Fuzzing is a practical approach to discovering flaws in the design and implementation of Wi-Fi protocols. However, existing Wi-Fi fuzzers are either vendor- or ecosystem-specific. Besides, they only cover a subset of 802.11 protocols and frame types. The growing complexity of Wi-Fi protocols, which have evolved to Wi-Fi6 and WPA3 already, calls for a free and comprehensive fuzzing tool for modern Wi-Fi devices. In this paper, we present such a fuzzing tool named Owfuzz. Unlike previous works using mostly firmware emulation fuzzing or driver fuzzing, Owfuzz takes the over-the-air fuzzing approach. It can perform fuzzing tests on arbitrary Wi-Fi devices from any vendor and can fuzz all three types of Wi-Fi frames (management, control, and data) defined in all versions of the 802.11 standards. It can be easily extended to support interactive testing of various protocol models. With Owfuzz, we have tested the products of mainstream Wi-Fi chip and device vendors, leading to the discovery of 23 flaws. We have reported most of these flaws to the related vendors with 8 CVE IDs assigned. Moreover, we have open-sourced Owfuzz to the community to facilitate future research.

Audio streaming services have become increasingly popular due to the wide use of smart phones. More and more people are enjoying live audio broadcasting while they are doing various kinds of activities. Meanwhile, the data volume of live audio streams is also ever increasing. Searching and indexing these audio streams is still an important and open problem, with the following challenges: (i) queries on the large number of audio streams need to be answered in real-time; (ii) a live audio stream is inserted into the index continuously to enable live audio streams to appear in query results, and the number of insertions is large which often becomes a performance issue. In this application paper, we propose a multi-modal and unified log structured merge-tree (a.k.a. LSM-tree which consists of multiple inverted indices) based index to support intensive insertions and real-time search on live audio stream applications. Our index natively supports two major types of indexing techniques in audio search: text based indexing and sound based indexing. To address the challenges of live audio indexing and search, we propose an index (called RTSI) which avoids traversing multiple inverted indices to compute the score of an audio stream. In RTSI, we propose various techniques to address the technical challenges. First, for each term we use three inverted lists which contain the sorted score of popularity, freshness and relevance, respectively, such that we can compute the top-k query results efficiently. Second, we devise an upper bound for the unchecked audio streams, such that the query answering process can be terminated earlier. Third, we create mirrors for the indices that need to be merged, such that queries can be answered in real-time when the indices are merging. We conduct extensive experiments on audio streams obtained from Ximalaya. The experimental results show that RTSI can answer a large number of queries in a real-time manner while concurrently handling massive insertions.