HOLMES: Real-time APT Detection through Correlation of Suspicious Information Flows
| Autor: | Milajerdi, Sadegh M., Gjomemo, Rigel, Eshete, Birhanu, Sekar, R., Venkatakrishnan, V. N. |
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| Rok vydání: | 2018 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: The final version of this paper will appear in the proceedings of the 40th IEEE Symposium on Security and Privacy in May 2019 (https://www.ieee-security.org/TC/SP2019/) |
| Databáze: | arXiv |
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