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Planning,monitoring and replanning techniques for handling abnormity in HTN-based planning and execution

Journal of Systems engineering and Electronics 2024年第5期35卷 1264-1275页

作者： KANG Kai CHENG Kai SHAO Tianhao ZHANG Hongjun ZHANG Ke Command and Control Engineering College Army Engineering University of PLANanjing 210000China The 63rd Research Institute of National University of Defense Technology Nanjing 210000China

A framework that integrates planning,monitoring and replanning techniques is *** can devise the best solution based on the current state according to specific objectives and properly deal with the influence of abnormity on the plan *** framework consists of three parts:the hierarchical task network(HTN)planner based on Monte Carlo tree search(MCTS),hybrid plan monitoring based on forward and backward and norm-based replanning method *** HTN planner based on MCTS selects the optimal method for HTN compound task through *** on specific objectives,it can identify the best solution to the current *** hybrid plan monitoring has the capability to detect the influence of abnormity on the effect of an executed action and the premise of an unexecuted action,thus trigger the *** norm-based replanning selection method can measure the difference between the expected state and the actual state,and then select the best replanning *** experimental results reveal that our method can effectively deal with the influence of abnormity on the implementation of the plan and achieve the target task in an optimal way.

关键词： hierarchical task network Monte carlo tree search(MCTS) planning execution abnormity

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Network Protocol Recognition Based on Convolutional Neural Network

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China Communications 2020年第4期17卷 125-139页

作者： Wenbo Feng Zheng Hong Lifa Wu Menglin Fu Yihao Li Peihong Lin Institute of Command and Control Engineering Army Engineering University of PLANanjing210007China School of Computer Science and Technology Nanjing University of Posts and TelecommunicationsNanjing210023China

How to correctly acquire the appropriate features is a primary problem in network protocol recognition *** to avoid the trouble of artificially extracting features in traditional methods and improve recognition accuracy,a network protocol recognition method based on Convolutional Neural Network(CNN)is *** method utilizes deep learning technique,and it processes network flows ***,normalization is performed on the intercepted network flows and they are mapped into two-dimensional matrix which will be used as the input of ***,an improved classification model named Ptr CNN is built,which can automatically extract the appropriate features of network ***,the classification model is trained to recognize the network *** proposed approach is compared with several machine learning *** results show that the tailored CNN can not only improve protocol recognition accuracy but also ensure the fast convergence of classification model and reduce the classification time.

关键词： convolutional neural network protocol recognition network flow classification model

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Exploration and practice of the experiment teaching of web application security course 10

Exploration and practice of the experiment teaching of web a...

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10th International Conference on Information Technology in Medicine and Education, ITME 2019

作者： Chen, Ping Zhao, Min Wang, Jinshuang Yu, Han Command and Control Engineering Institute Army Engineering University of PLA Nanjing210007 China

ISBN: (纸本)9781728139173

In the experiment teaching of Web application security course, we introduce a teaching assistant system -DVWA, which is vulnerable to the most common types of web application. We design the experiment cases for each Web application vulnerability from two aspects of attack and defense based on DVWA, and apply the multi-round progressive attack-defense confrontation mode to organize the experiment teaching. Teaching practice shows that the method increases the interest of students' learning and improves their ability of Web attack-defense. © 2019 IEEE.

关键词： Teaching

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Camouflage people detection via strong semantic dilation network 19

Camouflage people detection via strong semantic dilation net...

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2019 ACM Turing Celebration Conference - China, ACM TURC 2019

作者： Fang, Zheng Cao, Tieyong Zhang, Xiongwei Zheng, Changyan Deng, Xiaotong Institute of Command and Control Engineering PLA Army Engineering University Nanjing Jiangsu China

ISBN: (纸本)9781450371582

Camouflage greatly reduces the probability of the target being discovered, detecting the camouflage object from background is very hard for former object detection methods because they are designed to detect the object in an ideal environment. It brings difficulties to subsequent applications such as battlefield information acquisition, precision guided weapons and object tracking. To deal with the problem, in this paper, a new complete camouflage people detection dataset is firstly constructed, then we propose strong semantic dilation network (SSDN), which is specially designed to detect camouflage people in an end-to-end architecture. SSDN makes full use of semantic information in CNN and dilated convolutions are also added to enlarge the receptive field to find camouflage people. Experiments demonstrate that SSDN perform well in camouflage people detection dataset compared with other object detection methods. © 2019 Copyright held by the owner/author(s).

