Improving Detectability of Advanced Persistent Threats (APT) by Use of APT Group Digital Fingerprints

Abstract

Over the last 15 years, cyberattacks have moved from attacking IT systems to targeted attacks on Operational Technology (OT) systems, also known as Cyber–Physical Systems (CPS). The first targeted OT cyberattack was Stuxnet in 2010, at which time the term Advanced Persistent Threat (APT) appeared. An APT often refers to a sophisticated two-stage cyberattack requiring an extensive reconnaissance period before executing the actual attack. Following Stuxnet, a sizable number of APTs have been discovered and documented. APTs are difficult to detect due to the many steps involved, the large number of attacker capabilities that are in use, and the timeline. Such attacks are carried out over an extended time period, sometimes spanning several years, which means that they cannot be recognized using signatures, anomalies, or similar patterns. APTs require detection capabilities beyond what current detection paradigms are capable of, such as behavior-based, signature-based, protocol-based, or other types of Intrusion Detection and Prevention Systems (IDS/IPS). This paper describes steps towards improving the detection of APTs by means of APT group digital fingerprints. An APT group fingerprint is a digital representation of the attacker’s capabilities, their relations and dependencies, and their technical implementation for an APT group. The fingerprint is represented as a directed graph, which models the relationships between the relevant capabilities. This paper describes part of the analysis behind establishing the APT group digital fingerprint for the Russian Cyberspace Operations Group - Sandworm.