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检索条件"任意字段=2023 International Conference on Computer Network Security and Software Engineering, CNSSE 2023"

共 6774 条记录，以下是31-40 订阅

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A survey of the accountability framework for jobs of batch computing services

A survey of the accountability framework for jobs of batch c...

引用

2023 international conference on computer network security and software engineering, cnsse 2023

作者： Zhang, Jiajia Liao, Fu Liang, Huang Wu, Zheng Li, Xiaojian School of Computer Science & Engineering Guangxi Normal University Guilin China

ISBN: (数字)9781510666528

ISBN: (纸本)9781510666511

Batch computing services have the characteristics of providing services required by users on demand. Furthermore, failure factors such as resource constraints and scheduling can lead to service violations. The accountability mechanism is essential to be established, which integrates analysis, network generation, Determination, and Tracing. There are functional modules that have done some research on the parts of the accountability mechanism. However, in order to integrate them into the accountability mechanism, the accountability framework is essential for batch computing services. Therefore, this article aims to present a survey of functional modules of accountability mechanism, presenting and comparing main features described in the literature and challenges of integrating the accountability framework. At the end of this work, we provide suggestions for future research directions. © 2023 SPIE.

关键词： Clouds Failure analysis Analytical research Factor analysis Interfaces Semantics network security Lithium Cloud computing Solids

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P2P Based on network Behavior Analysis Trust value calculation method

P2P Based on Network Behavior Analysis Trust value calculati...

引用

2023 international conference on computer network security and software engineering, cnsse 2023

作者： Zeng, Xingeng Yu, Xuejun Beijing University of Technology Beijing China

ISBN: (数字)9781510666528

ISBN: (纸本)9781510666511

With the rapid development of the Internet and technological innovation, the number and resources of computers accessed in P2P (Peer to Peer) peer-to-peer networks are increasing. Another important feature of P2P is to change the current Ethernet centric state of the Internet, return to "decentralization", and return power to users. Its openness, anonymity and autonomy increase the possibility of security attacks by malicious nodes. For example, malicious nodes providing false services will affect normal transactions between P2P network nodes. This has affected the development of P2P applications to a certain extent. In view of the existing P2P network node security problems, can not deal with the node collusion attacks, joint fraud and other issues, this paper proposes a new trust calculation method. This method draws on the basic elements of social psychology about establishing intimate relationship between people, considers the influence of node behavior on trust, and uses aggregated trust, current trust, feedback trust and cumulative abuse trust to calculate and update the trust value, so as to improve the dynamic adaptability and fault tolerance of peer-to-peer networks. © 2023 SPIE.

关键词： Peer to peer networks

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A Deep Reinforce Learning Based Intrusion Detection Method for Safeguarding Internet of Things

A Deep Reinforce Learning Based Intrusion Detection Method f...

引用

2023 international conference on computer network security and software engineering, cnsse 2023

作者： Zhang, Liang Lv, Hongjie School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9781510666528

ISBN: (纸本)9781510666511

With the increasing number of Internet of Things (IoT) access devices, security incidents in the IoT field have occurred frequently in recent years. Traditional intrusion detection techniques can no longer meet the current cyber threat discovery needs in the IoT environment. For this reason, we proposed a deep reinforcement learning (DRL)-based IoT intrusion detection system using an intrusion detection dataset produced by an IoT-based platform to enhance network security in the IoT environment in this study. The system applied a feature selection algorithm based on Pearson's correlation coefficient to extract the most effective feature set, applied a multilayer perceptron containing four hidden layers as the deep neural network structure shared by the value network and the policy network in this intrusion detection system, and constructs an intrusion detection system based on DRL proximal policy optimization algorithm. Based on the experimental results show that the proposed intrusion detection system obtains 99.96% accuracy in the face of different network attacks on IoT and outperforms current deep learning models based on LSTM, CNN, and DQN in terms of accuracy, precision, recall, and F1-measure. © 2023 SPIE.

关键词： network security

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Internet of things-oriented data security risk monitoring system

Internet of things-oriented data security risk monitoring sy...

