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Research and Implementation of Hybrid Encryption System Based on SM2 and SM4 Algorithm

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Research and Implementation of Hybrid Encryption System Base...

作者： Zhiqiang Wang Hongyu Dong Yaping Chi Jianyi Zhang Tao Yang Qixu Liu Beijing Electronic Science and Technology Institute State Information Center Key Lab of Information Network Security Ministry of Public Security Key Laboratory of Network Assessment Technology Institute of Information EngineeringChinese Academy of Sciences

In recent years,the mobile platform network communication performance has made significant progress,with an increasingly large user base and market *** the same time,it also brings many problems such as the leakage of sensitive information after the loss of smart phones,the theft and tampering of private *** at the above problems,this paper proposes a hybrid cryptosystem based on the national secret algorithm SM2 and SM4 and its system solution for implementing secure *** asymmetric encryption algorithm SM2 and the symmetric encryption algorithm SM4 are combined to encrypt the key and the plaintext information of the symmetric encryption algorithm to achieve the double encryption *** experimental results show that the system designed in this paper improves the security of information transmission and key sharing.

关键词： Android National secret hybrid algorithm Information encryption Data Security Terminal interaction

Dynamics and Resonance Fluorescence from a Superconducting Artificial Atom Doubly Driven by Quantized and Classical Fields

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arXiv 2024年

作者： Ruan, Xinhui Wang, Jia-Heng He, Dong Song, Pengtao Li, Shengyong Zhao, Qianchuan Kuang, L.M. Tsai, Jaw-Shen Zou, Chang-Ling Zhang, Jing Zheng, Dongning Astafiev, O.V. Liu, Yu-Xi Peng, Zhihui Department of Automation Tsinghua University Beijing100084 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center of Quantum Effects and Applications Hunan Normal University Changsha410081 China School of Integrated Circuits Tsinghua University Beijing100084 China Institute of Physics Chinese Academy of Sciences Beijing100190 China School of Physical Sciences University of Chinese Academy of Sciences Beijing100190 China Center for Quantum Computing RIKEN Saitama351-0198 Japan Graduate School of Science Tokyo University of Science 1-3 Kagurazaka Shinjuku Tokyo162-0825 Japan CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei Anhui230026 China Hefei National Laboratory Hefei230088 China School of Automation Science and Engineering Xi'an Jiaotong University Xi’an710049 China MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xi’an710049 China Skolkovo Institute of Science and Technology Nobel str. 3 Moscow143026 Russia Moscow Institute of Physics and Technology Institutskiy Pereulok 9 Dolgoprudny141701 Russia Royal Holloway University of London Surrey EghamTW20 0EX United Kingdom

We report an experimental demonstration of resonance fluorescence in a two-level superconducting artificial atom under two driving fields coupled to a detuned cavity. One of the fields is classical and the other is varied from quantum (vacuum fluctuations) to classical one by controlling the photon number inside the cavity. The device consists of a transmon qubit strongly coupled to a one-dimensional transmission line and a coplanar waveguide resonator. We observe a sideband anti-crossing and asymmetry in the emission spectra of the system through a one-dimensional transmission line, which is fundamentally different from the weak coupling case. By changing the photon number inside the cavity, the emission spectrum of our doubly driven system approaches to the case when the atom is driven by two classical bichromatic fields. We also measure the dynamical evolution of the system through the transmission line and study the properties of the first-order correlation function, Rabi oscillations and energy relaxation in the system. The study of resonance fluorescence from an atom driven by two fields promotes understanding decoherence in superconducting quantum circuits and may find applications in superconducting quantum computing and quantum networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Electric lines

Graph representation learning via graphical mutual information maximization

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arXiv 2020年

作者： Peng, Zhen Huang, Wenbing Luo, Minnan Zheng, Qinghua Rong, Yu Xu, Tingyang Huang, Junzhou Ministry of Education Key Lab for Intelligent Networks and Network Security School of Computer Science and Technology Xi'an Jiaotong University China State Key Lab of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University China Tencent AI Lab China

The richness in the content of various information networks such as social networks and communication networks provides the unprecedented potential for learning high-quality expressive representations without external supervision. This paper investigates how to preserve and extract the abundant information from graph-structured data into embedding space in an unsupervised manner. To this end, we propose a novel concept, Graphical Mutual Information (GMI), to measure the correlation between input graphs and high-level hidden representations. GMI generalizes the idea of conventional mutual information computations from vector space to the graph domain where measuring mutual information from two aspects of node features and topological structure is indispensable. GMI exhibits several benefits: First, it is invariant to the isomorphic transformation of input graphs-an inevitable constraint in many existing graph representation learning algorithms;Besides, it can be efficiently estimated and maximized by current mutual information estimation methods such as MINE;Finally, our theoretical analysis confirms its correctness and rationality. With the aid of GMI, we develop an unsupervised learning model trained by maximizing GMI between the input and output of a graph neural encoder. Considerable experiments on transductive as well as inductive node classification and link prediction demonstrate that our method outperforms state-of-the-art unsupervised counterparts, and even sometimes exceeds the performance of supervised ones. Copyright © 2020, The Authors. All rights reserved.

