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Asia Communications and Photonics Conference, ACPC 2015

作者： Ding, Jianfeng Yang, Lin Zhang, Lei State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors P. O. Box 912 Beijing100083 China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi214125 China

ISBN: (纸本)9781943580064

关键词： Light modulators

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Color Image Steganalysis Algorithm Based on Quaternion Convolutional Neural Network 30th

Color Image Steganalysis Algorithm Based on Quaternion Convo...

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30th International Conference on Computational and Experimental engineering and Sciences, ICCES 2024

作者： Xu, Meng Wang, Jinwei State Key Laboratory of Mathematical Engineering and Advanced Computing Henan450001 China Department of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9783031687747

Most of the images on the Internet are color images, and steganalysis of color images is a very critical issue in the field of steganalysis. The current proposed color image steganalysis features mainly rely on manual design, and the steganalysis features do not fully consider the internal connections between the three channels of color images. Most steganalyzers based on deep learning are also designed for grayscale images. In recent years, the continuous introduction of advanced steganography techniques for color images has also brought more severe challenges to the steganography analysis of color images. Quaternions are a tool that can effectively represent color images, and utilizing quaternion transformations can fully exploit the correlation between color image channels. Combining quaternion theory with convolutional neural networks, this paper proposed a color image steganalysis algorithm based on quaternion convolutional neural networks. Firstly, the image is represented by quaternion, and then input into the quaternion convolutional neural network. The convolution kernel in the network is composed of quaternions, and then perform convolution and pooling. The experimental results have demonstrated the significant effectiveness of the proposed network. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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A Software Diversity-Based lab in Operating System for Cyber Security Students 3

A Software Diversity-Based Lab in Operating System for Cyber...

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3rd IEEE International Conference on Computer Science and Educational Informatization, CSEI 2021

作者： Wang, Junchao Pang, Jianmin Shan, Zheng Wei, Jin Yao, Jinyang Liu, Fudong State Key Laboratory of Mathematical Engineering and Advanced Computing Zheng Zhou China School of Computer Science Data Arena Institute Fudan University Zheng Zhou China

ISBN: (纸本)9781728184739

The course of operating system's labs usually fall behind the state of art technology. In this paper, we propose a Software Diversity-Assisted Defense (SDAD) lab based on software diversity, mainly targeting for students majoring in cyber security and computer science. This lab is consisted of multiple modules and covers most of the important concepts and principles in operating systems. Thus, the knowledge learned from the theoretical course will be deepened with the lab. For students majoring in cyber security, they can learn this new software diversity-based defense technology and understand how an exploit works from the attacker's side. The experiment is also quite stretchable, which can fit all level students. © 2021 IEEE.

关键词： Students

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Stealthy Malware Detection Based on Deep Neural Network 2

Stealthy Malware Detection Based on Deep Neural Network

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2019 2nd International Symposium on Big Data and Applied Statistics, ISBDAS 2019

作者： Lu, Shoupu Li, Qingbao Zhu, Xinbing State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Province Zhengzhou450001 China Henan University of Economics and Law Henan Province Zhengzhou450000 China

Network attacks using advanced local hiding technology have not only increased, but also become a serious threat. However, attacks using these technologies can not be detected through traffic detection, and some attacks imitate benign traffic to avoid detection. To solve these problems, a malware process detection method based on process behavior in possibly infected terminals is proposed. In this method, a deep neural network is introduced to classify malware processes. Firstly, the recurrent neural network is trained to extract the characteristics of process behavior. Secondly, training convolutional neural network is used to classify feature images generated by trained RNN features. The experiments results show that this method can effectively extract the features of malicious processes, and the AUC of ROC curve is 0.97 in the best case. © Published under licence by IOP Publishing Ltd.

关键词： Malware

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Multi-objective Firefly Algorithm for Test Data Generation with Surrogate Model 7th

Multi-objective Firefly Algorithm for Test Data Generation w...

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7th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2021

作者： Zhang, Wenning Zhou, Qinglei Jiao, Chongyang Xu, Ting State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450000 China Zhongyuan University of Technology Zhengzhou450000 China Zhengzhou University Zhengzhou450000 China

ISBN: (纸本)9789811659393

To solve the emerging complex optimization problems, multi objective optimization algorithms are needed. By introducing the surrogate model for approximate fitness calculation, the multi objective firefly algorithm with surrogate model (MOFA-SM) is proposed in this paper. Firstly, the population was initialized according to the chaotic mapping. Secondly, the external archive was constructed based on the preference sorting, with the lightweight clustering pruning strategy. In the process of evolution, the elite solutions selected from archive were used to guide the movement to search optimal solutions. Simulation results show that the proposed algorithm can achieve better performance in terms of convergence iteration and stability. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Multiobjective optimization

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IEEE International Conference on Software engineering and Service Sciences (ICSESS)

作者： Duanyi Wang Hui Shu Fei Kang Wenjuan Bu State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan China

ISBN: (数字)9781728165790

ISBN: (纸本)9781728165806

Recently, graph-based malware similarity analysis has been widely used in the field of malware detection. However, the wide application of code obfuscation, polymorphism, and deformation changes the structure of malicious code, which brings great challenges to the malware similarity analysis. To solve these problems, in this paper, we present a new approach to malware similarity analysis based on the network control structure graph (NCSG). This method analyzed the behavior of malware by application program interface (API) association and constructed NCSG. The graph could reflect the command-and-control(C&C) logic of malware. Therefore, it can resist the interference of code obfuscation technology. The structural features extracted from NCSG will be used as the basis of similarity analysis for training the detection model. Finally, we tested the dataset constructed from five known malware family samples, and the experimental results showed that the accuracy of this method for malware variation analysis reached 92.75%. In conclusion, the malware similarity analysis based on NCSG has a strong application value for identifying the same family of malware.

