Privacy Aware Memory Forensics
Janardhan Kalikiri, Gaurav Varshney, Jaswinder Kour, Tarandeep Singh

TL;DR
This paper introduces a privacy-preserving memory forensics method that detects insider data leaks by analyzing RAM for sensitive information, balancing security needs with user privacy, demonstrated through a military case study.
Contribution
A novel approach that captures and analyzes RAM to detect insider data leaks without compromising user privacy, utilizing deep learning for context-aware identification.
Findings
Effective detection of sensitive data leaks in RAM
Maintains user privacy during forensic analysis
Validated with a military use case
Abstract
In recent years, insider threats and attacks have been increasing in terms of frequency and cost to the corporate business. The utilization of end-to-end encrypted instant messaging applications (WhatsApp, Telegram, VPN) by malicious insiders raised data breach incidents exponentially. The Securities and Exchange Board of India (SEBI) investigated reports on such data leak incidents and reported about twelve companies where earnings data and financial information were leaked using WhatsApp messages. Recent surveys indicate that 60% of data breaches are primarily caused by malicious insider threats. Especially, in the case of the defense environment, information leaks by insiders will jeopardize the countrys national security. Sniffing of network and host-based activities will not work in an insider threat detection environment due to end-to-end encryption. Memory forensics allows access…
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Taxonomy
TopicsSecurity and Verification in Computing · Advanced Data Storage Technologies · Digital and Cyber Forensics
