Biblio

Modern malware indicators utilized by the current top threat feeds are easily bypassed and generated through enigmatic methods, leading to a lack of detection capabilities for cyber defenders. Static hash-based algorithms such as MD5 or SHA generate indicators that are rendered obsolete by modifying a single byte of the source file. Conversely, fuzzy hash-based algorithms such as SSDEEP and TLSH are more robust to alterations of source information; however, these methods often utilize context boundaries that are hard to define or not based on meaningful information. In previous work, a custom binary analysis tool was created called @DisCo. In this study, four current ransomware campaigns were analyzed using TLSH fuzzy hashing and the @DisCo tool. While TLSH works on the binary level of the entire program, @DisCo works at an intermediate function level. The results from each analysis method were compared to provide validation between the two as well as introduce a narrative for using combinations of these types of methods for the creation of stronger indicators of compromise.

Information security can benefit from real-time cyber threat indicator sharing, in which companies and government agencies share their knowledge of emerging cyberattacks to benefit their sector and society at large. As attacks become increasingly sophisticated by exploiting behavioral dimensions of human computer operators, there is an increased risk to systems that store personal information. In addition, risk increases as individuals blur the boundaries between workplace and home computing (e.g., using workplace computers for personal reasons). This paper describes an architecture to leverage individual perceptions of privacy risk to compute privacy risk scores over cyber threat indicator data. Unlike security risk, which is a risk to a particular system, privacy risk concerns an individual's personal information being accessed and exploited. The architecture integrates tools to extract information entities from textual threat reports expressed in the STIX format and privacy risk estimates computed using factorial vignettes to survey individual risk perceptions. The architecture aims to optimize for scalability and adaptability to achieve real-time risk scoring.