引用

2023 international conference on computer network security and software engineering, cnsse 2023

作者： Mao, Rongjie State Grid Xinjiang Electric Power Limited Company and Altay Power Supply Company China

ISBN: (数字)9781510666528

ISBN: (纸本)9781510666511

In order to realize real-time dynamic monitoring, remote visualization in different places, and improve the efficiency of data collection, transmission and processing in the process of subway security risk monitoring, a data security risk monitoring system for Internet of Things is proposed. Firstly, this paper puts forward the application of Internet of Things technology in subway safety monitoring. Through the application of information perception, processing, security and other technologies, a real-time online perception and intelligent management system of underground environment is constructed, which provides a safe and controllable real-time monitoring function. Secondly, in each module of the monitoring system, this paper mainly makes discriminant analysis for the early warning module, and establishes the subway safety early warning model by using fuzzy comprehensive evaluation method, and compares it with other comprehensive evaluation methods such as BP neural network. The results show that the fuzzy comprehensive evaluation method has strong theory and application in subway safety early warning. The similarity between the evaluation results and the results of multi-index grey interval number correlation decision, artificial neural network and regression analysis is above 82%, and it is in line with the actual situation on the spot. It can reliably provide scientific and reasonable theoretical basis for subway safety early warning and formulating risk response measures, guide site construction, and provide reference for other similar projects. © 2023 SPIE.

关键词： Risk assessment

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CBAN: A DDoS detection method based on CNN, BiGRU, and attention mechanism

CBAN: A DDoS detection method based on CNN, BiGRU, and atten...

引用

2024 international conference on computer network security and software engineering, cnsse 2024

作者： Wang, Bing Yu, Yankun Zhao, Chunlan Jiang, Jingjing School of Computer Science and Software Engineering Southwest Petroleum University Sichuan Chengdu610500 China Key Laboratory of Energy Security and Low-carbon development Southwest Petroleum University Sichuan Chengdu610500 China School of Sciences Southwest Petroleum University Sichuan Chengdu610500 China Information Technology Department Jinchuan County Middle School Aba Tibetan and Qiang Autonomous Prefecture Sichuan624100 China

ISBN: (纸本)9781510680234

In order to address the obstacles posed by the growing security issues of the Internet of Things to the development of big data, this paper conducts in-depth research on the defense of the most harmful DDoS attack. This paper designs a deep learning model for DDoS detection - CBAN. The CBAN model integrates technologies such as 1D-CNN, BiGRU, and attention mechanism for structural design. This model can effectively extract spatial and temporal features of network traffic data for efficient detection of potential DDoS attacks. The CBAN model has shown excellent performance on the CIC-DDoS-2019 dataset. © 2024 SPIE.

关键词： Big data

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Deep learning based intrusion detection methods

Deep learning based intrusion detection methods

引用

2023 international conference on computer network security and software engineering, cnsse 2023

作者： Chen, Hong Chen, Lili School of Software Liaoning Technical University China

ISBN: (数字)9781510666528

ISBN: (纸本)9781510666511

With the increasing number and categories of cyber attacks in the era of big data, intrusion detection techniques are constantly updated and optimised. In order to address the shortcomings of traditional intrusion detection methods and single neural network models in intrusion detection, this paper proposes a deep learning-based intrusion detection method. By combining a one-dimensional convolutional neural network with a bidirectional gated recurrent unit, a new CNN-BiGRU network model is formed to fully extract intrusion detection data features and improve the accuracy of intrusion detection. Using the public dataset CIC-IDS2017, multiple sets of comparison experiments were conducted on the proposed method in this paper in the same environment. Firstly, the ablation experiments yielded that in terms of detection performance, the accuracy of this paper's method improved from 99.50% and 99.34% to 99.58% compared with CNN and BiGRU-based models respectively. In terms of running time, the detection time of this model is reduced from 1921s to 907s compared with that of BiGRU, demonstrating the good detection performance of this method. Finally, through comparison tests, it is verified that the intrusion detection method proposed in this paper has better detection performance compared with other methods. © 2023 SPIE.

关键词： Intrusion detection

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Based on QUBO models with quantum-inspired algorithms to enhance the CVQKD systems to ensure security of hacking

Based on QUBO models with quantum-inspired algorithms to enh...

