Novel Machine Learning Approach for DDoS Cloud Detection: Bayesian-Based CNN and Data Fusion Enhancements

Author:

AlSaleh Ibtihal1,Al-Samawi Aida1ORCID,Nissirat Liyth1

Affiliation:

1. College of Computer Sciences and Information Technology, Department of Computer Networks, King Faisal University, Al-Ahsa 31982, Saudi Arabia

Abstract

Cloud computing has revolutionized the information technology landscape, offering businesses the flexibility to adapt to diverse business models without the need for costly on-site servers and network infrastructure. A recent survey reveals that 95% of enterprises have already embraced cloud technology, with 79% of their workloads migrating to cloud environments. However, the deployment of cloud technology introduces significant cybersecurity risks, including network security vulnerabilities, data access control challenges, and the ever-looming threat of cyber-attacks such as Distributed Denial of Service (DDoS) attacks, which pose substantial risks to both cloud and network security. While Intrusion Detection Systems (IDS) have traditionally been employed for DDoS attack detection, prior studies have been constrained by various limitations. In response to these challenges, we present an innovative machine learning approach for DDoS cloud detection, known as the Bayesian-based Convolutional Neural Network (BaysCNN) model. Leveraging the CICDDoS2019 dataset, which encompasses 88 features, we employ Principal Component Analysis (PCA) for dimensionality reduction. Our BaysCNN model comprises 19 layers of analysis, forming the basis for training and validation. Our experimental findings conclusively demonstrate that the BaysCNN model significantly enhances the accuracy of DDoS cloud detection, achieving an impressive average accuracy rate of 99.66% across 13 multi-class attacks. To further elevate the model’s performance, we introduce the Data Fusion BaysFusCNN approach, encompassing 27 layers. By leveraging Bayesian methods to estimate uncertainties and integrating features from multiple sources, this approach attains an even higher average accuracy of 99.79% across the same 13 multi-class attacks. Our proposed methodology not only offers valuable insights for the development of robust machine learning-based intrusion detection systems but also enhances the reliability and scalability of IDS in cloud computing environments. This empowers organizations to proactively mitigate security risks and fortify their defenses against malicious cyber-attacks.

Funder

Deanship of Scientific Research, Vice Presidency for Graduate Studies

Publisher

MDPI AG

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Detection and Mitigation of DDoS Attacks : A Review of Robust and Scalable Solutions;International Journal of Scientific Research in Computer Science, Engineering and Information Technology;2024-09-05

2. Improved Whale Optimization Algorithm and Optimized Long Short-Term Memory for DDoS Cyber Security Threat;2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE);2024-04-26

3. Cloud Network Anomaly Detection Using Machine and Deep Learning Techniques— Recent Research Advancements;IEEE Access;2024

4. Collaborative Defense Method Against DDoS Attacks on SDN-Architected Cloud Servers;Lecture Notes in Computer Science;2024

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