AI-Driven Insider Threat Detection and Secure Data Transfer Using Hybrid Cryptography
Alt Text: AI-Driven Insider Threat Detection and Secure Data Transfer Using Hybrid Cryptography
Title: AI-Driven Insider Threat Detection and Secure Data Transfer Using Hybrid Cryptography
Caption: Leveraging AI-driven anomaly detection and hybrid cryptography for enhanced cybersecurity.
Description: This research presents a novel approach to insider threat detection and secure data transfer using AI-driven anomaly detection and hybrid cryptography techniques.
Keywords: AI Security, Insider Threat Detection, Hybrid Cryptography, Secure Data Transfer, Cybersecurity
International Journal of Engineering and Techniques – Volume 10 Issue 5, October 2024
Winner Pulakhandam (Personify Inc, Texas, USA) wpulakhandam.rnd@gmail.com
Vallu Visrutatma Rao (Insmed Incorporated, Texas, USA) visrutatmaraovallu@gmail.com
Vamshi Krishna Samudrala (American Airlines, Texas, USA) samudralavamshi0309@gmail.com
R. Hemnath (Assistant Professor, Department of Computer Science, Sri Ramakrishna Mission Vidyalaya College of Arts and Science, Coimbatore) hemnathmca@gmail.com
Abstract
Insider attacks remain a major cybersecurity challenge, leading to data breaches and financial losses. Traditional detection approaches often fail to identify complex insider activity. This research integrates AI-driven anomaly detection with hybrid cryptography to enhance insider threat detection while ensuring secure and efficient data transmission in sensitive organizational environments.
Keywords
AI Security, Insider Threat Detection, Hybrid Cryptography, Secure Data Transfer, Cybersecurity
How to Cite
Winner Pulakhandam, Vallu Visrutatma Rao, Vamshi Krishna Samudrala, R. Hemnath, “AI-Driven Insider Threat Detection and Secure Data Transfer Using Hybrid Cryptography,” International Journal of Engineering and Techniques, Volume 10, Issue 5, 2024. ISSN 2395-1303.
Post Comment