Towards mechanisms for detection and prevention of data exfiltration by insiders: keynote talk paper
Proceedings of the 6th ACM Symposium on Information, Computer and Communications Security
A framework for avoiding steganography usage over HTTP
Journal of Network and Computer Applications
Detecting data theft using stochastic forensics
Digital Investigation: The International Journal of Digital Forensics & Incident Response
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Detecting and mitigating insider threat is a critical element in the overall information protection strategy. By successfully implementing tactics to detect this threat, organizations mitigate the loss of sensitive information and also potentially protect against future attacks. Within the broader scope of mitigating insider threat, we focus on detecting exfiltration of sensitive data through a protected network. We propose a multilevel framework called SIDD (Sensitive Information Dissemination Detection) system which is a high-speed transparent network bridge located at the edge of the protected network. SIDD consists of three main components: 1) network-level application identification, 2) content signature generation and detection, and 3) covert communication detection. Further, we introduce a model implementation of the key components, demonstrating how our system can be deployed. Our approach is based on the application of statistical and signal processing techniques on traffic flow to generate signatures and/or extract features for classification purposes. The proposed framework aims to address methods to detect, deter and prevent deliberate and unintended distribution of sensitive content outside the organization using the organization's system and network resources by a trusted insider.