Table of Content

    20 June 2021 Volume 48 Issue 3
    Advances in Vehicular Network Technologies
    Algorithm for detection of twice iterative signals for LTE-V2X systems
    LIAO Yong,SUN Ning,WANG Shuai,CHEN Ying
    Journal of Xidian University. 2021, 48(3):  1-8.  doi:10.19665/j.issn1001-2400.2021.03.001
    Abstract ( 134 )   HTML ( 227 )   PDF (986KB) ( 120 )   Save
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    Aiming at the traditional signal detection algorithms that cannot effectively deal with the inter carrier interference problem,this paper proposes an improved iterative parallel interference cancellation signal detection algorithm based on the Gauss Seidel algorithm.The proposed algorithm first uses the Gaussian Seidel algorithm with adaptive iterations to obtain the rough estimation of the signal to be detected,and then uses the iterative parallel interference cancellation algorithm to iteratively optimize the initial solution,and introduces the early update mechanism in the iterative process.Simulation results show that the detection performance of the proposed algorithm is better than that of the traditional iterative parallel interference cancellation algorithm in LTE-V2X communication systems,and approaches the performance of the optimal initial solution algorithm.The algorithm proposed in this paper achieves a better detection performance with a lower computational complexity,which provides a new idea for the design of the signal detection algorithm for LTE-V2X systems.

    Game theory based incentive scheme for cooperative downloading in the internet of vehicles
    LAI Chengzhe,CHEN Yao,GUO Qili,ZHENG Dong
    Journal of Xidian University. 2021, 48(3):  9-20.  doi:10.19665/j.issn1001-2400.2021.03.002
    Abstract ( 57 )   HTML ( 31 )   PDF (2104KB) ( 32 )   Save
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    This paper proposes a cooperative downloading incentive scheme based on a two-layer game in the Internet of Vehicles,in which the reverse auction mechanism is used to select cooperatively downloading vehicles,and the evolutionary game solves the problem of vehicles participating in data forwarding.At the same time,the use of sequential aggregated signature technology ensures the security of the data transmission process.In addition,the Bitcoin-based timing commitment mechanism realizes electronic currency incentives and ensures the fairness of transaction payments.Finally,theoretical analysis shows that the scheme achieves data integrity,identity authentication,and non-repudiation,can effectively promote vehicle cooperation and achieve fairness in transactions.Simulation results prove the effectiveness of the scheme in terms of communication cost and storage cost.

    Method for using the blockchain to protect data privacy of IoV
    YANG Yanbo,ZHANG Jiawei,MA Jianfeng
    Journal of Xidian University. 2021, 48(3):  21-30.  doi:10.19665/j.issn1001-2400.2021.03.003
    Abstract ( 65 )   HTML ( 36 )   PDF (1604KB) ( 31 )   Save
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    With the rapid development of the Internet of Things,vehicle industry is undergoing a rapid transformation from the early vehicle Ad-Hoc network to the Internet of Vehicles (IoV) in which massive vehicles and on-board smart devices can realize data sharing and interaction.However,this brings about a big challenge for the security of shared data in a huge volume containing a lot of user privacy,especially the issues of access control,trust and key escrow.To address these problems,we propose an efficient blockchain-based distributed data sharing method in IoV.In our scheme,blockchain transactions are utilized for trustworthily data storage and access as well as user revocation.To realize end device verification,we introduce certificateless cryptography for transaction verification of blockchain nodes and the ease of the key escrow problem.Moreover,we propose an efficient partial policy-hidden CP-ABE scheme for data fine-grained access control with efficient user revocation.Security analysis and experiments demonstrate that our scheme is secure,efficient and practical.