关键词： Vector spaces

A value-function-based interior-point method for non-convex bi-level optimization

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arXiv 2021年

作者： Liu, Risheng Liu, Xuan Yuan, Xiaoming Zeng, Shangzhi Zhang, Jin DUT-RU International School of Information Science and Engineering Dalian University of Technology Key Laboratory for Ubiquitous Network Service Software of Liaoning Province Pazhou Lab Guangzhou China Department of Mathematics The University of Hong Kong Department of Mathematics Southern University of Science and Technology National Center for Applied Mathematics Shenzhen China

Bi-level optimization model is able to capture a wide range of complex learning tasks with practical interest. Due to the witnessed efficiency in solving bi-level programs, gradient-based methods have gained popularity in the machine learning community. In this work, we propose a new gradient-based solution scheme, namely, the Bilevel Value-Function-based Interior-point Method (BVFIM). Following the main idea of the log-barrier interior-point scheme, we penalize the regularized value function of the lower level problem into the upper level objective. By further solving a sequence of differentiable unconstrained approximation problems, we consequently derive a sequential programming scheme. The numerical advantage of our scheme relies on the fact that, when gradient methods are applied to solve the approximation problem, we successfully avoid computing any expensive Hessian-vector or Jacobian-vector product. We prove the convergence without requiring any convexity assumption on either the upper level or the lower level objective. Experiments demonstrate the efficiency of the proposed BVFIM on non-convex bi-level problems. © 2021, CC BY.

关键词： Gradient methods

Semantics-Guided Contrastive network for Zero-Shot Object Detection

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arXiv 2021年

作者： Yan, Caixia Chang, Xiaojun Luo, Minnan Liu, Huan Zhang, Xiaoqin Zheng, Qinghua The Ministry of Education Key Lab of Intelligent Networks and Network Security National Engineering Lab for Big Data Analytics School of Electronic and Information Engineering Xi'an Jiaotong University Shaanxi710049 China The ReLER Lab AAAI Faculty of Engineering and Information Technology University of Technology Sydney Australia The College of Computer Science and Artificial Intelligence Wenzhou University China

Zero-shot object detection (ZSD), the task that extends conventional detection models to detecting objects from unseen categories, has emerged as a new challenge in computer vision. Most existing approaches tackle the ZSD task with a strict mapping-transfer strategy that may lead to suboptimal ZSD results: 1) the learning process of these models neglects the available semantic information on unseen classes, which can easily bias towards the seen categories;2) the original visual feature space is not well-structured for the ZSD task due to the lack of discriminative information. To address these issues, we develop a novel Semantics-Guided Contrastive network for ZSD, named ContrastZSD, a detection framework that first brings contrastive learning mechanism into the realm of zero-shot detection. Particularly, ContrastZSD incorporates two semantics-guided contrastive learning subnets that contrast between region-category and region-region pairs respectively. The pairwise contrastive tasks take advantage of supervision signals derived from both the ground truth label and class similarity information. By performing supervised contrastive learning over those explicit semantic supervision, the model can learn more knowledge about unseen categories to avoid the bias problem to seen concepts, while optimizing the visual data structure to be more discriminative for better visual-semantic alignment. Extensive experiments are conducted on two popular benchmarks for ZSD, i.e., PASCAL VOC and MS COCO. Results show that our method outperforms the previous state-of-the-art on both ZSD and generalized ZSD tasks. © 2021, CC BY.

关键词： Object detection

A Method of Shadowsocks(R) Traffic Identification Based on Protocol Analysis

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A Method of Shadowsocks(R) Traffic Identification Based on P...

