Identifying vulnerabilities in trust and reputation systems

Abstract

Online communities use trust and reputation systems to assist their users in evaluating other parties. Due to the preponderance of these systems, malicious entities have a strong incentive to attempt to influence them, and strategies employed are increasingly sophisticated. Current practice is to evaluate trust and reputation systems against known attacks, and hence are heavily reliant on expert analysts. We present a novel method for automatically identifying vulnerabilities in such systems by formulating the problem as a derivative-free optimisation problem and applying efficient sampling methods. We illustrate the application of this method for attacks that involve the injection of false evidence, and identify vulnerabilities in existing trust models. In this way, we provide reliable and objective means to assess how robust trust and reputation systems are to different kinds of attacks.

Automatic Part-of-Speech Tagging for Security Vulnerability Descriptions

“…to tokens in SVD, is challenging due to its content includes high-level natural language descriptions of the text composed of a mixed language studded with codes, domain-specific jargon, vague language, and averviations [1]. In reality, identifying a tokens functional role within a SVD and annotating each of the tokens with POS tags is useful for a wide scope of downstream applications ranging from domain-specific NER [3], [4] to more sophisticated uses such as security vulnerability systems [5]- [7]. Hence, POS tagging is the foundation of many downstream applications and a prerequisite for a wide range of systems [8].…”