    On the security of the intrusion detection system in smart vehicles
    CHAI Yanna,LI Kunlun,SONG Huansheng
    Journal of Xidian University. 2021, 48(3):  31-39.  doi:10.19665/j.issn1001-2400.2021.03.004
    Abstract ( 28 )   HTML ( 3 )   PDF (2884KB) ( 15 )   Save
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    As the Internet of Vehicles matures and becomes widely used,the security challenges that come with it are increasing.Vehicular networks and smart cars are classic applications of inter-connected systems.Smart vehicles not only have networks connecting their internal components,but also are connected to the outside world by roadside units and other vehicles.In some cases,the internal and external network packets pass through the same hardware and are merely isolated by software defined rules.Any misconfiguration opens a window for the hackers to intrude into vehicles’ internal components,which can lead to disastrous outcomes.In this paper,we define two covert attacks in the context of cruise control and propose a novel intrusion detection and compensation method to disclose and respond to such attacks.First,we employ a neural network identifier in the IDS engine to estimate the system output dynamically and compare it with the ACC output.If any anomaly is detected,an embedded compensation controller kicks in and takes over the control of the system.We conducted extensive experiments in the MATLAB to evaluate the effectiveness of the proposed scheme in a simulated environment.

    Method for estimation of vehicular network traffic for smart transportations
    LING Min,LUO Ying,YUAN Liang,JIN Chuanxue
    Journal of Xidian University. 2021, 48(3):  40-48.  doi:10.19665/j.issn1001-2400.2021.03.005
    Abstract ( 34 )   HTML ( 7 )   PDF (1607KB) ( 21 )   Save
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    With the rapid deployment of 5G networks,the Internet of Vehicles (IoV),Internet of Things (IoT),and edge computing have made a great progress,which leads to vehicular network traffic measurements facing many challenges.To this end,the present paper studies the vehicular network traffic estimation problem for smart cities.A software-defined network (SDN)-based vehicular network traffic estimation method is proposed.A coarse-grained measurement value via an SDN architecture is designed.A fine-grained measurement model based on the autoregressive moving average (ARMA) model is constructed.Finally,a heuristic algorithm is presented to obtain accurate estimation results for vehicular network traffic.Simulation results indicate that the method proposed in this paper is feasible and effective.

    Information and Communications Engineering & Mechanical Engineering
    Permutation codesable to correct stable and unstable burst erasure errors
    HE Yaping,HE Yucheng,ZHOU Lin
    Journal of Xidian University. 2021, 48(3):  49-55.  doi:10.19665/j.issn1001-2400.2021.03.006
    Abstract ( 23 )   HTML ( 4 )   PDF (656KB) ( 12 )   Save
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    In order to improve the reliability of flash memories,permutation is used to represent the rank modulation scheme of the flash cell charge.Error-correcting codes based on permutation groups can correct a variety of special error types that are caused by the rank modulation scheme.When a flash memory cell is damaged and the stored charge value cannot be read correctly,an erase error or a delete error may occur in the corresponding position.Aiming at the problem of the stability of burst erasure errors in rank modulation of the flash memory,a new construction of permutation codes combined with the permutation interleaving technique is proposed by the existing Levenshtein permutation code that can correct a single deletion error.The proposed construction can correct a single stable burst erasure and a single unstable burst erasure,respectively.Two corresponding decoding methods are presented in the proof of the proposed construction.The code construction and the decoding methods are validated with examples.

    Reactive relay selection strategy for NOMA systems
    SONG Chuanwang,LIU Dong,LI Enyu,ZHAO Ruishou,HAO Siyuan,SHEN Tianqi
    Journal of Xidian University. 2021, 48(3):  56-62.  doi:10.19665/j.issn1001-2400.2021.03.007
    Abstract ( 24 )   HTML ( 11 )   PDF (1644KB) ( 14 )   Save
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    Non-Orthogonal Multiple Access is considered to be a promising candidate for 5G multiple access schemes due to its superior spectral efficiency,higher throughput,and lower transmission delay.To improve the outage performance of non-orthogonal cooperative systems,non-orthogonal multiple access system is studied in the downlink scenario.First,a distributed reactive relay selection scheme based on the decode-and-forward protocol is designed.Then,the closed-form results of the exact outage performance and the approximate results in a high signal-to-noise ratio of the two users are derived.Finally,the effects of the relay position,channel quality and relay number on the outage probability of the system are analyzed by simulation.At the same time,compared with the partial relay selection strategy,the performance advantage of the proposed reactive relay selection strategy is verified.

