Detecting Insider Attacks with Video Websites Using Distributed Image Steganalysis (Abstract Only)

The safety of information inside of cloud networks is of interest to the network administrators. In a new insider attack, inside attackers merge confidential information with videos using digital video steganography. The video can be uploaded to video websites, where the information can be distributed online, where it can cost firms millions in damages. Standard behavior based exfiltration detection does not always prevent these attacks. This form of steganography is almost invisible. Existing compressed video steganalysis only detects small-payload watermarks. We develop such a strategy using distributed algorithms and classify videos, then compare existing algorithms to new ones. We find our approach improves on behavior based exfiltration detection, and on the existing online video steganalysis.