Biblio

With the continuing evolution of sophisticated APT attacks, provenance tracking is becoming an important technique for efficient attack investigation in enterprise networks. Most of existing provenance techniques are operating on system event auditing that discloses dependence relationships by scrutinizing syscall traces. Unfortunately, such auditing-based provenance is not able to track the causality of another important dimension in provenance, the shared libraries. Different from other data-only system entities like files and sockets, dynamic libraries are linked at runtime and may get executed, which poses new challenges in provenance tracking. For example, library provenance cannot be tracked by syscalls and mapping; whether a library function is called and how it is called within an execution context is invisible at syscall level; linking a library does not promise their execution at runtime. Addressing these challenges is critical to tracking sophisticated attacks leveraging libraries. In this paper, to facilitate fine-grained investigation inside the execution of library binaries, we develop Lprov, a novel provenance tracking system which combines library tracing and syscall tracing. Upon a syscall, Lprov identifies the library calls together with the stack which induces it so that the library execution provenance can be accurately revealed. Our evaluation shows that Lprov can precisely identify attack provenance involving libraries, including malicious library attack and library vulnerability exploitation, while syscall-based provenance tools fail to identify. It only incurs 7.0% (in geometric mean) runtime overhead and consumes 3 times less storage space of a state-of-the-art provenance tool.

Advanced cyber attacks consist of multiple stages aimed at being stealthy and elusive. Such attack patterns leave their footprints spatio-temporally dispersed across many different logs in victim machines. However, existing log-mining intrusion analysis systems typically target only a single type of log to discover evidence of an attack and therefore fail to exploit fundamental inter-log connections. The output of such single-log analysis can hardly reveal the complete attack story for complex, multi-stage attacks. Additionally, some existing approaches require heavyweight system instrumentation, which makes them impractical to deploy in real production environments. To address these problems, we present HERCULE, an automated multi-stage log-based intrusion analysis system. Inspired by graph analytics research in social network analysis, we model multi-stage intrusion analysis as a community discovery problem. HERCULE builds multi-dimensional weighted graphs by correlating log entries across multiple lightweight logs that are readily available on commodity systems. From these, HERCULE discovers any "attack communities" embedded within the graphs. Our evaluation with 15 well known APT attack families demonstrates that HERCULE can reconstruct attack behaviors from a spectrum of cyber attacks that involve multiple stages with high accuracy and low false positive rates.

Modern software systems are becoming increasingly complex, relying on a lot of third-party library support. Library behaviors are hence an integral part of software behaviors. Analyzing them is as important as analyzing the software itself. However, analyzing libraries is highly challenging due to the lack of source code, implementation in different languages, and complex optimizations. We observe that many Java library functions provide excellent documentation, which concisely describes the functionalities of the functions. We develop a novel technique that can construct models for Java API functions by analyzing the documentation. These models are simpler implementations in Java compared to the original ones and hence easier to analyze. More importantly, they provide the same functionalities as the original functions. Our technique successfully models 326 functions from 14 widely used Java classes. We also use these models in static taint analysis on Android apps and dynamic slicing for Java programs, demonstrating the effectiveness and efficiency of our models.