International Conference on Communication technology (ICCT)

作者： Jie Luo Liang Bao Lvlin Ni Science and Technology on Communication Security Laboratory The 30th Research Institute of China Electronics Technology Group Corporation Chengdu Sichuan China Key Lab of Information Network Security of Ministry of Public Security The Third Research Institute of Ministry of Public Security Shanghai China

ISBN: (纸本)9781665432078

Shadowsocks (R) is a proxy software based on Socks5, which is the collective name of Shadowsocks and Shadowsocks (R). In order to improves the shortcomings of other softwares breaking internet censorship, Shadowsocks (R) is a private protocol without handshake negotiation mechanism, and pays more attention to high camouflage and high concealment of traffic, which makes it difficult to use common methods(fingerprint identification, port filtering)to identify Shadowsocks (R) traffic[1], This paper proposes a method of Shadowsocks (R) traffic identification from the perspective of the protocol, which extracts features from four dimensions, and inputs features into the machine learning model for training. This method performs secondary identification and statistical filtering on the output of the machine learning model, and finally identifys Shadowsocks (R) traffic. This method has been very well validated in the laboratory environment.

关键词： Training Ports (computers) Protocols laboratories Machine learning Reconnaissance Feature extraction

DQ-RM: Deep Reinforcement Learning-based Route Mutation Scheme for Multimedia Services 16

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DQ-RM: Deep Reinforcement Learning-based Route Mutation Sche...

16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020

作者： Zhang, Tao Xu, Changqiao Zhang, Bingchi Kuang, Xiaohui Wang, Yue Yang, Shujie Muntean, Gabriel-Miro State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China School of International Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Science and Technology on Information System Security Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Performance Engineering Lab. School of Electronic Engineering Dublin City University Dublin Ireland

ISBN: (纸本)9781728131290

Increasingly growing various multimedia services (e.g., interactive live video and so on) have brought tremendous pressure on existing static defense techniques. To cope with inherent drawback of static defense techniques, network Moving Target Defense (NMTD) such as route mutation (RM) was proposed. What's more, applying reinforcement learning (RL) into RM has been proved feasible in our previous work. But two main problems still need to be considered in this combination of RL with RM: 1) It lacks the consideration of multiple flows situation. 2) With the state-action space grow larger, current solution can't handle efficiently. In this paper, we propose a deep Q-learning method for RM (DQ-RM) to solve above two problems. Firstly, benefited from the satisfiability module theory, we formalize RM space considering single flow and multiple flows concurrently. Then we further propose a deep reinforcement learning-based RM scheme based on our previous work, which is suitable for large-scale state-action space. Finally, extensive experimental results highlight the improvement of DQ-RM in defense performance and convergence speed compared to the representative solution. © 2020 IEEE.

关键词： Reinforcement learning

A Novel Configurable High-precision and Low-cost Circuit Design of Sigmoid and Tanh Activation Function

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A Novel Configurable High-precision and Low-cost Circuit Des...

IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA)

作者： Feng Liu Bowen Zhang Gang Chen Guoliang Gong Huaxiang Lu Wenchang Li Institute of Semiconductors Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Semiconductor Neural Network Intelligent Perception and Computing Technology Beijing Key Lab Beijing China Center for Excellence in Brain Science and Intelligence Technology CAS Shanghai China

ISBN: (纸本)9781665417488

This paper proposes a configurable Sigmoid and Tanh activation function circuit design by second-order approximation and deviation compensation. The proposed circuit is synthesized in TSMC 180nm technology within a 7218 um 2 area at 500MHz, it is also deployed on Xilinx ZC706 FPGA by 158 LUTs and 46 FFs at 333MHz. It achieves almost 4.68× better precision and 20.2% less area than previous works. The experimental results show that our method has almost no accuracy loss on LSTM Chinese speech recognition tasks and the YOLOv3 image recognition post-processing task.

关键词： Integrated circuit technology Image recognition Costs Conferences Speech recognition Hardware Circuit synthesis

Continuous Distributed Processing of Software Defined Radar

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Continuous Distributed Processing of Software Defined Radar

IEEE International Conference on Radar

作者： Bing Li Qiang Qiu Shiqi Gong Yongjun Liu Yu Lei CAS Key Lab of Network Data Science and Technology Chinese Academy of Sciences Institute of Computing Technology Beijing China State Key Laboratory of Internet of Things for Smart City University of Macau Macau China National Laboratory of Radar Signal Processing Xidian University Xi'an China Golaxy Data Technology Co. Ltd. Beijing China

ISBN: (纸本)9781665468893

Software-defined radar has been an active research field for more than ten years. However, the low performance and low scalability of the traditional processing techniques of SDR make it hard to deal with complex radar tasks. The stream processing engine is a software that supports low latency and high scalability distributed data processing. It is widely used in log, image, and video processing scenarios but rarely used in radar applications. This paper reviews processing software-defined radar data by using the stream processing engine, and we illustrate the differences between processing log data and radar data. Furthermore, we discuss some optimizations for stream processing engines to get better performance in the radar data processing.

关键词： Protocols Scalability Streaming media Radar imaging Data processing Software Hardware