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8th International Conference on Dependable Systems and Their Applications, DSA 2021

作者： Zhu, Jing Huang, Song Shi, Yaqing Wu, Kaishun Wang, Yanqiu Command and Control Engineering College Army Engineering University of PLA Training Management Department Navy Command College Nanjing China Baopo Technology Co. LTD Nanjing China

ISBN: (纸本)9781665443913

At present, the method of random forest classification is one of the most common classifier. However, when there are many attributes in the acquired data set and the weights of attributes are not the same or close to each other, the direct use of this method for data training will not only ignore the correlation between attributes, but also make it difficult to reflect the advantages of different attribute weights, resulting in a negative impact on the accuracy of the final results. In addition, if there are many attributes in the data set, in order to meet the normalization of attribute weights, the values of the weights will inevitably be small, which will also lead to the loss of information, resulting in a negative impact on the final training result of the random forest. In order to solve these problems, this paper proposes a random forest method based on fuzzy comprehensive evaluation, which can not only considers the correlation between attributes, but also preserves the information in the original data set to the greatest extent, and at the same time significantly improves the accuracy of the results of random forest training. © 2021 IEEE.

关键词： Decision trees

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Construction of hospital big data center for intelligent medical care 7

Construction of hospital big data center for intelligent med...

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2022 7th International Conference on Information Science, Computer Technology and Transportation, ISCTT 2022

作者： Wang, Jun Wang, Xiu-Lai Yu, Ai-Rong Deng, Bo Nanjing Vocational College of Information Technology Nanjing210007 China General Hospital of Eastern Theater Command PLA Nanjing210002 China The Army Engineering University of PLA Nanjing210007 China

ISBN: (纸本)9783800760565

New technologies of Internet of Things and artificial intelligence are rapidly developing in the field of medical big health, and they are widely used in many fields such as text analysis of medical records, image recognition, and analysis of medical procedures. The construction of hospital big data center can give full play to the potential value of medical big data and realize the effective interaction among patients, medical care and treatment institutions through big data mining. By analyzing the classification, composition, characteristics and application scenarios of medical big data, adapting different storage and computing schemes and building hospital big data center according to the scheme, supporting the unified storage management of data resources, realizing the classification application, rapid retrieval, sharing and exchange and intelligent computing of data in different medical fields, maximizing the utilization of medical service resources, taking the construction of the eastern war zone general hospital big data as a typical case. It also promotes the scientific, professional, digital and refinement of hospital operation and management. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Big data

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A Software Security Testing Model for Autonomous Systems

A Software Security Testing Model for Autonomous Systems

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International Conference on Dependable Systems and Their Applications (DSA)

作者： Jinchang Hu Kang Sun Sen Yang Zhanwei Hui Song Huang College of Command and Control Engineering Army Engineering University of PLA Nanjing China AVIC The First Aircraft Institute Xi’an China Academy of Military Science China

The autonomous system is an extremely complex and multidisciplinary system, and the overall goal of this paper is to study safety testing methods for autonomous systems to enhance the robustness of autonomous systems. In order to realize an autonomous system with high reliability and robustness, this paper summarizes the security defect detection methods for autonomous systems from the two dimensions of static analysis and dynamic testing and expounds on the syntax-level, semantic, and system-level testing methods for autonomous systems.

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Compatibility Issue Detection for Android Apps Based on Path-Sensitive Semantic Analysis

Compatibility Issue Detection for Android Apps Based on Path...

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International Conference on Software engineering (ICSE)

作者： Sen Yang Sen Chen Lingling Fan Sihan Xu Zhanwei Hui Song Huang Command and Control Engineering College Army Engineering University of PLA China College of Intelligence and Computing Tianjin University China College of Cyber Science Nankai University China Academy of Military Science China

Android API-related compatibility issues have be-come a severe problem and significant challenge for app devel-opers due to the well-known Android fragmentation issues. To address this problem, many effective approaches such as app-based and API lifetime-based methods have been proposed to identify incompatible API usages. However, due to the various implementations of API usages and different API invoking paths, there is still a significant weakness of existing approaches, i.e., introducing a massive number of false positives (FP) and false negatives (FN). To this end, in this paper, we propose PSDroid, an automated compatibility detection approach for Android apps, which aims to reduce FPs and FNs by overcoming several technical bottlenecks. Firstly, we make substantial efforts to carry out a preliminary study to summarize a set of novel API usages with diverse checking implementations. Secondly, we construct a refined API lifetime database by leveraging a semantic resolving analysis on all existing Android SDK frameworks. Based on the above two key phases, we design and implement a novel path-sensitive semantic approach to effectively and automatically detect incompatibility issues. To demonstrate the performance, we compared with five existing approaches (i.e., FicFinder, ACRYL, CIDER, IctAPIFinder, and CID) and the results show that PSDroid outperforms existing tools. We also conducted an in-depth root cause analysis to comprehensively explain the ability of PSDroid in reducing FPs and FNs. Finally, 18/30 reported issues have been confirmed and further fixed by app developers.