    Video multipath transmission mechanism with load balancing in the FiWi network
    LÜ Yi,ZHU Bo,WU Dapeng
    Journal of Xidian University. 2021, 48(3):  63-70.  doi:10.19665/j.issn1001-2400.2021.03.008
    Abstract ( 20 )   HTML ( 4 )   PDF (1101KB) ( 15 )   Save
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    Aiming at the differentiated needs of users and the problem of video service interruption caused by the local overload of the converged network,a load-balanced video transmission mechanism is proposed.First,the split multipath routing protocol is improved to obtain the path selection model according to the front-end structural characteristics of the optical fiber wireless access network;second,we calculate the path differential delay and the video transmission delay on wireless side,and set their threshold as the quality of the experience constraint.Finally,while considering the quality of user experience,a particle swarm optimization algorithm with a multi-stage penalty function is used for video distribution.Simulation results show that the proposed mechanism is better than the throughput-aware load-sensing algorithms,enhances delay-control load distribution algorithms and delay-energy-quality aware multipath algorithms,and that it can effectively balance the network load and alleviate network congestion while avoiding user loss.

    Computer Science and Technology & Artificial Intelligence
    Deep consistency-preserving hashing
    SHI Juan,XIE De,JIANG Qing
    Journal of Xidian University. 2021, 48(3):  71-77.  doi:10.19665/j.issn1001-2400.2021.03.009
    Abstract ( 21 )   HTML ( 3 )   PDF (1403KB) ( 16 )   Save
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    At present,most existing cross-modal hashing methods fail to explore the relevance and diversity of different modality data,thus leading to unsatisfactory search performance.In order to solve the above problem,a simple yet efficient deep hashing model is proposed,named deep consistency-preserving hashing for cross-modal retrieval that simultaneously exploits modality-common representation and modality-private representation through the simple end-to-end network structure,and generates compact and discriminative hash codes for multiple modalities.Compared with other deep cross-modal hashing methods,the complexity and computation of the proposed method can be neglected with significant performance improvements.Comprehensive evaluations are conducted on three cross-modal benchmark datasets which illustrate that the proposed method is superior to the state-of-the-art cross-modal hashing methods.

    Many-objective particle swarm optimization algorithm for fitness ranking
    YANG Wusi,CHEN Li,WANG Yi,ZHANG Maosheng
    Journal of Xidian University. 2021, 48(3):  78-84.  doi:10.19665/j.issn1001-2400.2021.03.0010
    Abstract ( 31 )   HTML ( 9 )   PDF (2053KB) ( 24 )   Save
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    Due to the complexity and difficulty of solving the many-objective optimization problem,a many-objective particle swarm optimization algorithm for ensemble fitness ranking is proposed.In this algorithm,the nearest vector between the individual and reference points in the population is obtained,and the individuals in the population are sorted by the penalty-based boundary intersection approach.Then,the poor individuals in the population are deleted and the elite individuals are saved in the external archives.The four advanced many-objective evolutionary optimization algorithms are adopted to make comparisons on 5,8,10,15 objectives of 13 standard test sets.Experimental results show that the performance of the proposed algorithm is better than comparison algorithms in most of the test cases.It has also been proved that the algorithm has good convergence and diversity,and that it can effectively deal with many-objective optimization problems.

    Object detection based on the multiscale location Enhancement network
    WANG Ping,JIANG Yuze,ZHAO Guanghui
    Journal of Xidian University. 2021, 48(3):  85-90.  doi:10.19665/j.issn1001-2400.2021.03.011
    Abstract ( 43 )   HTML ( 5 )   PDF (1556KB) ( 26 )   Save
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    For the target detection task,there are two problems in the one-stage network structure of the deep neural network model.First,whether the design of the anchor box hyperparameter is suitable or not will affect the training results of the whole network;second,a large down sampling factor will affect the positioning ability of the target.To solve these problems,this paper proposes a multi-location enhancement network.The structure of the one-stage network model is redesigned,and a better scheme for selecting the super parameters of the anchor frame is proposed.So the efficiency of the first stage network is ensured and the positioning accuracy is better than the previous one.A large number of experiments show that the multi-location enhancement network can achieve a higher positioning accuracy while ensuring real-time performance.The average accuracy of 82.5 is achieved on the public dataset (Pascal VOC 2007).

