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Chinese Journal of Electronics 2024年第1期33卷 128-138页

作者： Jinxue PENG Yong WANG Jingfeng XUE Zhenyan LIU school of Computer Science and technology Beijing Institute of Technology

Cross-platform binary code similarity detection aims at detecting whether two or more pieces of binary code are similar or not. Existing approaches that combine control flow graphs(CFGs)-based function representation and graph convolutional network(GCN)-based similarity analysis are the best-performing ones. Due to a large amount of convolutional computation and the loss of structural information, the use of convolution networks will inevitably bring problems such as high overhead and sometimes inaccuracy. To address these issues, we propose a fast cross-platform binary code similarity detection framework that takes advantage of natural language processing(NLP)and inductive graph neural network(GNN) for basic blocks embedding and function representation respectively by simulating extracting structural features and temporal features. GNN’s node-centric and small batch is a suitable training way for large CFGs, it can greatly reduce computational overhead. Various NLP basic block embedding models and GNNs are evaluated. Experimental results show that the scheme with long short term memory(LSTM)for basic blocks embedding and inductive learning-based Graph SAGE(GAE) for function representation outperforms the state-of-the-art works. In our framework, we can take only 45% overhead. Improve efficiency significantly with a small performance trade-off.

关键词： Control flow graph Natural language processing Inductive graph neural network Binary code similarity detection

Hybrid tri-memristor hyperchaotic map and application in Wasserstein Generative Adversarial Nets

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Science China Technological Sciences 2024年第6期67卷 1855-1865页

作者： GU Yang BAO Han YU XiHong HUA ZhongYun BAO BoCheng XU Quan school of Microelectronics and Control Engineering Changzhou University school of Computer Science and technology Harbin Institute of Technology

Inspired by basic circuit connection methods, memristors can also be utilized in the construction of complex discrete chaotic systems. To investigate the dynamical effects of hybrid memristors, we propose two hybrid tri-memristor hyperchaotic(HTMH)mapping structures based on the hybrid parallel/cascade and cascade/parallel operations, respectively. Taking the HTMH mapping structure with hybrid parallel/cascade operation as an example, this map possesses a spatial invariant set whose stability is closely related to the initial states of the memristors. Dynamics distributions and bifurcation behaviours dependent on the control parameters are explored with numerical tools. Specifically, the memristor initial offset-boosting mechanism is theoretically demonstrated, and memristor initial offset-boosting behaviours are numerically verified. The results clarify that the HTMH map can exhibit hyperchaotic behaviours and extreme multistability with homogeneous coexisting infinite attractors. In addition, an FPGA hardware platform is fabricated to implement the HTMH map and generate pseudorandom numbers(PRNs)with high randomness. Notably, the generated PRNs can be applied in Wasserstein generative adversarial nets(WGANs) to enhance training stability and generation capability.

关键词： memristor hybrid tri-memristor map spatial invariant set hyperchaos extreme multistability generative adversarial nets

HEN:a novel hybrid explainable neural network based framework for robust network intrusion detection

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Science China(Information Sciences) 2024年第7期67卷 72-90页

作者： Wei WEI Sijin CHEN Cen CHEN Heshi WANG Jing LIU Zhongyao CHENG Xiaofeng ZOU school of Computer Science and Engineering Xi'an University of TechnologyShaanxi Key Laboratory for Network Computing and Security Technology school of Computer Science and technology Wuhan University of Science and Technology school of Future technology South China University of Technology Shenzhen Research Institute of Hunan University school of Computer Science Hunan University of Technology and Business Institute for Infocomm Research (I2R) Agency for Science Technology and Reseach (A*STAR)

With the rapid development of network technology and the automation process for 5G, cyberattacks have become increasingly complex and threatening. In response to these threats, researchers have developed various network intrusion detection systems(NIDS) to monitor network traffic. However, the incessant emergence of new attack techniques and the lack of system interpretability pose challenges to improving the detection performance of NIDS. To address these issues, this paper proposes a hybrid explainable neural network-based framework that improves both the interpretability of our model and the performance in detecting new attacks through the innovative application of the explainable artificial intelligence(XAI)method. We effectively introduce the Shapley additive explanations(SHAP) method to explain a light gradient boosting machine(Light GBM) model. Additionally, we propose an autoencoder long-term short-term memory(AE-LSTM) network to reconstruct SHAP values previously generated. Furthermore, we define a threshold based on reconstruction errors observed during the training phase. Any network flow that surpasses the specified threshold is classified as an attack flow. This approach enhances the framework's ability to accurately identify attacks. We achieve an accuracy of 92.65%, a recall of 95.26%, a precision of 92.57%,and an F1-score of 93.90% on the dataset NSL-KDD. Experimental results demonstrate that our approach generates detection performance on par with state-of-the-art methods.

