A Feature Similarity Machine Learning Model for DDoS Attack Detection in Modern Network Environments for Industry 4.0. (May 2022)

• Record Type: Journal Article
• Authors: Sambangi, Swathi; Gondi, Lakshmeeswari; Aljawarneh, Shadi
• Is Part Of: Computers & electrical engineering. Volume 100(2022)
• Publication Date: 2022-05
• Subjects: Machine learning -- Cloud security -- DDoS Attack -- DoS attack -- Industry 4.0 -- Early detection -- Botnet -- Feature transformation -- Classification; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.107955
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A new traffic attribute-pattern similarity function SWATHI for similarity computation between network traffic attribute patterns and evolutionary feature clustering of traffic attribute patterns to carry feature transformation-based dimensionality reduction. A Gaussian based network traffic similarity function for similarity computation between network traffic instances. A machine learning model SWASTHIKA which considers network traffic data obtained after feature transformation for detection of low rate and high-rate network attacks. ABSTRACT: Recent advancements in artificial intelligence and machine learning technologies have laid the flagstone for the fourth industrial revolution, Industry 4.0. The industry 4.0 is at a very high momentum when compared to previous revolutions witnessed by humans in a way which was never anticipated. Cyber Physical Systems and Cloud computing are the basis for Industry 4.0. An ongoing research challenge in cloud computing is the immediate need to address security and data availability challenges coined in modern networking environments. For instance, DDoS attacks in cloud are continuously throwing new challenges to network community which makes detection of these attacks, an ongoing research challenge with respect to cloud security. At the outset, the research reported in this work has addressed three important contributions (i) A new gaussian based traffic attribute-pattern similarity function for evolutionary feature clustering to achieve feature transformation-based dimensionality reduction, (ii) A Gaussian based network traffic similarity function for similarity computation between network traffic instances and (iii) A machine learning model SWASTHIKA which uses feature transformation traffic for detection of low rate and high-rate network attacks. For experimental study, the most recent benchmark dataset namely IoT DoS and DDoS attack dataset available at IEEE Dataport is considered as this dataset has highly non-linear traffic instances which are like the real-world traffic. The performance evaluation of the proposed machine learning model SWASTHIKA is done by considering various classifier evaluation parameters such as accuracy, precision, detection rate, and F-Score. The experiment results proved that the attack detection rate of SWASTHIKA is significantly better compared to state of art machine learning classifiers. Graphical Abstract: Image, graphical abstract