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Software diversity is an effective defense against code-reuse attacks, but most existing software diversification technologies are based on source code.Obtaining program source code may be difficult, while binary files are challenging to disassemble accurately and distinguish between code pointers and data constants.This makes binary file diversification difficult to generate high levels of randomization entropy, and easily compromised by attackers.To overcome these challenges, a binary file oriented software diversification method was proposed based on static binary rewriting technology, namely instruction offset randomization.This method inserted NOP instructions of varying byte lengths before program instructions with a certain probability, reducing the number of unintended gadgets in the program and randomly offsetting the original instruction address.This disrupts the program’s original memory layout and increases the cost of code-reuse attacks.At the same time, an optimization strategy based on hot code was designed for this method.The execution times of basic blocks in binary files were obtained by dynamic pile insertion, so as to adjust the NOP instruction insertion probability in each basic block.The higher the execution frequency, the fewer NOP instructions were inserted into the basic block, which can ensure lower performance overhead and produce higher randomization entropy.In the experimental part, the SPEC benchmark program was used to test the optimized method from the aspects of performance overhead, gadget survival rate and file size.The results show that a 15% insertion probability achieves the best effect, with an average gadget survival rate of less than 1.49%, increasing attackers’ difficulty in reusing the same gadget attack chain.Furthermore, only a 4.1% operation overhead and 7.7% space overhead are added, maintaining high levels of security.

In order to reduce the risk of rapid spread of homogeneous attacks in network systems, and enhance network and software security, software diversification technologies are applied widely nowadays.Software diversification aims to generate functionally equivalent but internally changed program variants, thereby alter a single operating environment and mitigating homogenization attacks.The existing diversified technical evaluation index ROP gadgets survival rate is difficult to directly reflect the safety impact and the evaluation method is single.In order to evaluate the effectiveness of the diversification method more comprehensively and effectively, a software diversification evaluation method based on the properties of ROP/JOP gadgets is proposed, by analyzing common code reuse attacks, and turns abstract quantification into concrete indicators evaluates the security gain and effect of diversified methods from three aspects of space, time and quality.The method first discusses how diversification techniques affect ROP/JOP attacks according to the three properties of gadgets similarity, damage degree and availability.Nine kinds of diversification methods, such as instruction replacement, NOP insertion, and control flow flattening, are used to diversify the GNU coreutils assembly to generate diversification assembly.Experiments based on the property of gadgets are carried out on the diverse assemblies, and the effectiveness of different diversification methods and the impact on attacks are evaluated according to the experimental results.The experimental results show that this method can accurately evaluate the security gain of software diversification methods, the diversification technology will lead to the increase of the attack chain space required by the ROP/JOP attack, the longer time to construct the attack chain and the lower the attack success rate.The effects of different diversification methods are different, it has a guiding role for the follow-up research on diversified technologies with higher safety gains.

At present,there are no relevant standards and specifications for the verification of civil aircraft maintenance manuals in China.It is urgent to carry out relevant research and establish a complete manual verification system as soon as possible.T his paper puts forward the technical requirements,operation process and implementation points of manual verification operation based on large number of maintenance manual verification work,which combined with the flight test modification work.Taking the aircraft maintenance manual verification procedure of flap high lift system and flap structural components as an example,this paper carries out the manual verification plan arrangement and verification implementation method design from the perspective of practical operation feasibility and time saving.The key elements of the verification plan and design of practical operation process are given.The key operate points and precautions for greater difficult flap removal and installation programs are given as example,and the summary of common maintenance problems and improvement methods are recommended.All these work put forward suggestions for the follow-up verification work.

The maintenance procedure of domestic civil aircraft has the problem of incomplete verification method,so it is necessary to study its verification method.According to the procedure type and content of aircraft maintenance manual technique,three verification methods are introduced.Based on the table-top verification method and on-object verification method from S1000D standard,two sub-methods of maintenance use verification and maintenance dedicated verification are proposed in combination with verification practice and conclusion of domestic civil aircraft,and their application ranges are described.The evaluation/simulation verification method and its four submethods are proposed,and verified with instance.T he results show that the proposed verification method of civil aircraft maintenance manual are effective,and are of reference significance for the development of aircraft maintenance manual verification.

A method for land-cover classification was proposed based on the fusion of features generated from waveform data and point cloud respectively. It aims to partially overcome the ineffectiveness of many traditional classifiers caused by the fact that point cloud is lacking spectral information. The whole flowchart of the method is as follows: Firstly, Gaussian decomposition was applied to fit an echo full-waveform. The parameters associated with the Gaussian function were optimized by LM (Levenberg-Marquard) algorithm. Six and thirteen features were generated to describe the waveform characteristics and the local geometry of point cloud, respectively. Secondly, a random forest was selected as the classifier to which the generated features were input. Relief-F was used to rank the weights of all the features generated. Finally, features were input to the classifier one by one according to the weights calculated from feature ranking, where classification accuracies were evaluated. The experimental results show that the effectiveness of the fusion of features generated from waveform and point cloud for LiDAR data classification, with 95.4% overall accuracy, 0.90 kappa coefficient, which outperform the results obtained by a single class of features, no matter whether they were generated from point cloud or waveform data.

Vibration signals are used to monitor the running state of mechanical equipment, but always suffer from a lot of noise in the acquisition process. In order to eliminate noise interference as much as possible, a multilevel residual convolution autoencoder network based noise learning method (LN-MRSCAE) is proposed in this paper. The LN-MRSCAE consists of a deep convolutional autoencoder network and multilevel residual, in which the learning noise module encodes and decodes the noise components, combining with a residual module to obtain the denoised signal. The multilevel residual structure is designed to address the gradient disappearance in network learning and improve the training speed. In specific, the noise learning replaces the traditional clean signal learning. Instead of learning the characteristics of the original clean signal, just learn the characteristics of the noise signal. The effectiveness of LN-MRSCAE network denoising is verified by classic analog signal and actual bearing vibration signals with different faults. The results show that the LN-MRSCAE model is superior to the latest models, and the noise component of the signal is obviously suppressed. [ABSTRACT FROM AUTHOR]