Binary code search has received much attention recently due to its impactful applications, e.g., plagiarism detection, malware detection and software vulnerability auditing. However, developing an effective binary code search tool is challenging due to the gigantic syntax and structural differences in binaries resulted from different compilers, architectures and OSs. In this paper, we propose BINGO — a scalable and robust binary search engine supporting various architectures and OSs. The key contribution is a selective inlining technique to capture the complete function semantics by inlining relevant library and user-defined functions. In addition, architecture and OS neutral function filtering is proposed to dramatically reduce the irrelevant target functions. Besides, we introduce length variant partial traces to model binary functions in a program structure agnostic fashion. The experimental results show that BINGO can find semantic similar functions across architecture and OS boundaries, even with the presence of program structure distortion, in a scalable manner. Using BINGO, we also discovered a zero-day vulnerability in Adobe PDF Reader, a COTS binary.

Bounded Model Checking (BMC) for software is a precise bug-finding technique that builds upon the efficiency of modern SAT and SMT solvers. BMC currently does not scale to large programs because the size of the generated formulae exceeds the capacity of existing solvers. We present a new, compositional and property-sensitive algorithm that enables BMC to automatically find bugs in large programs. A novel feature of our technique is to decompose the behaviour of a program into a sequence of BMC instances and use a combination of satisfying assignments and unsatisfiability proofs to propagate information across instances. A second novelty is to use the control- and data-flow of the program as well as information from proofs to prune the set of variables and procedures considered and hence, generate smaller instances. Our tool BLITZ outperforms existing tools and scales to programs with over 100,000 lines of code. BLITZ automatically and efficiently discovers bugs in widely deployed software including new vulnerabilities in Internet infrastructure software.

We propose a novel approach to infer protocol state machines in the realistic high-latency network setting, and apply it to the analysis of botnet Command and Control (C &C) protocols. Our proposed techniques enable an order of magnitude reduction in the number of queries and time needed to learn a botnet C &C protocol compared to classic algorithms (from days to hours for inferring the MegaD C &C protocol). We also show that the computed protocol state machines enable formal analysis for botnet defense, including finding the weakest links in a protocol, uncovering protocol design flaws, inferring the existence of unobservable communication back-channels among botnet servers, and finding deviations of protocol implementations which can be used for fingerprinting. We validate our technique by inferring the protocol state-machine from Postfix's SMTP implementation and comparing the inferred state-machine to the SMTP standard. Further, our experimental results offer new insights into MegaD's C &C, showing our technique can be used as a powerful tool for defense against botnets.

We propose a new fingerprinting technique that differentiates between unique devices over a Wireless Local Area Network (WLAN) simply through the timing analysis of 802.11 probe request frames. Our technique can be applied to spoof detection, network reconnaissance, and implementation of access control against masquerading attacks. Experimental results indicate that our technique is consistent and accurate in differentiating between unique devices. In contrast with existing wireless fingerprinting techniques, our technique is passive, non-invasive and does not require the co-operation of fingerprintee hosts.

We present a modeling framework for studying header compression performance. For the first time, a source model is developed for header compression. By varying the source and channel models in our framework, different source and deployment scenarios can be modeled. Using this new paradigm, we study the tradeoff dependencies between performance metrics. We also demonstrate how a scheme can be designed to optimize tradeoffs based on the desired level of performance.

Context replication is a new context initialization technique for header compression proposed by the IETF. It increases header compression gains by reducing the size of bulky initialization packets. Context replication compresses header fields based on the redundancy between flows, rather than within flows. For context replication to be performed efficiently, an elaborate understanding of inter-flow field behavior is required. However, inter-flow field behavior is more complicated and relatively unstudied compared to well-known intra-flow field behavior. Also, because context replication is a new technique, much is still unknown about the range of efficiency gains achievable. We present a novel approach to obtain and analyze inter-flow field behavior. Our approach allows the optimization of encoding specifications in context replication. By implementation, we show that our optimized encodings produce leaner initialization packets compared to current proposals. We also obtain from implementation useful insights on the range of compressed header sizes and efficiency gains in context replication.