Machine Learning Approaches For Intelligent Intrusion Detection Systems In Cloud Networks

Abstract

Cloud Computing Has Become A Fundamental Platform For Storing Data And Running Applications Due To Its Scalability, Flexibility, And Cost Efficiency. However, The Rapid Growth Of Cloud Environments Has Also Increased Security Threats Such As Unauthorized Access, Malware Attacks, And Distributed Denial-of-service (DDoS) Attacks. Traditional Security Mechanisms Often Struggle To Detect New And Sophisticated Cyber Threats In Dynamic Cloud Infrastructures. To Address These Challenges, Intelligent Intrusion Detection Systems (IDS) Based On Machine Learning (ML) Techniques Have Gained Significant Attention. This Study Explores Various Machine Learning Approaches For Developing Intelligent Intrusion Detection Systems In Cloud Networks. Machine Learning Algorithms Such As Decision Trees, Support Vector Machines (SVM), Random Forest, Naïve Bayes, And Deep Learning Models Are Used To Analyze Network Traffic And Identify Abnormal Patterns That Indicate Potential Intrusions. These Models Are Trained On Network Datasets To Distinguish Between Normal And Malicious Activities With High Accuracy.

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