    Combination of dynamic features with a new mask to optimize neural network speech enhancement
    MEI Shulin,JIA Hairong,WANG Xiaogang,WU Yifeng
    Journal of Xidian University. 2021, 48(3):  91-98.  doi:10.19665/j.issn1001-2400.2021.03.012
    Abstract ( 23 )   HTML ( 4 )   PDF (1900KB) ( 13 )   Save
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    Concerning the problem that the Neural Network speech enhancement algorithm cannot fully represent the nonlinear structure of speech due to feature selection,which leads to speech distortion.This paper proposes the combination of dynamic features with a new mask to optimize neural network speech enhancement.First,three features of noisy speech are extracted and spliced to obtain static features.Then,the first and second difference derivatives are obtained to capture the instantaneous signals of speech and fuse them into dynamic features.The combination of dynamic and static features completes internal complementarity of features and reduced speech distortion.Second,in order to enhance the intelligibility and clarity of speech at the same time,an adaptive mask is proposed,which can adjust the energy ratio of speech and noise as well as the ratio of the traditional mask and the square root mask.The Gammatone channel weight is used to modify the mask value in each channel to simulate the human auditory system and further improve the speech intelligibility.Finally,the simulation of multiple voices under different noise backgrounds shows that compared with different literature algorithms,the algorithm has a higher SNR,subjective speech quality and short-term objective intelligibility,which verifies the effectiveness of the algorithm.

    Effective learning strategy for hard samples
    CAO Yi,CAI Xiaodong
    Journal of Xidian University. 2021, 48(3):  99-105.  doi:10.19665/j.issn1001-2400.2021.03.013
    Abstract ( 20 )   HTML ( 3 )   PDF (1518KB) ( 9 )   Save
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    To improve the learning efficiency for hard samples and reduce noise interference caused by superfluous hard samples in deep hash algorithm,a generic strategy called Loss to Gradient for hard sample learning is proposed.First,a non-uniform gradient normalization method is proposed to improve the learning ability of models for hard samples.Back propagation gradients are weighted by calculating the loss ratio between hard samples and all samples.Furthermore,a weighted random sampling method is designed for accuracy improvement with superfluous hard samples.According to the loss,training samples are weighted and under-sampled for noise filtering and a small number of hard samples are retained to avoid over-fitting.Based on open datasets,the average accuracy of hash feature retrieval is increased by 4.7% and 3.4%,respectively.Experimental results show that the improved method outperforms other benchmarking methods in accuracy,proving that the feature representation of hard samples in the dataset can be effectively learned.

    Diversity controlled multiobjective particle swarm optimization
    LIU Tianyu,WANG Zhu
    Journal of Xidian University. 2021, 48(3):  106-114.  doi:10.19665/j.issn1001-2400.2021.03.014
    Abstract ( 35 )   HTML ( 11 )   PDF (2419KB) ( 21 )   Save
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    For solving the premature in traditional multiobjective particle swarm optimization,a multi-objective particle swarm optimization based on diversity control is proposed.The proposed algorithm utilizes a diversity metric,which is based on weight vectors,to evaluate the population diversity in each generation and control the evolution process of the algorithm adaptively.To maintain population diversity,an adaptive mutation strategy based on Steffensen’s method is adopted to update the repository population.With the purpose of balancing the population diversity and convergence,the global best positions of particles areselected adaptively.This algorithm is compared with several widely used multiobjective evolutionary algorithms on a set of benchmark test problems in the experimental part.Statistical results demonstrate the effectiveness of the proposed algorithm.

