Visible to the public Biblio

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Miller, Andrew, Xia, Yu, Croman, Kyle, Shi, Elaine, Song, Dawn.  2016.  The Honey Badger of BFT Protocols. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. :31–42.

The surprising success of cryptocurrencies has led to a surge of interest in deploying large scale, highly robust, Byzantine fault tolerant (BFT) protocols for mission-critical applications, such as financial transactions. Although the conventional wisdom is to build atop a (weakly) synchronous protocol such as PBFT (or a variation thereof), such protocols rely critically on network timing assumptions, and only guarantee liveness when the network behaves as expected. We argue these protocols are ill-suited for this deployment scenario. We present an alternative, HoneyBadgerBFT, the first practical asynchronous BFT protocol, which guarantees liveness without making any timing assumptions. We base our solution on a novel atomic broadcast protocol that achieves optimal asymptotic efficiency. We present an implementation and experimental results to show our system can achieve throughput of tens of thousands of transactions per second, and scales to over a hundred nodes on a wide area network. We even conduct BFT experiments over Tor, without needing to tune any parameters. Unlike the alternatives, HoneyBadgerBFT simply does not care about the underlying network.

Ho, Grant, Leung, Derek, Mishra, Pratyush, Hosseini, Ashkan, Song, Dawn, Wagner, David.  2016.  Smart Locks: Lessons for Securing Commodity Internet of Things Devices. Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security. :461–472.

We examine the security of home smart locks: cyber-physical devices that replace traditional door locks with deadbolts that can be electronically controlled by mobile devices or the lock manufacturer's remote servers. We present two categories of attacks against smart locks and analyze the security of five commercially-available locks with respect to these attacks. Our security analysis reveals that flaws in the design, implementation, and interaction models of existing locks can be exploited by several classes of adversaries, allowing them to learn private information about users and gain unauthorized home access. To guide future development of smart locks and similar Internet of Things devices, we propose several defenses that mitigate the attacks we present. One of these defenses is a novel approach to securely and usably communicate a user's intended actions to smart locks, which we prototype and evaluate. Ultimately, our work takes a first step towards illuminating security challenges in the system design and novel functionality introduced by emerging IoT systems.

Xu, Xiaojun, Liu, Chang, Feng, Qian, Yin, Heng, Song, Le, Song, Dawn.  2017.  Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. :363–376.

The problem of cross-platform binary code similarity detection aims at detecting whether two binary functions coming from different platforms are similar or not. It has many security applications, including plagiarism detection, malware detection, vulnerability search, etc. Existing approaches rely on approximate graph-matching algorithms, which are inevitably slow and sometimes inaccurate, and hard to adapt to a new task. To address these issues, in this work, we propose a novel neural network-based approach to compute the embedding, i.e., a numeric vector, based on the control flow graph of each binary function, then the similarity detection can be done efficiently by measuring the distance between the embeddings for two functions. We implement a prototype called Gemini. Our extensive evaluation shows that Gemini outperforms the state-of-the-art approaches by large margins with respect to similarity detection accuracy. Further, Gemini can speed up prior art's embedding generation time by 3 to 4 orders of magnitude and reduce the required training time from more than 1 week down to 30 minutes to 10 hours. Our real world case studies demonstrate that Gemini can identify significantly more vulnerable firmware images than the state-of-the-art, i.e., Genius. Our research showcases a successful application of deep learning on computer security problems.