Protocols serve as the foundation for communication between devices and systems in modern computing environments. However, their widespread use also exposes them to various security threats. A flaw in protocol implementation can be exploited to compromise system security, leading to severe disruptions or unauthorized access. Fuzzing has become a widely used technique for protocol vulnerability detection. However, the existing protocol fuzzing approaches often lack effective guidance strategies for exploring the complex protocol state space, which leads to limited coverage and suboptimal testing efficiency.In this paper, we propose BayesFuzz, a Bayesian-guided greybox fuzzing approach for stateful protocols. It maintains a probability table to record the probability of sending different messages and triggering different state transitions in the current state. This table is updated via Bayesian inference based on feedback from each fuzzing iteration. Accordingly, BayesFuzz is able to select the optimal message for the current state during test case generation, thereby improving fuzzing efficiency in complex stateful protocols. Experimental results confirm the effectiveness of BayesFuzz. Compared with state-of-the-art fuzzers BooFuzz and AFLNET, BayesFuzz increases branch coverage by averagely 23.18% and 45.3% within 24 hours. Furthermore, it successfully discovered an unknown vulnerability in the MQTT protocol implementation.

The application of iris biometric data has become prevalent across various domains, encompassing access control, identity verification, and criminal investigations. Consequently, there is a pressing need to develop effective methods for safeguarding the privacy of iris data. While numerous methods for iris data protection have been proposed, the majority of them fall short of meeting the ISO/IEC 24745 standards about irreversibility, revocability, and unlinkability. In this paper, we introduce a novel iris data protection method called TNCB, which is based on a transformation network. The TNCB involves performing a block-wise permutation of the original iris images using application-specific parameters, followed by pixel-by-pixel modulo and inversion fusion operations. The resulting images are subsequently employed for pre-training a recognition network that will be used to recognize protected images. Afterwards, a transformation network is introduced to achieve a further non-invertible transformation. Our security analysis demonstrates that the TNCB could fulfill the three major security requirements. To validate its effectiveness, we conducted a series of attack and performance experiments on the CASIA-Iris-Lamp and CASIA-Iris-Thousand datasets. Experimental results substantiated the robustness of TNCB in maintaining recognition performance while safeguarding the privacy of iris data. Furthermore, experimental results also highlight that our scheme could effectively support iris recognition in both open-set and close-set modes.