In order to improve the performance of Grasshopper Optimization Algorithm (GOA) in solving multi-objective problems, a Hybrid Multi-objective Grasshopper Optimization Algorithm (HMOGOA) based on fusion of multiple strategies was proposed. First, the Halton sequence was used to establish the initial population to ensure that the population had an uniform distribution and high diversity in the initial stage. Then, the differential mutation operator was applied to guide the population mutation, so as to promote the population to move to the elite individuals and extend the search range of optimization. Finally, the adaptive weight factor was used to adjust the global exploration ability and local optimization ability of the algorithm dynamically according to the status of population optimization, so as to improve the optimization efficiency and the solution set quality. With seven typical functions selected for experiments and tests, HMOGOA were compared with algorithms such as multi-objective grasshopper optimization, Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) and Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA Ⅱ). Experimental results indicate that compared with the above algorithms, HMOGOA avoids falling into local optimum, makes the distribution of the solution set significantly more uniform and broader, and has greater convergence accuracy and stability.

For the facts that WeChat red packet and fund transfer functions are used for illegal activities such as red packet gambling and illegal transactions, and the existing research work in this field is difficult to identify the specific numbers of sending and receiving red packets and fund transfers in WeChat, and there are problems of low recognition rate and high resource consumption, a method for dividing large and small burst blocks of traffic was proposed to extract the characteristics of traffic, so as to effectively identify the sending and receiving of red packets and the transfer behaviors. Firstly, by taking advantage of the suddenness of sending and receiving red packets and fund transfers, a large burst time threshold was set to define the burst blocks of such behaviors. Then, according to the feature that the behaviors of sending and receiving red packets and fund transfers consist of several consecutive user operations, a small burst threshold was set to further divide the traffic block into small bursts. Finally, synthesizing the features of small burst blocks in the big burst block, the final features were obtained. The experimental results show that the proposed method is generally better than the existing research on WeChat payment behavior recognition in terms of time efficiency, space occupancy rate, recognition accuracy and algorithm universality, with an average accuracy rate up to 97.58%. The test results of the real environment show that the proposed method can basically accurately identify the numbers of sending and receiving red packets and fund transfers for a user in a period of time.

In order to address the problem that the mutation in the current fuzzing has certain blindness and the samples generated by the mutation mostly pass through the same high-frequency paths, a binary fuzzing method based on light-weight program analysis technology was proposed and implemented. Firstly, the target binary program was statically analyzed to filter out the comparison instructions which hinder the sample files from penetrating deeply into the program during the fuzzing process. Secondly, the target binary program was instrumented to obtain the specific values of the operands in the comparison instructions, according to which the real-time comparison progress information for each comparison instruction was established, and the importance of each sample was measured according to the comparison progress information. Thirdly, the real-time path coverage information in the fuzzing process was used to increase the probability that the samples passing through rare paths were selected to be mutated. Finally, the input files were directed and mutated by the comparison progress information combining with a heuristic strategy to improve the efficiency of generating valid inputs that could bypass the comparison checks in the program. The experimental results show that the proposed method is better than the current binary fuzzing tool AFL-Dyninst both in finding crashes and discovering new paths.

The deobfuscation result of deobfuscation framework Miasm is a picture, which cannot be decompiled to recovery program source code. After deep research on the obfuscation strategy of Obfuscator Low Level Virtual Machine (OLLVM) and Miasm deobfuscation idea, a general OLLVM automatic deobfuscation framework based on symbolic execution was proposed and implemented. Firstly, the basic block identification algorithm was used to find useful basic blocks and useless blocks in the obfuscated program. Secondly, the symbolic execution technology was used to determine the topological relations among useful blocks. Then, the instruction repairment was directly applied to the assembly code of basic blocks. Finally, an executable file after deobfuscation was obtained. The experimental results show that, under the premise of guaranteeing the deobfuscation time as little as possible, the code similarity between the deobfuscation program and the non-obfuscated source program is 96.7%. The proposed framework can realize the OLLVM deobfuscation of the C/C ++ files under the x86 architecture very well.

Focused on the issue that the existing password evaluation models cannot be used universally, and there is no evaluation model applicable from simple passwords to very complex passwords. A password evaluation model was designed based on multi-model ensemble learning. Firstly, an actual password training set was used to train multiple existing password evaluation models as the sub-models. Secondly, a multiple trained evaluation sub-models were used as the base learners for ensemble learning, and the ensemble learning strategy which designed to be partial to weakness, was used to get all advantages of sub-models. Finally, a common password evaluation model with high accuracy was obtained. Actual user password set that leaked on the network was used as the experimental data set. The experimental results show that the multi-model ensemble learning model used to evaluate the password strength of different complexity passwords, has a high accuracy and is universal. The proposed model has good applicability in the evaluation of passwords.

The SQL (Structured Query Language) injection attack is a threat to Web applications. Aiming at SQL injection behaviors in PHP (Hypertext Preprocessor) applications, a model of detecting SQL injection behaviors based on tainting technology was proposed. Firstly, an SQL statement was obtained when an SQL function was executed, and the identity information of the attacker was recorded through PHP extension technology. Based on the above information, the request log was generated and used as the analysis source. Secondly, the SQL parsing process with taint marking was achieved based on SQL grammar analysis and abstract syntax tree. By using tainting technology, multiple features which reflected SQL injection behaviors were extracted. Finally, the random forest algorithm was used to identify malicious SQL requests. The experimental results indicate that the proposed model gets a high accuracy of 96.9%, which is 7.2 percentage points higher than that of regular matching detection technology. The information acquisition module of the proposed model can be loaded in an extended form in any PHP application; therefore, it is transplantable and applicable in security audit and attack traceability.

