Sadegh M. Milajerdi

Sadegh M. Milajerdi, Birhanu Eshete, Rigel Gjomemo et al.

Cyber threat intelligence (CTI) is being used to search for indicators of attacks that might have compromised an enterprise network for a long time without being discovered. To have a more effective analysis, CTI open standards have incorporated descriptive relationships showing how the indicators or observables are related to each other. However, these relationships are either completely overlooked in information gathering or not used for threat hunting. In this paper, we propose a system, called POIROT, which uses these correlations to uncover the steps of a successful attack campaign. We use kernel audits as a reliable source that covers all causal relations and information flows among system entities and model threat hunting as an inexact graph pattern matching problem. Our technical approach is based on a novel similarity metric which assesses an alignment between a query graph constructed out of CTI correlations and a provenance graph constructed out of kernel audit log records. We evaluate POIROT on publicly released real-world incident reports as well as reports of an adversarial engagement designed by DARPA, including ten distinct attack campaigns against different OS platforms such as Linux, FreeBSD, and Windows. Our evaluation results show that POIROT is capable of searching inside graphs containing millions of nodes and pinpoint the attacks in a few minutes, and the results serve to illustrate that CTI correlations could be used as robust and reliable artifacts for threat hunting.

Sadegh M. Milajerdi, Birhanu Eshete, Rigel Gjomemo et al.

Kernel audit logs are an invaluable source of information in the forensic investigation of a cyber-attack. However, the coarse granularity of dependency information in audit logs leads to the construction of huge attack graphs which contain false or inaccurate dependencies. To overcome this problem, we propose a system, called ProPatrol, which leverages the open compartmentalized design in families of enterprise applications used in security-sensitive contexts (e.g., browser, chat client, email client). To achieve its goal, ProPatrol infers a model for an application's high-level tasks as input-processing compartments using purely the audit log events generated by that application. The main benefit of this approach is that it does not rely on source code or binary instrumentation, but only on a preliminary and general knowledge of an application's architecture to bootstrap the analysis. Our experiments with enterprise-level attacks demonstrate that ProPatrol significantly cuts down the forensic investigation effort and quickly pinpoints the root- cause of attacks. ProPatrol incurs less than 2% runtime overhead on a commodity operating system.

Sadegh M. Milajerdi, Rigel Gjomemo, Birhanu Eshete et al.

In this paper, we present HOLMES, a system that implements a new approach to the detection of Advanced and Persistent Threats (APTs). HOLMES is inspired by several case studies of real-world APTs that highlight some common goals of APT actors. In a nutshell, HOLMES aims to produce a detection signal that indicates the presence of a coordinated set of activities that are part of an APT campaign. One of the main challenges addressed by our approach involves developing a suite of techniques that make the detection signal robust and reliable. At a high-level, the techniques we develop effectively leverage the correlation between suspicious information flows that arise during an attacker campaign. In addition to its detection capability, HOLMES is also able to generate a high-level graph that summarizes the attacker's actions in real-time. This graph can be used by an analyst for an effective cyber response. An evaluation of our approach against some real-world APTs indicates that HOLMES can detect APT campaigns with high precision and low false alarm rate. The compact high-level graphs produced by HOLMES effectively summarizes an ongoing attack campaign and can assist real-time cyber-response operations.