V2I8P3

Cloud Security Response Based on Coordinated Cryptographic Mechanisms

Shivam Agarwal^1*, Lav Dixit²

Abstract

The internet has become an essential part of daily life, used for education, business, trade, and even by young children. Despite its benefits, it poses significant security threats such as phishing, sniffing, hacking, cracking, denial-of-service attacks, and data theft. These risks create a sense of vulnerability among users, highlighting the need for robust network security. As information technology rapidly advances, the demand for effective security measures also grows. Tools like Intrusion Detection Systems (IDS) play a crucial role in identifying suspicious activities on networks. However, detection alone is not sufficient; prevention is also necessary. To address this, we propose a machine learning-based intrusion detection approach targeting threats like SQL injection, cross-site scripting, and DDoS attacks. By applying models such as Random Forest and General Regression Neural Networks, we achieve 69.10% accuracy in securing critical services—mail servers, databases, file systems, FTP servers- hosted via cloud infrastructure.

Keywords:

Network Security; Intrusion Detection Systems (IDS); Machine Learning; Cyber Threats; Cloud Infrastructure