• Jasmeet Kaur Ghai
• Susheel Tiwari

Cyberattack detection in cloud computing is a rising concern in the domain of distributed computing. With the increased use of cloud computing in every area of service, threats and attacks in these areas are also growing commensurately. Cyber-attacks in cloud computing have different variants, such as denial of service, probing, and many more flooding behaviours of network traffic. This paper proposes clustering and a classification-based algorithm for the detection of cyberattacks. The proposed algorithm is very efficient in terms of detecting attacks that are normal or abnormal. The machine learning (ML) algorithms that have been used here are support vector machines (SVM) and neural networks (NN). The evaluation metrics used in the comparison of performance are accuracy, recall, f1 score, and area under the Receiver Operating Characteristic Curve. The system obtained 99.4% test accuracy for the proposed algorithm. Though these techniques have the same accuracy, other metrics prove that Random Forest performs comparatively better.

Cloud Computing, intrusion, machine learning, classification

"Machine Learning Based Cyber Security Intrusion Detection in Cloud Computing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 12, page no.e171-e177, December-2023, Available :http://www.jetir.org/papers/JETIR2312423.pdf

"Machine Learning Based Cyber Security Intrusion Detection in Cloud Computing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 12, page no. ppe171-e177, December-2023, Available at : http://www.jetir.org/papers/JETIR2312423.pdf