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Privacy Protocol Analysis based on Android Application 8

Privacy Protocol Analysis based on Android Application

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8th International Conference on Dependable Systems and Their Applications, DSA 2021

作者： Yao, Yongming Wang, Yulin Jiang, Weiyi Wang, Ziyuan Huang, Song Peoples Liberation Army Engineering University Command and Control Engineering College Nanjing210007 China

ISBN: (纸本)9781665443913

With the number of mobile applications increasing year by year, users may consciously or unconsciously provide personal information to the application, some of which have illegal access to and disclosure of user's information behavior, user privacy security is greatly threatened. And because the privacy protocol is too lengthy, users don't care about the content of the privacy protocol, so they ignore the problematic behavior. To solve this problem, we analyze and study the privacy protocol based on the Android application, and propose a method to detect the sentences related to personal information operation in privacy protocol documents. To help users quickly read the personal information-related content in the privacy protocol document, we create Verb List, Negative Word List, and Noun List related to the user's information operation, and combine them to detect the sentences related to the user's personal information. © 2021 IEEE.

关键词： Android (operating system)

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Predicting Building Energy Consumption Based On A Hybrid Neural Network Model

SSRN

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SSRN 2024年

作者： Zhiyuan, Gao Xuewei, Zhang Changsheng, Wang Jianchun, Xing Zhongkai, Deng Tao, Chen National Defense Engineering College Army Engineering University of PLA Nanjing China National Defense Mobilization Office Command Information Assurance Center Zhangjiakou China Unit of PLA Hengyang421900 China

: The accurate prediction of building energy consumption provides technology and data support for the construction of intelligent building energy systems. Moreover, it is also a crucial means of responding to the national "Carbon Peaking and Carbon Neutrality Goals". Traditional methods can yield poor results because they fail to consider the nonlinear, nonstationary, and multi-seasonal characteristics of the building energy consumption data. To overcome these limitations, this paper proposes an asymmetric energy consumption prediction approach based on an encoder-decoder architecture. The proposed approach employs the CEEMDAN algorithm for data preprocessing to enhance the reliability of building energy consumption data. Subsequently, the convolutional gated recurrent unit (Conv-GRU) model is utilized to extract high-dimensional features and capture nonlinear relationships from the input energy consumption data. Finally, by employing the GRU-Attention algorithm to assign feature weights, this approach enhances the accuracy of building energy consumption prediction. Experimental evaluations conducted on real datasets demonstrate the superiority of the proposed approach over the existing classic methods. © 2024, The Authors. All rights reserved.

关键词： Carbon

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Evaluating Ethereum Reentrancy Detection Tools via Mutation Testing

Evaluating Ethereum Reentrancy Detection Tools via Mutation ...

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International Symposium on Software Reliability engineering (ISSRE)

作者： Kaitai Zhu Xingya Wang Zhenyu Chen Song Huang Junhua Wu College of Computer and Information Engineering (College of Artificial Intelligence) Nanjing Tech University China State Key Lab for Novel Software Technology Nanjing University China Command & Control Engineering College Army Engineering University of PLA China

Reentrancy vulnerabilities in Ethereum smart contracts have caused huge financial losses in recent years, promoting the development of reentrancy detection tools. Evaluation of reentrancy detection tools has emerged as an essential research focus consequently. However, despite numerous reentrancy detection evaluation approaches, there is no systematically classified set of reentrancy vulnerabilities in evaluation. This oversight may result in a limited understanding of the actual effectiveness of reentrancy detection tools. This paper employs mutation testing to generate classified sets of reentrancy vulnerabilities and evaluate reentrancy detection tools. To determine the types of reentrancy that can be mutated, we use symbolic execution to identify potential reentrancy paths in smart contracts. For each reentrancy type, we design one to three mutation operators. Then, we employ pattern matching to locate statements where mutation operators can be applied, thus generating sets of reentrancy vulnerabilities of different types. We implemented and compared our mutation testing tool with other tools. Results show that we generated at least 29% more classified reentrancy mutants. Furthermore, we evaluated two state-of-the-art reentrancy detection tools based on the classified reentrancy mutants. The findings provide directions for advancing reentrancy detection tools.

