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Choi, Hongjun, Lee, Wen-Chuan, Aafer, Yousra, Fei, Fan, Tu, Zhan, Zhang, Xiangyu, Xu, Dongyan, Deng, Xinyan.  2018.  Detecting Attacks Against Robotic Vehicles: A Control Invariant Approach. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. :801–816.
Robotic vehicles (RVs), such as drones and ground rovers, are a type of cyber-physical systems that operate in the physical world under the control of computing components in the cyber world. Despite RVs' robustness against natural disturbances, cyber or physical attacks against RVs may lead to physical malfunction and subsequently disruption or failure of the vehicles' missions. To avoid or mitigate such consequences, it is essential to develop attack detection techniques for RVs. In this paper, we present a novel attack detection framework to identify external, physical attacks against RVs on the fly by deriving and monitoring Control Invariants (CI). More specifically, we propose a method to extract such invariants by jointly modeling a vehicle's physical properties, its control algorithm and the laws of physics. These invariants are represented in a state-space form, which can then be implemented and inserted into the vehicle's control program binary for runtime invariant check. We apply our CI framework to eleven RVs, including quadrotor, hexarotor, and ground rover, and show that the invariant check can detect three common types of physical attacks – including sensor attack, actuation signal attack, and parameter attack – with very low runtime overhead.
Kwon, Yonghwi, Kim, Dohyeong, Sumner, William Nick, Kim, Kyungtae, Saltaformaggio, Brendan, Zhang, Xiangyu, Xu, Dongyan.  2016.  LDX: Causality Inference by Lightweight Dual Execution. Proceedings of the Twenty-First International Conference on Architectural Support for Programming Languages and Operating Systems. :503–515.

Causality inference, such as dynamic taint anslysis, has many applications (e.g., information leak detection). It determines whether an event e is causally dependent on a preceding event c during execution. We develop a new causality inference engine LDX. Given an execution, it spawns a slave execution, in which it mutates c and observes whether any change is induced at e. To preclude non-determinism, LDX couples the executions by sharing syscall outcomes. To handle path differences induced by the perturbation, we develop a novel on-the-fly execution alignment scheme that maintains a counter to reflect the progress of execution. The scheme relies on program analysis and compiler transformation. LDX can effectively detect information leak and security attacks with an average overhead of 6.08% while running the master and the slave concurrently on separate CPUs, much lower than existing systems that require instruction level monitoring. Furthermore, it has much better accuracy in causality inference.

Pei, Kexin, Gu, Zhongshu, Saltaformaggio, Brendan, Ma, Shiqing, Wang, Fei, Zhang, Zhiwei, Si, Luo, Zhang, Xiangyu, Xu, Dongyan.  2016.  HERCULE: Attack Story Reconstruction via Community Discovery on Correlated Log Graph. Proceedings of the 32Nd Annual Conference on Computer Security Applications. :583–595.

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.

Wang, Fei, Kwon, Yonghwi, Ma, Shiqing, Zhang, Xiangyu, Xu, Dongyan.  2018.  Lprov: Practical Library-Aware Provenance Tracing. Proceedings of the 34th Annual Computer Security Applications Conference. :605-617.

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.

Xu, Dongyan.  2014.  Virtualization and Security: Happily Ever After? Proceedings of the 4th ACM Conference on Data and Application Security and Privacy. :73–74.

Virtualization has been a major enabling technology for improving trustworthiness and tamper-resistance of computer security functions. In the past decade, we have witnessed the development of virtualization-based techniques for attack/malware monitoring, detection, prevention, and profiling. Virtual platforms have been widely adopted for system security experimentation and evaluation, because of their strong isolation, maneuverability, and scalability properties. Conversely, the demand from security research has led to significant advances in virtualization technology itself, for example, in the aspects of virtual machine introspection, check-pointing, and replay. In this talk, I will present an overview of research efforts (including our own) in virtualization-based security and security-driven virtualization. I will also discuss a number of challenges and opportunities in maintaining and elevating the synergies between virtualization and security.