Aiming at the problems of low Test Case Acceptance Rate （TCAR） and lack of diversity in application of fuzzing in Industrial Control Protocols （ICPs）， a fuzzing method for ICPs based on adaptive dynamic interval strategy was proposed. Recurrent Neural Network （RNN） was added to self-attention mechanism in Transformer to construct a protocol feature extraction model； RNN was used to extract local features of the data through a sliding window， and the self-attention mechanism was introduced to carry out global feature extraction， so as to ensure the TCAR； the residual connection was added between the attention blocks to transfer the weight scores and improve the computational efficiency； a dynamic interval strategy was generated to adjust sampling range of the model at any time step， so as to increase diversity of the test cases； in the testing process， the field adaptive importance function was constructed to locate the key variant fields. Based on the above method， a fuzzing framework TDRFuzzer was designed and experimentally evaluated using three industrial protocols： Modbus TCP， S7 comm， and Ethernet/IP. The results show that compared to three models： GANFuzzer， WGANFuzzer， and PeachFuzzer， TDRFuzzer has the TCAR increased significantly， and the Vulnerability Detection Rate （VDR） increased by 0.073， 0.035， and 0.150 percentage points， respectively. This indicates that TDRFuzzer has stronger vulnerability mining capability for ICPs.

To solve the problem that there are a lot of invalid mutations， and the performance is wasted in Coverage-Guided Fuzzing （CGF）， an adaptive sensitive region mutation algorithm was proposed. Firstly， the mutation locations were divided into effective mutation location set and invalid mutation location set according to whether the mutated test case executed a new path. Then， the sensitive region was determined based on the effective mutation location， and the subsequent mutations were concentrated in the sensitive region. In the subsequent fuzzing process， the sensitive region of the corresponding seed was adjusted adaptively according to the execution results of test cases， so as to reduce the invalid mutations. In addition， a new seed selection strategy was designed to assist the sensitive region mutation algorithm. The adaptive sensitive region mutation algorithm was integrated into the American Fuzzy Lop （AFL） to form Sensitive-region-based Mutation American Fuzzy Lop （SMAFL）. SMAFL was evaluated on 12 popular applications and the experimental results showed that compared to AFL，when there was one initial seed， SMAFL found 31.4% more paths on average， increased the number of fuzzed counts by 3.4 times， and achieved higher code coverage across all 12 programs. In the testing of the LAVA-M dataset， SMAFL found 2 more bugs than AFL， and found the same bugs in a shorter time. Overall， the adaptive sensitive region mutation algorithm can improve the exploration efficiency of fuzzers.

Maritime transportation is one of the most important modes of human transportation. Maritime cybersecurity is crucial to avoid financial loss and ensure shipping safety. Due to the obvious weakness of maritime cybersecurity， maritime cyberattacks are frequent. There are a lot of research literatures about maritime cybersecurity at domestic and abroad， but most of them have not been reviewed yet. The structures， risks and countermeasures of the maritime network were systematically organized and comprehensively introduced. On this basis， some suggestions were put forward to deal with the maritime cyberthreats.

The privacy policy document declares the privacy information that an application needs to obtain， but it cannot guarantee that it clearly and fully discloses the types of privacy information that the application obtains. Currently， there are still deficiencies in the analysis of the consistency between actual sensitive behaviors of applications and privacy policies. To address the above issues， a method for analyzing the consistency between sensitive behaviors and privacy policies of Android applications was proposed. In the privacy policy analysis stage， a Bi-GRU-CRF （Bi-directional Gated Recurrent Unit Conditional Random Field） neural network was used and the model was incrementally trained by adding a custom annotation library to extract key information from the privacy policy declaration. In the sensitive behavior analysis stage， IFDS （Interprocedural， Finite， Distributive， Subset） algorithm was optimized by classifying sensitive API （Application Programming Interface） calls， deleting already analyzed sensitive API calls from the input sensitive source list， and marking already extracted sensitive paths. It ensured that the analysis results of sensitive behaviors matched the language granularity of the privacy policy description， reduced the redundancy of the analysis results and improved the efficiency of analysis. In the consistency analysis stage， the semantic relationships between ontologies were classified into equivalence， subordination， and approximation relationships， and a formal model for consistency between sensitive behaviors and privacy policies was defined based on these relationships. The consistency situations between sensitive behaviors and privacy policies were classified into clear expression and ambiguous expression， and inconsistency situations were classified into omitted expression， incorrect expression， and ambiguous expression. Finally， based on the proposed semantic similarity-based consistency analysis algorithm， the consistency between sensitive behaviors and privacy policies was analyzed. Experimental results show that， by analyzing 928 applications， with the privacy policy analysis accuracy of 97.34%， 51.4% of Android applications are found to have inconsistencies between the actual sensitive behaviors and the privacy policy declaration.

In order to reduce the complexity of the reconstruction algorithm and improve the precision of estimation, the authors proposed a new Sparsity Adaptive Matching Pursuit (SAMP) algorithm by using the adaptive threshold applied in the OFDM (Orthogonal Frequency Division Multiplexing) sparse channel estimation. The Monte Carlo simulation results show that, compared with the traditional method, the CPU run time decreased by 44.7%. And in lower SNR (SignaltoNoise Ratio), the performance achieved obvious improvements. Besides, in OFDM sparse channel estimation, a new design of pilot pattern was presented based on the mutual coherence of the measurement matrix in Compressive Sensing (CS) theory. The Monto Carlo simulation results show that, the precision of channel is increased by 2-4 dB with the new pilot pattern.