关键词： explainable artificial intelligence light gradient boosting machine machine learning network intrusion detection Shapley additive explanation hybrid explainable neural network (HEN)

Effect of strain on structure and electronic properties of monolayer C_(4)N_(4)

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Chinese Physics B 2024年第5期33卷 595-600页

作者：陈昊徐瑛赵家石周丹 school of Physics Changchun University of Science and TechnologyChangchun 130022China school of Computer Science and technology Changchun University of Science and TechnologyChangchun 130022China

The first-principles calculations are performed to examine structural,mechanical,and electronic properties at large strain for a monolayer C_(4)N_(4),which has been predicted as an anchoring promising material to attenuate shuttle effect in Li–S batteries stemming from its large absorption energy and low diffusion energy barrier.Our results show that the ideal strengths of C_(4)N_(4)under tension and pure shear deformation conditions reach 13.9 GPa and 12.5 GPa when the strains are 0.07 and 0.28,respectively.The folded five-membered rings and diverse bonding modes between carbon and nitrogen atoms enhance the ability to resist plastic deformation of C_(4)N_(4).The orderly bond-rearranging behaviors under the weak tensile loading path along the[100]direction cause the impressive semiconductor–metal transition and inverse semiconductor–metal transition.The present results enrich the knowledge of the structure and electronic properties of C_(4)N_(4)under deformations and shed light on exploring other two-dimensional materials under diverse loading conditions.

关键词： two-dimensional materials strain effect structural evolution electronic properties

Comparison of tailpipe carbonyls and volatile organic compounds emissions from in-use gasoline/CNG bi-fuel vehicles

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Journal of Environmental Sciences 2024年第1期135卷 619-629页

作者： Jiachen Li Yunshan Ge Xin Wang Mengzhu Zhang Haohao Wang school of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China

Ground-level ozone contamination has been globally an urban air quality issue,particularly for China,which has recently made significant progress in purifying its sky.Unregulated exhaust emissions from motor vehicles,predominantly carbonyls and volatile organic compounds(VOCs),are among the leading contributors to ozone formation.In this chassislevel study,the unregulated emissions from five China-5 certified gasoline/CNG bi-fuel taxis,along with their ozone forming potential(OFP),were evaluated.It is found that carbonyls and VOCs were mainly emitted during the starting phase no matter the engine was cold or hot.Compared to gasoline,CNG fueling reduced VOCs emissions on a large scale,especially in the starting phase,but had elevated carbonyls.On a fleet average,CNG fueling derived 15%and 46%less OFP than gasoline in cold-and hot-start tests,respectively.VOCs contributed to over 90%of the total OFP of the exhaust.In terms of alleviating ground-level ozone contamination,CNG is a feasible alternative to gasoline on light-duty vehicles.

关键词： CNG Gasoline Carbonyls VOCs OFP

A lightweight symmetric image encryption cryptosystem in wavelet domain based on an improved sine map

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Chinese Physics B 2024年第3期33卷 266-276页

作者：陈柏池黄林青蔡述庭熊晓明张慧 school of Advanced Manufacturing Guangdong University of TechnologyJieyang 522000China school of Integrated Circuits Guangdong University of TechnologyGuangzhou 510006China school of Automation Guangdong University of TechnologyGuangzhou 510006China

In the era of big data,the number of images transmitted over the public channel increases exponentially.As a result,it is crucial to devise the efficient and highly secure encryption method to safeguard the sensitive image.In this paper,an improved sine map(ISM)possessing a larger chaotic region,more complex chaotic behavior and greater unpredictability is proposed and extensively tested.Drawing upon the strengths of ISM,we introduce a lightweight symmetric image encryption cryptosystem in wavelet domain(WDLIC).The WDLIC employs selective encryption to strike a satisfactory balance between security and speed.Initially,only the low-frequency-low-frequency component is chosen to encrypt utilizing classic permutation and diffusion.Then leveraging the statistical properties in wavelet domain,Gaussianization operation which opens the minds of encrypting image information in wavelet domain is first proposed and employed to all sub-bands.Simulations and theoretical analysis demonstrate the high speed and the remarkable effectiveness of WDLIC.