    Self-supervised facial asymmetry learning for automatic evaluation of facial paralysis
    SUN Haojie,LI Miaoyu,ZHANG Panpan,XU Pengfei
    Journal of Xidian University. 2021, 48(3):  115-122.  doi:10.19665/j.issn1001-2400.2021.03.015
    Abstract ( 26 )   HTML ( 2 )   PDF (1642KB) ( 17 )   Save
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    Facial paralysis is a kind of facial disease with the functional disorder of the facial expression muscle,and adversely affects the patients’ mental and physical health.The aided diagnosis and evaluation system of facial paralysis based on computer vision can reduce the influence of doctors’ subjective experience on the diagnosis results,and improve the accuracy and efficiency of diagnosis.However,the existing evaluation methods suffer mainly from three drawbacks:① the facial asymmetry features are extracted from the static facial images,which can not represent the asymmetrical features of facial movements;② the shallow machine learning models have their limitations on extracting useful facial features;③ it is difficult for depth models to learn the effective facial features from small-scaled videos of facial paralysis.To solve these problems,we present an automatic facial paralysis evaluation method based on self-supervised facial asymmetry learning (self-SFAL).The key idea behind our method is that the pre-trained 3D-CNNs on the pretext task are transferred to extract the patients’ facial spatiotemporal features for the downstream task of facial paralysis evaluation.With the assistance of the pretext task,the 3D-CNNs can leverage numerous videos without any labels,and can be easily pre-trained to adapt to our facial paralysis evaluation task.Furthermore,inspired by the doctor’s evaluating facial paralysis by focusing on the asymmetry of the facial muscle movements on both the patients’ whole faces and the involved facial regions,our method combines the global and local facial spatiotemporal features for the final facial paralysis evaluation.Experimental results have verified a better performance of the proposed method,with accuracy,Recall and F1 improved.

    Yarn-dyed shirt piece defect detection based on an unsupervised reconstruction model of the U-shaped denoising convolutional auto-encoder
    ZHANG Hongwei,TAN Quanlu,LU Shuai,GE Zhiqiang,XU Jian
    Journal of Xidian University. 2021, 48(3):  123-130.  doi:10.19665/j.issn1001-2400.2021.03.016
    Abstract ( 19 )   HTML ( 2 )   PDF (4168KB) ( 17 )   Save
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    Due to the scarcity of defective yarn-dyed fabric samples in the textile industry,the imbalance of defect types and the high cost to manually design defect features gained the poor generalization,and the supervised model solves the problem of yarn-dyed fabric defect detection with difficulty.Therefore,an unsupervised reconstruction model is proposed based on the denoising U-shaped convolutional auto-encoder,and a residual analysis method ispresented to inspect yarn-dyed shirt piece defects.First,normal samples are collected for a specific fabric in the training phase.Second,an unsupervised reconstruction model is trained based on the denoising U-shaped deep convolutional auto-encoder,which is employed to reconstruct new test samples.Finally,calculating the residual map between the original image and correspondingly reconstructed image is used to inspect and locate areas of fabric defects.Experimental results show that the proposed method can inspect and locate many types of yarn-dyed fabric defects without any defective fabric samples.

    Two-way energy trading algorithms for the end-user in the smart grid
    LIU Didi,SUN Haotian,XIAO Jiawen,JIANG Frank,ZHENG Kunkun
    Journal of Xidian University. 2021, 48(3):  131-137.  doi:10.19665/j.issn1001-2400.2021.03.017
    Abstract ( 15 )   HTML ( 1 )   PDF (1312KB) ( 14 )   Save
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    In this paper,the problem of two-way energy trading for the end-user in a smart grid is studied,where the end-user is equipped with renewable energy harvesting (EH) devices and the energy storage battery.The goal is to maximize the end-user’s benefits based on the randomness of EH,changes in electricity price,and battery storage capabilities.A real-time algorithm based on the Lyapunov optimization is proposed with low complexity,and it does not require the statistical characteristics of the user energy demand,changes in electricity price,and the arrival of renewable sources.Theoretical analysis shows that the proposed algorithm can make the optimization target infinitely close to optimum.Finally,simulation results show the validity of the proposed algorithm,and the impact of the battery capacity on the end-user’s revenue is analyzed.

