techniques to Enhance Cyber Security Algorithms.
Mohamed Amer, G., Abd El Hay, E., Abdel-Baset, I., Abd El Azim Mohamed, M. (2021). Development Machine Learning Techniques to Enhance Cyber Security Algorithms.. EKB Journal Management System, 46(4), 36-46. doi: 10.21608/bfemu.2021.206401
Ghada Mohamed Amer; Ehab Hany Abd El Hay; Ibrahim Yasser Abdel-Baset; Mohamed Abd El Azim Mohamed. "Development Machine Learning Techniques to Enhance Cyber Security Algorithms.". EKB Journal Management System, 46, 4, 2021, 36-46. doi: 10.21608/bfemu.2021.206401
Mohamed Amer, G., Abd El Hay, E., Abdel-Baset, I., Abd El Azim Mohamed, M. (2021). 'Development Machine Learning Techniques to Enhance Cyber Security Algorithms.', EKB Journal Management System, 46(4), pp. 36-46. doi: 10.21608/bfemu.2021.206401
Mohamed Amer, G., Abd El Hay, E., Abdel-Baset, I., Abd El Azim Mohamed, M. Development Machine Learning Techniques to Enhance Cyber Security Algorithms.. EKB Journal Management System, 2021; 46(4): 36-46. doi: 10.21608/bfemu.2021.206401

Development Machine Learning Techniques to Enhance Cyber Security Algorithms.

MEJ- Mansoura Engineering Journal
Article 21, Volume 46, Issue 4, December 2021, Page 36-46  XML PDF (1.29 MB)
Document Type: Research Studies
DOI: 10.21608/bfemu.2021.206401
View on SCiNiTO View on SCiNiTO
Authors
Ghada Mohamed Amer email 1; Ehab Hany Abd El Hay2; Ibrahim Yasser Abdel-Baset3; Mohamed Abd El Azim Mohamedorcid 4
1Communications Engineer at North Delta Electricity Distribution Company, Faculty of Engineering, Mansoura University.
2Assistant Prof., Faculty of Engineering, Mansoura University.
3Assistant Lecturer, Nile Higher Institute for Engineering and Technology. Faculty of Engineering, Mansoura University.
4chief, Dean of the College and Chairman of the Board of Directors, Faculty of Engineering, Mansoura University.
Abstract
Nowadays, Cyber security threats are a growing global problem. As technology evolves, cyber threats, including cyber-hacking threats, and cybercrime organizing groups, are on the rise. Distributed Denial of Service (DDoS) is one of the most serious attacks faced by Cloud computing. This attack aims to make cloud services unavailable to end-users by exhausting system resources, resulting in heavy losses that pose a threat to national security and information security assets, and thus making the development of defensive solutions against such attacks necessary to expand the use of Cloud computing technology. Machine learning (ML) has promising results in detecting cyber-attacks including DDoS when applied to intrusion detection systems. In this research, the proposed system was built using Random forest (RF) is supervised machine learning algorithm, which is an ensemble learning method that operates by constructing a multitude of decision trees at training time. The experiments conducted using the most common and standard data sets, NSL-KDD, and CICIDS 2017, achieved a detection accuracy of up to 99.09% for the first dataset and 99.97% for the second dataset respectively. The proposed system performs well when compared to other methods in terms of accuracy, detection rate, and low false-positive rate.
Keywords
Cloud computing; DDoS attacks; Machine Learning; Random forest; and Weka
Statistics
Article View: 391
PDF Download: 335