引用

2024 international conference on computer network security and software engineering, cnsse 2024

作者： Zhu, Feiyue Qiu, Haifeng Wang, Ziyu School of Civil Engineering Central South university 410083 China

ISBN: (纸本)9781510680234

This paper presents the need for innovative solutions to optimize computational power networks and the application of quantum computing to tackle real-world *** research addresses this critical issue by developing a robust pre-training scheme that integrates Quantum Unweighted Quadratic Unconstrained Binary Optimization (QUBO) models with quantum-inspired algorithms. This approach aims to enhance the adversarial robustness of CVQKD systems, ensuring their security in the face of sophisticated hacking attempts. Our experimental results demonstrate that the proposed strategy effectively defends against adversarial attacks while maintaining the integrity of secret keys, showcasing the adaptability and efficiency of QUBO models in quantum communication scenarios. This work not only contributes to the broader application of QUBO models in quantum communication but also provides a robust pre-training scheme that can be generalized and transplanted to other machine learning-assisted systems, significantly improving their security in the face of adversarial attacks. © 2024 SPIE ·.

关键词： Quantum communication

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Research on malicious code detection and classification based on dynamic and static features

Research on malicious code detection and classification base...

引用

2024 international conference on computer network security and software engineering, cnsse 2024

作者： Shang, Yueyang Wang, Fuwei Zhang, Yunfei Li, Dong Tan, Wenbin School of Artificial Intelligence and Software LiaoNing Petrochemical University Fushun113000 China

ISBN: (纸本)9781510680234

Malicious code can reflect its malicious behaviour through dynamic API sequences and static PE header information, and deep learning algorithms have made progress in such malicious code detection. The article designs a 1D-CNN-BiGRU network model based on one-dimensional convolutional neural networks and bidirectional gated recurrent neural networks. The model takes API call sequences and PE header information as feature inputs, and undergoes convolutional computation and recurrent neural network learning training to further learn the features of malicious code. Through the analysis of experimental results, the correctness of the malicious code verification of this model is demonstrated. The detection accuracy of normal samples on dynamic API call sequences is over 97%, and the accuracy on static PE structures is 95.64%. It has good performance in malicious code detection. © 2024 SPIE.

关键词： Recurrent neural networks

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Attack Detection System Based on Provenance Graph Semantic Analysis

Attack Detection System Based on Provenance Graph Semantic A...

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2023 international conference on computer network security and software engineering, cnsse 2023

作者： Cui, Can Yang, Zhuorao Cui, Baojiang School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (数字)9781510666528

ISBN: (纸本)9781510666511

Advanced Persistent Threat (APT) is an advanced persistent network attacks on target information systems by attackers. Most existing APT detection methods are implemented by manually designing detection rules based on the analysis of APT attack strategies (e.g., ATT&CK network attack and defense knowledge base). Such methods are difficult to detect unknown APT attacks. In this paper, we propose an attack detection method based on provenance graph semantic analysis, which analyzes sequences from provenance graphs to detect attack events. Firstly, we construct a provenance graph with low complexity and low redundancy based on preserving its semantics. Secondly, we increase the number of attack sequences by mutation to alleviate the problem of data imbalance between normal and attack sequences. Finally, the Natural Language Processing (NLP) model is introduced to realize attack detection by analyzing the semantics of attack sequences. Through our experiments, we found that the method can reduce the scale of the provenance graph by more than 95%. It correctly identifies attack entities with an average of 100% precision and an average of 93.42% recall, as well as identifies attack events with an average of 100% precision and an average of 99.57% recall. The results show that the method proposed in this paper can effectively detect the attack behavior. © 2023 SPIE.

关键词： Semantics

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Research on WCET assessment method of airborne display software

Research on WCET assessment method of airborne display softw...

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2024 international conference on computer network security and software engineering, cnsse 2024

作者： Xu, Junfeng Shanghai Aircraft Design and Research Institute Shanghai201210 China

ISBN: (纸本)9781510680234

Airborne display software WCET assessment relies on actual scenario and traditional static analysis and dynamic test methods can not reflect actual scenario requirements. To achieve WCET value closing to actual scenario for resource saving purpose, firstly the typical architecture of airborne display unit is described;secondly, the worst scenario analysis principles for WCET are identified;thirdly, WCET assessment flow and steps are proposed respectively for non-A661 partition and A661 service partition;lastly, the WCET acceptance principles are given in both software level and system level. This research can help to support airborne display software resource assessment and allocation in the early stage of software design. © 2024 SPIE.

关键词： software design

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