    Cyberspace Security
    K-anonymous location privacy protection scheme for the mobile terminal
    SONG Cheng,JIN Tong,NI Shuiping,HE Junyi,DU Shouheng
    Journal of Xidian University. 2021, 48(3):  138-145.  doi:10.19665/j.issn1001-2400.2021.03.018
    Abstract ( 24 )   HTML ( 5 )   PDF (1741KB) ( 11 )   Save
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    In view of the security and effectiveness of the current location privacy protection scheme,we propose a K-anonymous location privacy protection scheme for the mobile terminal.In this scheme,2K valid dummy locations are obtained from the cloaking regions,and then K-1 better locations are selected from them according to the position entropy,so as to achieve a better anonymous effect.The secure and efficient oblivious transfer protocol is adopted,which avoids the dependence on the trusted anonymous center,improves the scheme's efficiency,and realizes the requirement of querying multiple interest points at one time.Security analysis shows that the scheme not only satisfies the unforgeability,but also resists the playback attack.Meanwhile,simulation indicates that the scheme can improve the execution efficiency,enhance the level of privacy protection,and reduce the communication overhead.

    Constructing formal verification models for hardware Trojans
    SHEN Lixiang,MU Dejun,CAO Guo,XIE Guangqian,SHU Fangyong
    Journal of Xidian University. 2021, 48(3):  146-153.  doi:10.19665/j.issn1001-2400.2021.03.019
    Abstract ( 13 )   HTML ( 0 )   PDF (1122KB) ( 8 )   Save
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    The effectiveness of hardware security verification is affected by the way of constructing formal verification models.To solve this problem,this paper proposes a method which can automatically construct formal verification models for hardware Trojans detection.First,the method traverses the control flow graphs of the register transfer level design to extract the path conditions of assignment statements and the corresponding expressions.The constraint relations of the Kripke’ state transition are generated based on the path conditions and the expressions.Second,the constraint relations of the Verilog grammar are transformed to the grammar of the model checker and generate the formal verification models.Finally,a model checker verifies the models and detects the hardware Trojans when a predefined specification is verified as false.In experiments,the hardware Trojans in the Trust-HUB benchmarks are detected,which shows that the models constructed by our method can effectively detect hardware Trojans in register transfer level design.

    Optimization and implementation of the SM4 on FPGA
    HE Shiyang,LI Hui,LI Fenghua
    Journal of Xidian University. 2021, 48(3):  155-162.  doi:10.19665/j.issn1001-2400.2021.03.020
    Abstract ( 27 )   HTML ( 3 )   PDF (849KB) ( 13 )   Save
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    Data encryption is one of the important means to ensure information security.In data encryption,the SM4 algorithm is widely used by considering its advantages of strong security,high efficiency,and easy hardware implementation.Current researchesfocus on hardware-feature based implementation to improve the cost and performanceof the SM4 algorithm.Four sets of hardware architecture are proposed for the SM4 algorithm and implemented on XILINX KINTEX-7 FPGA.The circular architecture is optimized for resource saving,which consumes 193 SLICE,and has a throughput of 1.27 Gb/s;the pipeline architecture is based on the LUT,BRAM,BRAM+REGISTER method implementation.According to different application scenarios,three solutions can be optimized in terms of resource consumption such as lookup tables,registers,and block memory,with the throughput reaching 42.10 Gb/s.

    Analysis of physical layer security performance for satellite communication systems
    XIAO Yeqiu,ZHU Xinghui,ZHAO Shuangrui,REN Baoquan,SHEN Yulong
    Journal of Xidian University. 2021, 48(3):  163-169.  doi:10.19665/j.issn1001-2400.2021.03.021
    Abstract ( 11 )   HTML ( 1 )   PDF (789KB) ( 0 )   Save
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    Due to the openness of the wireless medium and construction of the space-ground integrated information network,satellite communication (SATCOM) systems are facing severe security challenges.Physical layer security technology can guarantee secure satellite communication and effectively prevent the channel from being eavesdropped.By taking into account the space-ground integrated information network,a security performance analysis framework is developed for the SATCOM system with the existence of a ground eavesdropper and a satellite eavesdropper.Based on the Rayleigh fading channel and Shadowed-Rician fading channel,eavesdropping models of two eavesdroppers are provided,respectively.Regarding the non-collusion and collusion strategies employed at eavesdroppers,the exact expressions for average secrecy capacity (ASC) and secrecy outage probability (SOP) are derived under the two strategies,respectively.Simulation results show that a growing transmit power can improve ASC with SOP unchanged at the same time.Reducing the signal noise ratio at the satellite eavesdropper can provide a better secrecy performance for the SATCOM system than decreasing the signal noise ratio at the ground eavesdropper.

