Biblio

Fuzzing is a software testing technique that finds bugs by repeatedly injecting mutated inputs to a target program. Known to be a highly practical approach, fuzzing is gaining more popularity than ever before. Current research on fuzzing has focused on producing an input that is more likely to trigger a vulnerability. In this paper, we tackle another way to improve the performance of fuzzing, which is to shorten the execution time of each iteration. We observe that AFL, a state-of-the-art fuzzer, slows down by 24x because of file system contention and the scalability of fork() system call when it runs on 120 cores in parallel. Other fuzzers are expected to suffer from the same scalability bottlenecks in that they follow a similar design pattern. To improve the fuzzing performance, we design and implement three new operating primitives specialized for fuzzing that solve these performance bottlenecks and achieve scalable performance on multi-core machines. Our experiment shows that the proposed primitives speed up AFL and LibFuzzer by 6.1 to 28.9x and 1.1 to 735.7x, respectively, on the overall number of executions per second when targeting Google's fuzzer test suite with 120 cores. In addition, the primitives improve AFL's throughput up to 7.7x with 30 cores, which is a more common setting in data centers. Our fuzzer-agnostic primitives can be easily applied to any fuzzer with fundamental performance improvement and directly benefit large-scale fuzzing and cloud-based fuzzing services.

Distributed detection with data fusion has gained great attention in recent years. Collaborative detection improves the performance, and the optimal sensor deployment may change with time. It has been shown that with data fusion less sensors are needed to get the same detection ability when abundant sensors are deployed randomly. However, because of limitations on equipment number and deployment methods, fixed sensor locations may be preferred underwater. In this paper, we try to establish a theoretical framework for finding sensor positions to maximize the detection probability with a distributed sensor network. With joint data processing, detection performance is related to all the sensor locations; as sensor number grows, the optimization problem would become more difficult. To simplify the demonstration, we choose a 1-dimensional line deployment model and present the relevant numerical results.

Underwater sensing is envisioned using inexpensive underwater sensor nodes distributed over a wide area, deployed close to the bottom, and networked through underwater acoustic communications. In this paper, an underwater acoustic sensor node to perform the underwater sensing is designed and implemented. Specifically, we describe the design criteria, architecture and functional modules of underwater acoustic sensor node. Moreover, we give the experiment results of ocean current field estimation using the designed underwater acoustic sensor nodes at the sea area of Liuheng, Zhoushan, China.