Shiqing Ma- Research Statement

My research interest lies in solving security and software engineering problems via program analysis, with the focus on challenges caused by the opaqueness in modern complex computing systems, which may consist of various operating systems, virtual machines, containers, data processing procedures and artificial intelligence (AI) models. Due to their complexity, it is highly challenging to understand their internal workings. Such opaqueness is the root cause of many security and software engineering problems. I have developed novel techniques to make these systems transparent through the unique program analysis perspective. The key observation is that the various components in such systems are essentially programs. For example, OS can be considered a gigantic event-driven program and an AI model is usually a Python program dominated by matrix computation. My techniques fall into three categories: system operation transparency, data processing transparency and deep learning transparency. System operation transparency is critical for system security and problem diagnosis. For example, advanced persistent threats (APTs) are highly sophisticated, multi-staged attacks that act slowly and deliberately over a long period of time to expand their presence in an enterprise environment and reach their goals. However, the opaqueness of enterprise systems makes it difficult to infer causality between operations (e.g., sensitive files being stolen and visiting a malicious website a few weeks ago) so that attacks cannot be understood and defended. I have developed novel techniques [4, 9, 12, 13, 14, 15, 18, 19] that log critical system/application events that are semantically rich to provide system level transparency. My research outcomes have been published on USENIX Security, NDSS, USENIX ATC, ACSAC and so on, and received the Distinguished Paper Awards from the USENIX Security Symposium 2017 and ISOC NDSS 2016. My techniques reduce the run time overhead of logging system operations from over 40% to less than 7% and space overhead from 155.5 GB for 3 months to 1.6 GB. My techniques have been extensively evaluated in the DARPA Transparent Computing program and the ONR Learn-2-Reason program and produced good results. Some of them [12] are in the process of being integrated to Redhat Linux and some [15] were shipped to the Sandia National Laboratories. My tools [13, 15] are also being integrated to the Navy Trident Warrior training program aiming to train Navy soldiers to defend cyber attacks. Data processing transparency is critical for the trustworthy of processing results. Many security applications such as spam filtering and social network malicious community (e.g., zombie fans and spam accounts) analysis heavily rely on data processing. Defects in data processing procedures could lead to security bleaches and bogus findings that could lead to waste of resources. I developed novel techniques to provide transparency to data processing by on-the-fly partial derivative computation and probabilistic inference. Results were published on top-tier conferences such as ICSE and FSE [8, 20]. My techniques successfully identified defects in six existing spam filters and improved accuracy from 50%-70% to over 96%. Deep learning transparency is critical for deep neuron network (DNN) security (e.g., detecting adversarial samples) and accuracy. Without understanding DNN internals, it is hard to understand the causes of adversarial samples and low model accuracy. DNN models are essentially programs consisting of a sequence of matrix computations and activation functions. With the support of my advisors, I initiated and supervised a group of four other Ph.D. students to address the deep learning transparency problem from the program analysis perspective, and we have achieved many state-of-the-art results [7, 10, 11, 17]. In the security domain, my technique [11] (NDSS’19) uses DNN invariants (intuitively, neuron activation patterns of benign inputs) for adversarial sample detection and achieves over 96% detection accuracy on 11 attacks and 13 different models, outperforming many other state-of-the-art techniques, and our model hardening technique [17] (NeurIPS’18 Spotlight) rejects inputs if the model does not make predication based on appropriate features (e.g., human eyes/noses/mouths in the face recognition task) and is able to filter out 94% adversarial samples for 7 different tested attacks. In the model accuracy domain, my technique [10] (FSE’18) performs differential analysis to identify the root cause of low test accuracy and input selection to resolve the problem. Our evaluation shows that this method improves model accuracy from 75% to 93% on average for 20 models whereas the state-of-the-art can only improve to 85% with 11 times longer training time. These techniques have attracted world wide collaborations with PSU, Univ. of Minnesota-Twin Cities, HKUST, HKU (Hong Kong), NJU (China) as well as the X-Lab which is an industrial research lab working on the Apollo auto-driving systems [2] supported by Baidu. A number of our ideas and developed tools are being transferred to the X-Lab projects (e.g., Apollo), and I even raised $30,000 fund from them for my advisor.