    Research on malicious traffic identification technology in encrypted traffic
    ZENG Yong,WU Zhengyuan,DONG Lihua,LIU Zhihong,MA Jianfeng,LI Zan
    Journal of Xidian University. 2021, 48(3):  170-187.  doi:10.19665/j.issn1001-2400.2021.03.022
    Abstract ( 18 )   HTML ( 1 )   PDF (3000KB) ( 3 )   Save
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    The encrypted transmission of network traffic is one of the development trends of the Internet.The identification of malicious traffic in encrypted traffic is an important way to maintain the security of cyberspace.One of the prior tasks of identifying malicious traffic is to classify encrypted traffic into the encrypted/unencrypted,different kinds of the application programs and encryption algorithms in order to improve the efficiency of identification.Then they are transformed into the image,matrix,n-gram or other forms which will be sent into the machine learning training model,so as to realize the binary classification and multi classification of benign malicious traffic.However,the machine learning based way relies seriously on the number and quality of samples,and can not effectively deal with the data after traffic shaping or confusion.Fortunately,cryptography based malicious traffic identification can search malicious keywords over encrypted traffic to avoid such problems,which must integrate searchable encryption technology,deep packet inspection and a provable security model to protect both data and rules.Finally,some unsolved problems of malicious traffic identification technology in encrypted traffic are presented.

    T-NTRU IoT dynamic access authentication technology
    LI Xinghua,CAI Jueping,LI Xiaolong,WANG Feng,YAN Zhenhua
    Journal of Xidian University. 2021, 48(3):  188-196.  doi:10.19665/j.issn1001-2400.2021.03.023
    Abstract ( 12 )   HTML ( 1 )   PDF (1042KB) ( 2 )   Save
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    The secure access authentication of the Internet of the things terminal is the key technology to ensure the large-scale construction of the power Internet of things.The elliptic curve cryptography (ECC) algorithm is usually used in the transmission authentication scheme,and it requires a large amount of calculation.Furthermore it is proved that its security against quantum attacks is poor.The number theory research unit (NTRU) algorithm can resist quantum attacks,and its computational speed is faster than that of the ECC.This paper proposes a dynamic T-NTRU secure access authentication algorithm based on time information in the Internet of things.It uses the dynamic changing time series as the secret key of the hash function to solve the internal attack security problem caused by the fixed hash function.Experiments are carried out on the computer and single chip microcomputer,respectively.Experimental results show that compared with the traditional ECC calculation,the T-NTRU algorithm proposed in this paper reduces about 97% of the calculational amount,which is equivalent to the typical NTRU algorithm,and is suitable for the need of the resource constrained power IOT network application.

    Path planning method for cable harness considering complex constraints
    YANG Xu,ZHOU Dejian,SONG Wei,CHEN Xiaoyong
    Journal of Xidian University. 2021, 48(3):  197-204.  doi:10.19665/j.issn1001-2400.2021.03.024
    Abstract ( 19 )   HTML ( 1 )   PDF (1319KB) ( 8 )   Save
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    Aiming at the problem that the existing path planning method cannot be used in the path planning of multiple electric aircraft airborne equipment cable harness which considers complex engineering rules constraints,a route planning method considering complex constraints is studied and proposed based on the Quasi-Physical and Quasi-Human algorithm and improved A* algorithm.First,the calculation method for the total wiring cost considering the path length cost and the material cost,process cost and weight cost of the bending groove is proposed,with the evaluation function of the traditional A* algorithm improved.Then,the equivalent radius of the cable harness is calculated based on the Quasi-Physical and Quasi-Human algorithm,and the search space automatic processing algorithm and the corner node reasonableness judgment algorithm considering the constraints of engineering rules are proposed.Finally,an example of cable harness laying in an airborne equipment shows that the routing path obtained by using the improved A* algorithm can not only meet the complex constraints,but also reduce the total wiring cost by 5.1% compared with the existing algorithm.