Gang Zhao

h-index16
2papers
917citations

2 Papers

19.6CRFeb 25, 2021
Swivel: Hardening WebAssembly against Spectre

Shravan Narayan, Craig Disselkoen, Daniel Moghimi et al.

We describe Swivel, a new compiler framework for hardening WebAssembly (Wasm) against Spectre attacks. Outside the browser, Wasm has become a popular lightweight, in-process sandbox and is, for example, used in production to isolate different clients on edge clouds and function-as-a-service platforms. Unfortunately, Spectre attacks can bypass Wasm's isolation guarantees. Swivel hardens Wasm against this class of attacks by ensuring that potentially malicious code can neither use Spectre attacks to break out of the Wasm sandbox nor coerce victim code-another Wasm client or the embedding process-to leak secret data. We describe two Swivel designs, a software-only approach that can be used on existing CPUs, and a hardware-assisted approach that uses extension available in Intel 11th generation CPUs. For both, we evaluate a randomized approach that mitigates Spectre and a deterministic approach that eliminates Spectre altogether. Our randomized implementations impose under 10.3% overhead on the Wasm-compatible subset of SPEC 2006, while our deterministic implementations impose overheads between 3.3% and 240.2%. Though high on some benchmarks, Swivel's overhead is still between 9x and 36.3x smaller than existing defenses that rely on pipeline fences.

3.8CVJul 26, 2016
Dynamic Probabilistic Network Based Human Action Recognition

Anne Veenendaal, Eddie Jones, Zhao Gang et al.

This paper examines use of dynamic probabilistic networks (DPN) for human action recognition. The actions of lifting objects and walking in the room, sitting in the room and neutral standing pose were used for testing the classification. The research used the dynamic interrelation between various different regions of interest (ROI) on the human body (face, body, arms, legs) and the time series based events related to the these ROIs. This dynamic links are then used to recognize the human behavioral aspects in the scene. First a model is developed to identify the human activities in an indoor scene and this model is dependent on the key features and interlinks between the various dynamic events using DPNs. The sub ROI are classified with DPN to associate the combined interlink with a specific human activity. The recognition accuracy performance between indoor (controlled lighting conditions) is compared with the outdoor lighting conditions. The accuracy in outdoor scenes was lower than the controlled environment.