A Comparative Study Of Binary Class Logistic Regression and Shallow Neural Network For DDoS Attack Prediction

In the area of internet security, cybersecurity is a serious subject. Every industry is witnessing thousands of cyberattacks every year. Among the most deadly cyber-attacks are the distributed denial-of-service attack (DDOS) and the False data injection attack (FDIA). In this paper, we performed a c...

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Bibliographic Details
Published inProceedings of IEEE Southeastcon pp. 310 - 315
Main Authors Tufail, Shahid, Batool, Shanzeh, Sarwat, Arif I.
Format Conference Proceeding
LanguageEnglish
Published IEEE 26.03.2022
Subjects
Analytical models
Cyber Attacks
Data Packets
DDOS Attacks
Denial-of-service attack
Distributed Denial of Service
DOS Attacks
Logistic Regression
Machine learning
Machine Learning Algorithm
Multi Layer Perceptron
Networks
Neural networks
Predictive models
Software algorithms
Training
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Summary:In the area of internet security, cybersecurity is a serious subject. Every industry is witnessing thousands of cyberattacks every year. Among the most deadly cyber-attacks are the distributed denial-of-service attack (DDOS) and the False data injection attack (FDIA). In this paper, we performed a comparative study for predicting DDOS attacks using two machine learning algorithms that are logistic regression and shallow neural network(SNN). In logistic regression, we achieved an accuracy of 98.63% and for SNN accuracy we achieved was 99.85%. However, our study shows that the training time was exponentially higher for SNN in comparison to logistic regression.
ISSN:1558-058X
DOI:10.1109/SoutheastCon48659.2022.9764108