Black-box fuzzing is a testing technique to find both known and unknown vulnerabilities in software. When applying black-box fuzzing to smart devices, the main idea is to take a smart device as a black box and provide random input through a network-based interface, such as a Web interface. Due to the diversity of Web interface implementations and complex data format, a blind mutation of the message makes the message unable to pass the verification of the device component. Therefore, each Web interface needs a unique fuzzer, which precisely defines a message format of the target interface, a state maintenance method, the field positions to be mutated, and a specific input mutation method. At the time of writing, a fuzzer is completely developed by a security engineer. To save human labor, we present PDFuzzerGen, a tool to automatically synthesize complex black-box fuzzers for smart devices. PDFuzzerGen generates multiple fuzzing policies by analyzing raw messages and then synthesizes fuzzers based on policies. PDFuzzerGen requires no human intervention and can be applied to a wide range of smart devices. Furthermore, the generated fuzzers can expose bugs and flaws that rest deep in smart devices. PDFuzzerGen was evaluated to generate fuzzers for 19 different smart devices from 6 vendors. It has found 14 previously unknown vulnerabilities, 5 of which were confirmed and disclosed by the China National Vulnerability Database (CNVD) and 2 of which were confirmed and disclosed by Common Vulnerabilities and Exposures (CVE). The generated fuzzers outperform some manually crafted fuzzers on a few metrics, including the vulnerability detection rate and time cost of a newly developed fuzzer, which demonstrates the effectiveness and efficiency of PDFuzzerGen.

The rise of IoT devices has led to an increase in network endpoints, making Advanced Persistent Threat (APT) attacks a significant concern. APT attacks typically use encrypted communication protocols, making traditional detection methods less effective. Machine learning-based APT encrypted traffic detection methods have been proposed but are limited in their efficacy. In this paper, we propose a novel APT encrypted traffic detection method based on two parties and multi-session analysis. The method extracts a limited set of features from network traffic, including session sequence, session time interval, upstream and downstream data size, which are then transformed into images. These images are subsequently fed into a convolutional neural network (CNN) to recognize patterns and identify network traffic. The proposed method was evaluated through five experiments, demonstrating significant success in detecting APT attacks. The use of multi-session analysis provided a comprehensive view of network traffic, enabling more accurate APT attack detection. The proposed method offers potential for deployment in enterprise security, with implications for the development of more effective APT detection methods.

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High scalability and low operating cost make black-box protocol fuzzing a vital tool for discovering vulnerabilities in the firmware of IoT smart devices. However, it is still challenging to compare black-box protocol fuzzers due to the lack of unified benchmark firmware images, complete fuzzing mutation seeds, comprehensive performance metrics, and a standardized evaluation framework. In this paper, we design and implement IoTFuzzBench, a scalable, modular, metric-driven automation framework for evaluating black-box protocol fuzzers for IoT smart devices comprehensively and quantitatively. Specifically, IoTFuzzBench has so far included 14 real-world benchmark firmware images, 30 verified real-world benchmark vulnerabilities, complete fuzzing seeds for each vulnerability, 7 popular fuzzers, and 5 categories of complementary performance metrics. We deployed IoTFuzzBench and evaluated 7 popular black-box protocol fuzzers on all benchmark firmware images and benchmark vulnerabilities. The experimental results show that IoTFuzzBench can not only provide fast, reliable, and reproducible experiments, but also effectively evaluate the ability of each fuzzer to find vulnerabilities and the differential performance on different performance metrics. The fuzzers found a total of 13 vulnerabilities out of 30. None of these fuzzers can outperform the others on all metrics. This result demonstrates the importance of comprehensive metrics. We hope our findings ease the burden of fuzzing evaluation in IoT scenarios, advancing more pragmatic and reproducible fuzzer benchmarking efforts.