Provenance-based causal analysis of audit logs has proven to be an invaluable method of investigating system intrusions. However, it also suffers from dependency explosion, whereby long-running processes accumulate many dependencies that are hard to unravel. Execution unit partitioning addresses this by segmenting dependencies into units of work, such as isolating the events that processed a single HTTP request. Unfortunately, we discover that current designs have a semantic gap problem due to how system calls and application log messages are used to infer complex internal program states. We demonstrate how attackers can modify existing code exploits to control event partitioning, breaking links in the attack and framing innocent users. We also show how our techniques circumvent existing program and log integrity defenses.
We then propose a new design for execution unit partitioning that leverages additional runtime data to yield verified partitions that resist manipulation. Our design overcomes the technical challenges of minimizing additional overhead while accurately connecting low level code instructions to high level audit events, in part with the use of commodity hardware processor tracing. We implement a prototype of our design for Linux, MARSARA, and extensively evaluate it on 14 real-world programs, targeted with expertly crafted exploits. MARSARA's verified partitions successfully capture all the attack provenances while only reintroducing 2.82% of false dependencies, in the worst case, with an average overhead of 8.7%. Using a new metric called Partitioning Attack Surface, we show that MARSARA eliminates 47,642 more repartitioning gadgets per program than integrity defenses like CFI, demonstrating our prototype's effectiveness and the novelty of the attacks it prevents.
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A Novel AI-based Methodology for Identifying Cyber Attacks in Honey Pots
e present a novel AI-based methodology that identifies phases of a host-level cyber attack simply from system call logs. System calls emanating from cyber attacks on hosts such as honey pots are often recorded in audit logs. Our methodology first involves efficiently loading, caching, processing, and querying system events contained in audit logs in support of computer forensics. Output of queries remains at the system call level and is difficult to process. The next step is to infer a sequence of abstracted actions, which we colloquially call a storyline, from the system calls given as observations to a latent-state probabilistic model. These storylines are then accurately identified with class labels using a learned classifier. We qualitatively and quantitatively evaluate methods and models for each step of the methodology using 114 different attack phases collected by logging the attacks of a red team on a server, on some likely benign sequences containing regular user activities, and on traces from a recent DARPA project. The resulting end-to-end system, which we call Cyberian, identifies the attack phases with a high level of accuracy illustrating the benefit that this machine learning-based methodology brings to security forensics.
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- Award ID(s):
- 1916550
- NSF-PAR ID:
- 10298208
- Date Published:
- Journal Name:
- Proceedings of the AAAI Conference on Artificial Intelligence
- ISSN:
- 2374-3468
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/https://ojs.aaai.org/index.php/AAAI/article/view/17786
