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.

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.

The outlier detection algorithm based on autoencoder is easy to over-fit on small- and medium-sized datasets， and the traditional outlier detection algorithm based on ensemble learning does not optimize and select the base detectors， resulting in low detection accuracy. Aiming at the above problems， an Ensemble learning and Autoencoder-based Outlier Detection （EAOD） algorithm was proposed. Firstly， the outlier values and outlier label values of the data objects were obtained by randomly changing the connection structure of the autoencoder generate different base detectors. Secondly， local region around the object was constructed according to the Euclidean distance between the data objects calculated by the nearest neighbor algorithm. Finally， based on the similarity between the outlier values and the outlier label values， the base detectors with strong detection ability in the region were selected and combined together， and the object outlier value after combination was used as the final outlier value judged by EAOD algorithm. In the experiments， compared with the AutoEncoder （AE） algorithm， the proposed algorithm has the Area Under receiver operating characteristic Curve （AUC） and Average Precision （AP） scores increased by 8.08 percentage points and 9.17 percentage points respectively on Cardio dataset； compared with the Feature Bagging （FB） ensemble learning algorithm， the proposed algorithm has the detection time cost reduced by 21.33% on Mnist dataset. Experimental results show that the proposed algorithm has good detection performance and real-time performance under unsupervised learning.

A broadcast mobile television system based on Audio and Video coding Standard（AVS） was introduced. The design includes the real time encoder of video and audio, the multiplexer of AVS system, the T-DMB transmit system, decoder and playback on the mobile phone and PC. The experimental results show that the broadcast mobile television system based on AVS is feasible.

This paper firstly analyzed the drawbacks of handoff in Ad Hoc networks being integrated to Internet based on mobile IPv6. Local Agent and foreign Agent were introduced into MIPv6 in the handoff mechanism which supported intra mobility, and part of the mobility management ability of the home Agent was transferred to local Agent and foreign Agent, then the intra mobility could be shielded from the home Agent and correspondent node. The results show that the handoff time decreases greatly, when the inter-handoff probability is low, the average handoff time can be cut to 50% of the proactive handoff time under certain condition.