关键词： image encryption discrete wavelet transform 1D-chaotic system selective encryption Gaussianization operation

PURP: A Scalable System for Predicting Short-Term Urban TrafficFlow Based on License Plate Recognition Data

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Big Data Mining and Analytics 2024年第1期7卷 171-187页

作者： Shan Zhang Qinkai Jiang Hao Li Bin Cao Jing Fan school of Computer Science and technology Zhejiang University of TechnologyHangzhou 310000China

Accurate and efficient urban traffic flow prediction can help drivers identify road traffic conditions in real-time,consequently helping them avoid congestion and accidents to a certain extent.However,the existing methods for real-time urban traffic flow prediction focus on improving the model prediction accuracy or efficiency while ignoring the training efficiency,which results in a prediction system that lacks the scalability to integrate real-time traffic flow into the training procedure.To conduct accurate and real-time urban traffic flow prediction while considering the latest historical data and avoiding time-consuming online retraining,herein,we propose a scalable system for Predicting short-term URban traffic flow in real-time based on license Plate recognition data(PURP).First,to ensure prediction accuracy,PURP constructs the spatio-temporal contexts of traffic flow prediction from License Plate Recognition(LPR)data as effective characteristics.Subsequently,to utilize the recent data without retraining the model online,PURP uses the nonparametric method k-Nearest Neighbor(namely KNN)as the prediction framework because the KNN can efficiently identify the top-k most similar spatio-temporal contexts and make predictions based on these contexts without time-consuming model retraining online.The experimental results show that PURP retains strong prediction efficiency as the prediction period increases.

关键词： traffic flow prediction k-Nearest Neighbor(KNN) License Plate Recognition(LPR)data spatio-temporalcontext

Sawtooth-enhanced bend sensor for gesture recognition

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Science China Technological Sciences 2024年第6期67卷 1727-1736页

作者： BAI YanRu ZHANG ZiHang WANG HaoYu GUO Rui LI XiSheng school of Automation and Electrical Engineering University of Science and Technology Beijing school of Advanced Engineering University of Science and Technology Beijing school of Precision Instrument and Opto-Electronics Engineering Tianjin University school of Computer and Communication Engineering University of Science and Technology Beijing

Gesture recognition has diverse application prospects in the field of human-computer interaction. Recently, gesture recognition devices based on strain sensors have achieved remarkable results, among which liquid metal materials have considerable advantages due to their high tensile strength and conductivity. To improve the detection sensitivity of liquid metal strain sensors,a sawtooth-enhanced bending sensor is proposed in this study. Compared with the results from previous studies, the bending sensor shows enhanced resistance variation. In addition, combined with machine learning algorithms, a gesture recognition glove based on the sawtooth-enhanced bending sensor is also fabricated in this study, and various gestures are accurately identified. In the fields of human-computer interaction, wearable sensing, and medical health, the sawtooth-enhanced bending sensor shows great potential and can have wide application prospects.

关键词： liquid metal bending sensor gesture recognition machine learning

Neural network study of the nuclear ground-state spin distribution within a random interaction ensemble

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Nuclear Science and Techniques 2024年第3期35卷 216-227页

作者： Deng Liu Alam Noor A Zhen-Zhen Qin Yang Lei school of Mathematics and Physics Southwest University of Science and TechnologyMianyang 621010China school of Nuclear Science and technology Southwest University of Science and TechnologyMianyang 621010China

The distribution of the nuclear ground-state spin in a two-body random ensemble(TBRE)was studied using a general classification neural network(NN)model with two-body interaction matrix elements as input features and the corresponding ground-state spins as labels or output predictions.The quantum many-body system problem exceeds the capability of our optimized NNs in terms of accurately predicting the ground-state spin of each sample within the TBRE.However,our NN model effectively captured the statistical properties of the ground-state spin because it learned the empirical regularity of the ground-state spin distribution in TBRE,as discovered by physicists.

关键词： Neural network Two-body random ensemble Spin distribution of nuclear ground state

Protein crystallization with DNA templates

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Particuology 2024年第3期86卷 262-268页

作者： Bo Zhang Jia Yu Zhe Pu Wenhui Guo Ruoqi Yang Yu Wen Zhijiang Rong Hui Wang Xiaoli Wu Bo Wang Hongyan Sun school of Environment and Resources Taiyuan University of Science and TechnologyTaiyuan030024China school of Chemical Engineering and technology Taiyuan University of Science and TechnologyTaiyuan030024China

Protein crystallization plays a significant role in three-dimensional structural analysis and protein pu-rification.It is important to increase the crystallization efficiency,which is possible by adding hetero-geneous templates in crystallization systems.DNA is biologically compatible and artificially designable polymer,which is easy to extract.In this study,single-and double-stranded DNA of precise sequences were designed and used as templates to promote protein crystallization of lysozyme and catalase.In-fluence of DNA,single-stranded DNA with 10,20,40 bases and double-stranded DNA with 10,20,40 base pairs,were investigated.The success rate of obtaining crystals of lysozyme and catalase in equal period was significantly improved with the addition of DNA comparing without templates added.Double-stranded DNA led to higher nucleation rate than that with single-stranded DNA.The promotion of nucleation was more obvious at low concentration of protein solution and with longer chain DNA templates.Crystal number and crystallization rate was enhanced with addition of long double-stranded DNA templates.All the results confirm that DNA is an effective polymer additive to enhance protein crystallization,especially for the application of the scarce protein crystallization.

关键词： DNA Polymer Additive Protein Crystallization