关键词： Training Software algorithms Resists Feature extraction Malware Strain Software engineering

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Optimal scheme for dynamic adjustment of active mirror in FAST system 2021

Optimal scheme for dynamic adjustment of active mirror in FA...

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2021 5th International Conference on Computer Science and Artificial Intelligence

作者： Hongchuan Zhou Jiaqi Liu Junyan Song Benchao Yang State Key Laboratory of Mathematical Engineering and advanced computing China

ISBN: (纸本)9781450384155

This paper mainly studies the optimal scheme of dynamic adjustment of active mirror in FAST system by establishing mathematical model. According to the constraints such as distance variation range, a mathematical model describing the dynamic trajectory of the position and angle of the active mirror is established, and the correctness of the solution of the model is verified by simulation. The optimal surface model is selected from all the results. When the measured object is directly above the fast system, it is modeled based on the ideal parabola to ensure that the expansion constraints and threshold conditions of the actuator are met. Because the distribution of actuator and cable in the whole fast system is not a uniform sphere, the change of radial distance of cable can be considered as the expansion of actuator; In addition, the model is optimized from two aspects: adjusting the position of focus on the horizontal plane and changing the size of zoom alignment. From these two aspects, the parabolic focusing range satisfying the expansion range is obtained. Finally, the optimization strategy of dynamic adjustment of active mirror in fast system is given.

关键词： Dimension reduction to simplify mathematical modeling Optimization model

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Research on Unpredetermined Behavior Recognition Techniques Based on Network Attacks 23

Research on Unpredetermined Behavior Recognition Techniques ...

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Proceedings of the 2023 5th International Conference on Internet of Things, Automation and Artificial Intelligence

作者： Yuxuan Xiao Jinlong Fei Junyi Wang State Key Laboratory of Mathematical Engineering and Advanced Computing China

ISBN: (纸本)9798400716485

With the increasing number of cyber-attack behaviors, it has caused great harm to the contemporary society. Currently, the cyber attack behavior recognition module is usually implemented by adopting a strategy based on rule-base matching, which often fails to identify the attack behaviors when faced with unpreset path attack behaviors of the actual attack process due to its reliance on predefined abnormal behavior patterns and attack labels. Therefore, this paper proposes an efficient and accurate unpreset behavior detection framework for cyber-attack behaviors, introduces machine learning techniques into the identification of unpreset attack behaviors, proposes an RF-Corr feature importance assessment method based on the Kendall correlation coefficient between features and the feature weight values, and designs an unpreset attack behavior identification method based on BiLSTM neural network, which improves the current effectiveness of identification and detection for unpresetted behavior of cyber attacks, and provides a solution for artificial intelligence detection of unpresetted behavior of cyber attacks.

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Towards optimized tensor code generation for deep learning on sunway many-core processor

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Frontiers of Computer Science 2024年第2期18卷 1-15页

作者： Mingzhen LI Changxi LIU Jianjin LIAO Xuegui ZHENG Hailong YANG Rujun SUN Jun XU Lin GAN Guangwen YANG Zhongzhi LUAN Depei QIAN State Key Laboratory of Software Development Environment Beijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China National University of Singapore Singapore 119077Singapore State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214000China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing 100854China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application *** the existing deep learning compilers,TVM is well known for its efficiency in code generation and optimization across diverse hardware *** the meanwhile,the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning *** paper combines the trends in these two ***,we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as *** addition,we leverage the architecture features during the compilation such as core group for massive parallelism,DMA for high bandwidth memory transfer and local device memory for data locality,in order to generate efficient codes for deep learning workloads on *** experiment results show that the codes generated by swTVM achieve 1.79x improvement of inference latency on average compared to the state-of-the-art deep learning framework on Sunway,across eight representative *** work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in *** believe this work will encourage more people to embrace the power of deep learning and Sunwaymany-coreprocessor.

关键词： sunway processor deep learning compiler code generation performance optimization

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An Abnormal Domain Name Generation Method Based on a Character-Level Model 22

An Abnormal Domain Name Generation Method Based on a Charact...

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Proceedings of the 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence

作者： Chunbing Wu Jinlong Fei State Key Laboratory of Mathematical Engineering and Advanced Computing China

ISBN: (纸本)9781450398343

Internet technology develops by leaps and bounds, new types of botnet attacks are springing up. Currently, most malware are stealthy when implementing attacks since they can bypass detection strategies such as blacklists and sinkholes. These malware mainly use DGA to generate many random domain names to prevent C&C server IP addresses from being traced to the source. To resist botnet attacks, machine-learning methods have been proposed for detecting abnormal domain names. However, there are still some inadequacies, e.g., detection methods based on manual feature extraction couldn't identify novel variant DGA domains, and existing neural network detection methods have weak generalization ability and lack training data for new variant DGA domain names. To address the above problems, this paper proposes an abnormal domain name data generation technology based on a character-level model. Firstly, combining specific text generation-related ideas improves the character-level language model. Secondly, the Bi-GRU network model and attention mechanism are used to thoroughly learn the internal correlation features, and simulate the generation of abnormal domain names. The experimental results show that this method's abnormal domain name data is highly similar to the actual domain name in relative entropy and character composition. Furthermore, it verifies the feasibility of the generated data as training data set for a new variant DGA domain name, which to a certain extent, helps to improve the generalization ability of the abnormal domain name detection model.

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