1. Security threat analysis and countermeasure using ML in cloud.
- Author
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Kushwaha, Akhilesh, Patel, Warish, Koyuncu, Hakan, Parikh, Swapnil, and Chauhan, Ankit
- Subjects
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ARTIFICIAL neural networks , *CONVOLUTIONAL neural networks , *DEEP learning , *RECURRENT neural networks , *DENIAL of service attacks , *SQL - Abstract
The rise of cyber-attacks such as Distributed Denial of Service (DDoS) and SQL injection has become a significant concern for organizations and individuals who rely on the internet. Traditional detection methods have become increasingly ineffective in addressing these challenges, necessitating the development of new and innovative solutions. This paper proposes using Convolutional Neural Networks (CNNs) to detect DDoS and SQL injection attacks. Our paper proposes a Convolutional Neural Network model with a multi-layer neural and relu activation function optimizer that reaches a higher degree of precision than previous Deep Learning models. In this study, we tested the relatively new dataset CIC-IDS-2018, which contains different types of attacks. With this dataset, our model achieves an unprecedented accuracy of>96%, minimizing computational time. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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