The purpose of the Discounted {0-1} Knapsack Problem (D{0-1}KP) is to maximize the sum of the value coefficients of all items loaded into the knapsack without exceeding the weight limit of the knapsack. In order to solve the problem of low accuracy when the existing algorithms solve the D{0-1}KP with large scale and high complexity, the Lagrange Interpolation based Learning Monkey Algorithm (LSTMA) was proposed. Firstly, the length of the visual field was redefined during the look process of the basic monkey algorithm. Then, the best individual in the population was introduced as the second pivot point and the search mechanism was adjusted during the jump process. Finally, the Lagrange interpolation operation was introduced after the jump process to improve the search performance of the algorithm. The simulation results on four types of examples show that LSMTA solves the D{0-1}KP with higher accuracy than the comparison algorithms, and it has good robustness.

Aiming at the characteristics of rail surface images such as uneven illumination, limited discernible features, low contrast and changeable reflection characteristics, a background differential rail surface defect detection method based on defect proportion limitation was proposed. The method mainly includes five steps:pre-processing of rail surface images, background modeling and difference, defect proportion limitation filtering, maximum entropy threshold segmentation of defect proportion limitation and connected area labeling. Firstly, the column grayscale mean and median of the rail surface image were combined to perform the rapid background modeling, and the difference operation was carried out to the pre-processed image and the background image. Secondly, the feature with low defect proportion in the rail surface image was used to truncate the upper threshold limit of the defect proportion in order to enhance the contrast of the difference image. Thirdly, the maximum entropy threshold segmentation was improved by using this feature, the global variable weighting of the target entropy was carried out by using the adaptive weighting factor, and an appropriate threshold was selected to maximize the entropy value, so as to reduce the interference of noises such as shadow and rust while retaining the real defects. Finally, the connected area labeling method was used to perform the statistics of the defect areas in the segmented binary image, and the area with defect area lower than the rail damage standard was determined as the noise and removed, so as to realize the rail surface defect detection. Simulation results show that the new method can detect rail surface defects well, and its results have the recall rate, precision rate and weighted harmonic mean of 94.19%, 88.34% and 92.96% respectively, and the average mis-classification error of 0.006 4, so that the method has certain practical value.

The traditional Android malware detection algorithms have low detection accuracy, which can not successfully identify the Android malware by using the technologies of repacking and code obfuscation. In order to solve the problems, the DeepDroid algorithm was proposed. Firstly, the static and dynamic features of Android application were extracted and the Android application features were created by combining static features and dynamic features. Secondly, the Deep Belief Network (DBN) of deep learning algorithm was used to train the collected training set for generating deep learning network. Finally, untrusted Android application was detected by the generated deep learning network. The experimental results show that, when using the same test set, the correct rate of DeepDroid algorithm is 3.96 percentage points higher than that of Support Vector Machine (SVM) algorithm, 12.16 percentage points higher than that of Naive Bayes algorithm, 13.62 percentage points higher than that of K-Nearest Neighbor ( KNN) algorithm. The proposed DeepDroid algorithm has combined the static features and dynamic features of Android application. The DeepDroid algorithm has made up for the disadvantages that code coverage of static detection is not enough and the false positive rate of dynamic detection is high by using the detection method combined dynamic detection and static detection. By using the DBN algorithm in feature recognition, the proposed DeepDroid algorithm has guaranteed high network training speed and high detection accuracy at the same time.

At present, the safety analysis of SNAKE algorithm is mainly about interpolation attack and impossible differential attack. The paper evaluated the security of SNAKE(2) block cipher against integral attack. Based on the idea of higher-order integral attack, an 8-round distinguisher was designed. Using the distinguisher, integral attacks were made on 9/10 round SNAKE(2) block cipher. The attack results show that the 10-round SNAKE(2) block cipher is not immune to integral attack.