Concerning the fact that when the receiver of an Intent does not validate empty data and abnormal data, the process will crash and cause denial of service, an automated Android component vulnerability mining framework based on static analysis techniques and fuzzing test techniques was proposed. In this framework, reverse analysis techniques and static data flow analysis techniques were used to extract package name, component, Intent with the data of a traffic and data flow paths from exported component to private component to assist fuzzing test. In addition, more mutation strategy on the attributes of Intent (such as Action, Category, Data and Extra) were added while generating Intent tests and the Accessibility technology was adopted to close the crash windows in order to realize automation. Finally, a tool named DroidRVMS was implemented, and a comparative experiment with Intent Fuzzer was designed to verify the validity of the framework. The experimental results show that DroidRVMS can find denial of service vulnerability resulting from dynamic broadcast receiver and most types of exceptions.

In the natural scenario, it is difficult to extract a complete outline of the object because of background textures, light and occlusion. Therefore an object recognition method based on local contour feature was proposed. Local contour feature of this paper formed by chains of 2-adjacent straight and curve contour segments (2AS). First, the angle of the adjacent segments, the segment length and the bending strength were analyzed, and the semantic model of the 2AS contour feature was defined. Then on the basis of the relative position relation between object's 2AS features, the 2AS mutual relation model was defined. Second, the 2AS semantic model of the object template primarily matched with the 2AS features of the test image, then 2AS mutual relation model of object template accurately matched with the 2AS features of the test image. At last, the pairs of 2AS of detected local contour features were obtained and repeatedly grouped, then grouped objects were verified according to the 2AS mutual relation model of object template. The contrast experiment with the 2AS feature algorithm with similar straight-line chains, the proposed algorithm has higher accuracy, low false positive rate and miss rate in the recognition of grading ring, then the method can more effectively recognize the grading ring.

In order to improve the efficiency of power transmission line inspection by Unmanned Aerial Vehicle (UAV), a new method was proposed for detecting broken transmission lines and defects of foreign body based on the perception of line structure. The transmission line image acquired by UAV was easily influenced by the background texture and light, the gradient operators of horizontal and vertical direction which can be used to detect the line width were used to extract line objects in the inspection image. The study on calculation of gestalt perception of similarity, continuity and colinearity connected the intermittent wires into continuous wires. Then the parallel wire groups were further determined through the calculation of parallel relationship between wires. In order to reduce the detection error rate, spacers and stockbridge dampers of wires were recognized based on a local contour feature. Finally, the width change and gray similarity of segmented conductor wire were calculated to detect the broken part of wire and foreign object defect. The experimental results show that the proposed method can detect broken wire strand and foreign object defect efficiently under complicated backgrounds from the transmission line of UAV images.

Images of transmission tower acquired by Unmanned Aerial Vehicle (UAV) have high resolution and complex background, the traditional stitching algorithm using feature points can detect a large number of feature points from background which costs much time and affects the matching accuracy. For solving this problem, a new image mosaic algorithm with quick speed and strong robustness was proposed. To reduce the influence of the background, each image was first segmented into foreground and background based on a new implementation method of salient region detection. To improve the feature point extraction and reduce the computation complexity, transformation matrix was calculated and image registration was completed by ORB (Oriented Features from Accelerated Segment Test (FAST) and Rotated Binary Robust Independent Elementary Features (BRIEF)) feature. Finally, the image mosaic was realized with image fusion method based on multi-scale analysis. The experimental results indicate that the proposed algorithm can complete image mosaic precisely and quickly with satisfactory mosaic effect.

This paper analyzed the metadata processing in PVFS2, and took the remove operation as an example. To find out the bottlenecks in the remove operation, the time of each step was tested. And an optimization method to reduce the communication number by placing judgmental process on the server side was proposed, which is also suitable for other metadata operations. The optimization method was implemented in PVFS2. Compared with the original remove operation, this proposed method shows about 10% improvement in performance.

Large-scale computing and high mis-classification rate are two disadvantages of Loopy Belief Propagation (LBP) algorithm for image segmentation. A fast image segmentation method based on LBP algorithm was proposed. At first, a local region Gibbs energy model was built up. Then the region messages were propagated by LBP algorithm. In order to improve the running speed for LBP algorithm, an efficient speedup technique was used. At last, the segmentation result was obtained by the Maximum A Posterior (MAP) criterion of local region Gibbs energy. The experimental results show that the proposed algorithm not only obtains more accurate segmentation results, especially to noise or texture image, but also implements more fast.

The proposed JPEG 2000 real-time quantization watermarking algorithm was used in an improved online bank pension distribution system. The system was based on fingerprint recognition and digital watermarking technologies. In the client side, real-time quantization watermark was embedded into the sampled fingerprint image in the JPEG 2000 coding pipeline; then the compressed bit-stream was sent to the server side. In the server side, the watermark was extracted from the compressed bit-stream in the JPEG 2000 decoding pipeline; then the decompressed fingerprint image and extracted watermark were used to verify users identification. Experiments showed when typical fingerprint image was compressed to 1/4~1/20 of its original size, the embedded watermark could be exactly extracted, and fingerprint recognition rate remained almost the same after lossy compression. The system has a better interaction performance in the band-limited network situation, and is very promising in the E-business applications.