Martin Zadnik

Vasileios Mavroeidis, Pavel Eis, Martin Zadnik et al.

Motivated by the introduction of CACAO, the first open standard that harmonizes the way we document courses of action in a machine-readable format for interoperability, and the benefits for cybersecurity operations derived from utilizing, and coupling and sharing course of action playbooks with cyber threat intelligence, we introduce a uniform metadata template that supports managing and integrating course of action playbooks into knowledge representation and knowledge management systems. We demonstrate the applicability of our approach through two use-case implementations. We utilize the playbook metadata template to introduce functionality and integrate course of action playbooks, such as CACAO, into the MISP threat intelligence platform and the OASIS Threat Actor Context ontology.

Vaclav Bartos, Martin Zadnik, Sheikh Mahbub Habib et al.

The devastating effects of cyber-attacks, highlight the need for novel attack detection and prevention techniques. Over the last years, considerable work has been done in the areas of attack detection as well as in collaborative defense. However, an analysis of the state of the art suggests that many challenges exist in prioritizing alert data and in studying the relation between a recently discovered attack and the probability of it occurring again. In this article, we propose a system that is intended for characterizing network entities and the likelihood that they will behave maliciously in the future. Our system, namely Network Entity Reputation Database System (NERDS), takes into account all the available information regarding a network entity (e. g. IP address) to calculate the probability that it will act maliciously. The latter part is achieved via the utilization of machine learning. Our experimental results show that it is indeed possible to precisely estimate the probability of future attacks from each entity using information about its previous malicious behavior and other characteristics. Ranking the entities by this probability has practical applications in alert prioritization, assembly of highly effective blacklists of a limited length and other use cases.