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Exploration of a Blended Teaching Mode for the Cultivation of High-Order Computing Ability 22

Exploration of a Blended Teaching Mode for the Cultivation o...

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Proceedings of the 14th International Conference on Education Technology and Computers

作者： Meijuan Wang Yibin Wang Hui Li College of Command Information Systems The Army Engineering University of PLA China College of Computer and Information Anqing Normal University China

ISBN: (纸本)9781450397766

The rapid development of information technology has a great influence on the future society. Future talents should have high-order thinking skills, such as adaptability, competence, and innovation, suitable for specific positions. Military universities undertake the important task of cultivating military talent. This work highlights the cultivation of students’ computing thinking ability in the course “The Fundamentals of Computer.” By carefully planning teaching time and content and using modern information technology and rich network teaching resources, the reform of online–offline blended teaching is practiced. The new teaching mode features high-order thinking skills training for students and realizes the transformation of the curriculum from knowledge output to high-order thinking ability orientation.

关键词： blended teaching computational thinking high-order thinking skills The fundamentals of computer

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Criticalfuzz: A Critical Neuron Coverage-Guided Fuzz Testing Framework for Deep Neural Networks

SSRN

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SSRN 2023年

作者： Bai, Tongtong Huang, Song Huang, Yifan Wang, Xingya Xia, Chunyan Qu, Yubin Yang, Zhen College of Command and Control Engineering Army Engineering University of PLA Jiangsu Nanjing China Nanyang Technological University Singapore College of Computer and Information Engineering College of Artificial Intelligence Nanjing Tech University China School of Information Engineering Jiangsu College of Engineering and Technology Nantong China

Neuron coverage-guided fuzz testing (NCFT) has become an effective measure for testing DNN, which iteratively generates new seeds with the guidance of neuron coverage to explore different logics of DNNs, and has found numerous defects. However, existing NCFT approaches ignore that the role of neurons is distinct for the final output of DNN. To ensure the quality of DNN and improve testing efficiency, NCFT should first cover neurons containing important logic of DNN. Therefore, in this paper, we propose the critical neurons that hold essential logic of DNN. In order to prioritize the detection of potential defects of critical neurons, we propose a fuzz testing framework, named CriticalFuzz, which preferentially covers critical neurons. To accelerate the speed of CriticalFuzz covering critical neurons, we propose the importance-based seed selection strategy and power schedule method. The importance-based seed selection strategy selects the seeds that cover the neurons close to critical neurons. The power schedule method is first proposed in NCFT to assign more energy to the seeds that are more likely to cover critical neurons. To evaluate the significance of critical neurons and the performance of CriticalFuzz, we conducted experiments on popular DNNs and datasets. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Empirical Analysis of Vulnerabilities Life Cycle in Golang Ecosystem

Empirical Analysis of Vulnerabilities Life Cycle in Golang E...

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International Conference on Software engineering (ICSE)

作者： Jinchang Hu Lyuye Zhang Chengwei Liu Sen Yang Song Huang Yang Liu College of Command and Control Engineering Army Engineering University of PLA Nanjing China Continental-NTU Corporate Lab Nanyang Technological University Singapore Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Academy of Military Science Nanjing China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Open-source software (OSS) greatly facilitates program development for developers. However, the high number of vulnerabilities in open-source software is a major concern, including in Golang, a relatively new programming language. In contrast to other commonly used OSS package managers, Golang presents a distinctive feature whereby commits are prevalently used as dependency versions prior to their integration into official releases. This attribute can prove advantageous to users, as patch commits can be implemented in a timely manner before the releases. However, Golang employs a decentralized mechanism for managing dependencies, whereby dependencies are upheld and distributed in separate repositories. This approach can result in delays in the dissemination of patches and unresolved vulnerabilities. To tackle the aforementioned concern, a comprehensive investi-gation was undertaken to examine the life cycle of vulnerability in Golang, commencing from its introduction and culminating with its rectification. To this end, a framework was established by gathering data from diverse sources and systematically amalgamating them with an algorithm to compute the lags in vulnerability patching. It turned out that 66.10% of modules in the Golang ecosystem were affected by vulnerabilities. Within the vulnerability life cycle, we found two kinds of lag impeding the propagation of vulnerability fixing. By analyzing reasons behind non-lagged and lagged vul-nerabilities, timely releasing and indexing patch versions could significantly enhance ecosystem security.

关键词： Computer languages Ecosystems Delays Security Stakeholders Open source software Indexing

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