关键词： Semantics

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Dual Attribute Adversarial Camouflage toward camouflaged object detection

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Defence Technology（防务技术） 2023年第4期22卷 166-175页

作者： Yang Wang Zheng Fang Yun-fei Zheng Zhen Yang Wen Tong Tie-yong Cao Institute of Command and Control Engineering Peoples Liberation Army Engineering UniversityNanjing210007China The Army Artillery and Defense Academy of PLA Nanjing210007China PLA University of Foreign Language Luoyang471000China

The object detectors can precisely detect the camouflaged object beyond human *** investigations reveal that the CNNs-based(Convolution Neural Networks)detectors are vulnerable to adversarial *** works can fool detectors by crafting the adversarial camouflage attached to the object,leading to wrong *** is hard for military operations to utilize the existing adversarial camouflage due to its conspicuous *** by this,this paper proposes the Dual Attribute Adversarial Camouflage(DAAC)for evading the detection by both detectors and *** DAAC includes two steps:(1)Extracting features from a specific type of scene to generate individual soldier digital camouflage;(2)Attaching the adversarial patch with scene features constraint to the individual soldier digital camouflage to generate the adversarial attribute of *** visual effects of the individual soldier digital camouflage and the adversarial patch will be improved after integrating with the scene *** results show that objects camouflaged by DAAC are well integrated with background and achieve visual concealment while remaining effective in fooling object detectors,thus evading the detections by both detectors and humans in the digital *** work can serve as the reference for crafting the adversarial camouflage in the physical world.

关键词： Adversarial camouflage Digital camouflage generation Visual concealment Object detection Adversarial patch

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AndrOpGAN: An Opcode GAN for Android Malware Obfuscations 1

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3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020

作者： Zhang, Xuetao Wang, Jinshuang Sun, Meng Feng, Yao Institute of Command Control Engineering Army Engineering University of PLA NanjingJiangsu210007 China

ISBN: (数字)9783030622237

ISBN: (纸本)9783030622220

With the rapid development of Android platform, the number of Android malwares is growing rapidly. Due to the limitations of traditional static and runtime Android malware analysis methods, machine learning based approaches are widely adopted recently. Whereas, evading methods are also emerging, e.g. data set pollution, feature modification. Current feature modifications are mainly based on high-level features such as API calls or sensitive permissions. Our contribution is to show it is also feasible to deceive the detectors by modifying underlying features. Through this confusion, detector deceiving can be achieved. An Android malware opcode distribution feature modification system AndrOpGAN was proposed. To adjust the opcode distribution of malware, Deep Convolution Generative Adversarial Networks (DCGAN) was proposed to generate opcodes distribution features, and opcodes would be inserted through an Opcode Frequency Optimal Adjustment algorithm (OFOA). OFOA module can keep the APK running normally after insertion with a low modification cost. Test results against four detectors show that more than 99% APKs processed by AndrOpGAN could bypass detections successfully. Test results against VirusTotal shows that, the number of successful detection engines decreased 20%–44%. AndrOpGAN validates the feasibility of such attacks based on underlying feature modifications and provides a prototype system for researchers to improve detector’s performance. © 2020, Springer Nature Switzerland AG.

关键词： Android malware

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Distortion Evaluation of EMP Sensors Using Associated-Hermite Functions

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Computers, Materials & Continua 2023年第1期74卷 1093-1105页

作者： Rupo Ma Siping Gao College of Command and Control Engineering Army Engineering UniversityNanjing210007China Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjing210031China Department of Electrical and Computer Engineering National University of Singapore119260Singapore

Electromagnetic pulse(EMP)is a kind of transient electromagnetic phenomenon with short rise time of the leading edge and wide spectrum,which usually disrupts communications and damages electronic equipment and *** is challenging for an EMP sensor to measure a wideband electromagnetic pulse without distortion for the whole ***,analyzing the distortion of EMP measurement is crucial to evaluating the sensor distortion characteristics and correcting the measurement *** fidelity is usually employed to evaluate the distortion of an ***,this metric depends on specific signal waveforms,thus is unsuitable for evaluating and analyzing the distortion of EMP *** this paper,an associated-hermite-function based distortion analysis method including system transfer matrices and distortion rates is proposed,which is general and independent from individual *** system transfer matrix and distortion rate can be straightforwardly calculated by the signal orthogonal transformation coefficients using associated-hermite *** of a sensor *** is then visualized via the system transfer matrix,which is convenient in quantitative analysis of the *** of a current probe,a coaxial pulse voltage probe and a B-field sensor were performed,based on which the feasibility and effectiveness of the proposed distortion analysis method is successfully verified.

