Snapcatch – Protection of Data from Covert Timing Channels

Abstract

In day-to-day life there is a rapid increase in data exfiltration enabled by digital attacks, Covert Timing Channels (CTC) became an inevitable organisation potential threat that evolves over time in both refinement and applications. These channels use the interval between appearance to collect sensitive data from the authorised networks. CTC detection is increasingly reliant on AI algorithms that use factually based measures to distinguish malicious (undercover) traffic flows from legitimate (obvious) traffic streams. An innovative picture-based solution for entirely computerised CTC identification and limiting is proposed in this paper. This approach is based on the assumption that incognito channels generate traffic that can be converted to shaded images. The solution is designed to intuitively identify and locate the harmful part (i.e., a set of parcels) inside a traffic flow with the help of this perspective. This methodology reduces the loss of nature of administration caused by obstructing the entire traffic streams in which secretive channels are identified by locating undercover parts within traffic streams.