Xinyang Ge

Ya Xiao, Salman Ahmed, Wenjia Song et al.

Despite recent research efforts, the vision of automatic code generation through API recommendation has not been realized. Accuracy and expressiveness challenges of API recommendation needs to be systematically addressed. We present a new neural network-based approach, Multi-HyLSTM for API recommendation --targeting cryptography-related code. Multi-HyLSTM leverages program analysis to guide the API embedding and recommendation. By analyzing the data dependence paths of API methods, we train embedding and specialize a multi-path neural network architecture for API recommendation tasks that accurately predict the next API method call. We address two previously unreported programming language-specific challenges, differentiating functionally similar APIs and capturing low-frequency long-range influences. Our results confirm the effectiveness of our design choices, including program-analysis-guided embedding, multi-path code suggestion architecture, and low-frequency long-range-enhanced sequence learning, with high accuracy on top-1 recommendations. We achieve a top-1 accuracy of 91.41% compared with 77.44% from the state-of-the-art tool SLANG. In an analysis of 245 test cases, compared with the commercial tool Codota, we achieve a top-1 recommendation accuracy of 88.98%, which is significantly better than Codota's accuracy of 64.90%. We publish our data and code as a large Java cryptographic code dataset.

Xinyang Ge, Hayawardh Vijayakumar, Trent Jaeger

Many smartphones now deploy conventional operating systems, so the rootkit attacks so prevalent on desktop and server systems are now a threat to smartphones. While researchers have advocated using virtualization to detect and prevent attacks on operating systems (e.g., VM introspection and trusted virtual domains), virtualization is not practical on smartphone systems due to the lack of virtualization support and/or the expense of virtualization. Current smartphone processors do have hardware support for running a protected environment, such as the ARM TrustZone extensions, but such hardware does not control the operating system operations sufficiently to enable VM introspection. In particular, a conventional operating system running with TrustZone still retains full control of memory management, which a rootkit can use to prevent traps on sensitive instructions or memory accesses necessary for effective introspection. In this paper, we present SPROBES, a novel primitive that enables introspection of operating systems running on ARM TrustZone hardware. Using SPROBES, an introspection mechanism protected by TrustZone can instrument individual operating system instructions of its choice, receiving an unforgeable trap whenever any SPROBE is executed. The key challenge in designing SPROBES is preventing the rootkit from removing them, but we identify a set of five invariants whose enforcement is sufficient to restrict rootkits to execute only approved, SPROBE-injected kernel code. We implemented a proof-of-concept version of SPROBES for the ARM Fast Models emulator, demonstrating that in Linux kernel 2.6.38, only 12 SPROBES are sufficient to enforce all five of these invariants. With SPROBES we show that it is possible to leverage the limited TrustZone extensions to limit conventional kernel execution to approved code comprehensively.