关键词： Electromagnetic pulse associated-hermite functions system transfer matrix distortion rate

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A Novel Subcharacter-enhanced Hybrid Network for Chinese Text Classification

A Novel Subcharacter-enhanced Hybrid Network for Chinese Tex...

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2023 International Conference on Frontiers of Robotics and Software engineering, FRSE 2023

作者： Chen, Lei Wang, Yang Zheng, Yun Fei Yang, Zhen Cao, Tieyong Institute of Command-and-Control Engineering The Army Engineering University of Pla The Army Artillery and Denfense Academy of Pla Nanjing China

ISBN: (纸本)9798350301113

Chinese text classification aimed to set pre-defined labels to given documents, and it is a very important research field. To deal with this problem, a novel subcharacter-enhanced hybrid network model called SHN is proposed. The proposed SHN can hierarchically extract the text semantic features. Firstly, our model decomposes Chinese words by Cangjie-code into subcharacters. Secondly, both word embeddings and subcharacters n-grams vectors are used as the input part of SHN, which is aimed to fuse both internal structure information and semantic information. Thirdly, SHN makes use of a bidirectional GRU module that contains word-level attention, to obtains semantic information from the sentences in document. Finally, a dynamic convolution neural network module is used by SHN, which aimed to extract high level features. Experiments carried out on two datasets show that SHN can obtain better results than conventional methods and is effective. © 2023 IEEE.

关键词： Classification (of information)

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Research on Indoor Positioning of Multi-source Information Fusion Based on Improved Particle Filter 22

Research on Indoor Positioning of Multi-source Information F...

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6th International Conference on Computer Science and Application engineering, CSAE 2022

作者： Zhou, Chulin Chen, Shiyou Chen, Jingdong Department of Command and Control Systems Wuhan Institute of Digital Engineering Wuhan China Department of Information and Electronics Wuhan Institute of Digital Engineering Wuhan China

ISBN: (纸本)9781450396004

In the information age, people are increasingly demanding location-based services. The traditional indoor positioning based on a single signal source has low accuracy and poor stability. On the one hand, the occlusion and echo interference in the indoor environment are serious, especially in the case of crowded environment and non-line-of-sight propagation, the UWB (Ultra Wide band) positioning accuracy will be greatly reduced. On the other hand, IMU (inertial Measurement Unit) can provide an accurate inertial navigation solution in a short time but its positioning error increases fast with time due to the cumulative error of accelerometer measurement. Therefore, in the process of pedestrian walking, we use the key information obtained by PDR (Pedestrian Dead Reckoning) to establish and update the real-time motion model of the target and calculate the prior information in the process of fusion filtering. Then, the initial position of the target is obtained through the UWB positioning solution, and the PDR positioning trajectory is corrected as the observation information in the fusion filtering process. Finally, the improved differential particle filter algorithm is used to fuse the above UWB positioning results and PDR positioning results, and make up for the advantages and disadvantages of the two positioning to improve the accuracy and stability of fusion positioning. © 2022 Association for Computing Machinery.

关键词： Location based services

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Cross-modal entity resolution based on co-attentional generative adversarial network 19

Cross-modal entity resolution based on co-attentional genera...

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4th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2019

作者： Lv, Guojun Cao, Jianjun Chang, Chen Zheng, Qibin Institute of Command and Control Engineering Army Engineering University Nanjing China Sixty-third Research Institute National University of Defense Technology Nanjing China

ISBN: (纸本)9781450371711

Cross-modal entity resolution aims to find semantically similar items from objects of different modalities(e.g. image and text). The core way to solve the problem is to construct a shared space where multi-modal examples can be represented uniformly. In this paper, we propose a novel Co-Attentional Generative Adversarial Network(CAGAN) method for solving cross-modal entity resolution, which seeks an effective space based on co-attention mechanism and adversarial learning. The generative adversarial network that we design contains two parts, Generator and Discriminator, the generator aims to generate a shared space through intra-modal loss and inter-modal loss, while discriminator is a classifier which tries to discriminate the modalities based on the generated representation. In order to eliminate the imbalance of information between modalities, generate more consistent representation and accelerate the convergence speed of the network, co-attention mechanism is introduced into the network. Experimental results performed on two cross-modal datasets demonstrated the outstanding performance of the proposed method for cross-modal entity resolution. © 2019 Association for Computing Machinery.

关键词： Generative adversarial networks

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