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Behaviour-diverse automatic penetration testing:a coverage-based deep reinforcement learning approach

Frontiers of computer Science 2025年第3期19卷 15-24页

作者： Yizhou YANG Longde CHEN Sha LIU Lanning WANG Haohuan FU Xin LIU Zuoning CHEN Zhongguancun Laboratory Beijing 100081China Zhejiang Lab Hangzhou 311121China National Research Centre of Parallel Computer Engineering and Technology Wuxi 214000China Faculty of Geographical Science Beijing Normal UniversityBeijing 100875China Department of Earth System Science Tsinghua UniversityBeijing 100084China

Reinforcement Learning(RL)is gaining importance in automating penetration testing as it reduces human effort and increases ***,given the rapidly expanding scale of modern network infrastructure,the limited testing scale and monotonous strategies of existing RLbased automated penetration testing methods make them less effective in practical *** this paper,we present CLAP(Coverage-Based Reinforcement Learning to Automate Penetration Testing),an RL penetration testing agent that provides comprehensive network security assessments with diverse adversary testing behaviours on a massive *** employs a novel neural network,namely the coverage mechanism,to address the enormous and growing action spaces in large *** also utilizes a Chebyshev decomposition critic to identify various adversary strategies and strike a balance between *** results across various scenarios demonstrate that CLAP outperforms state-of-the-art methods,by further reducing attack operations by nearly 35%.CLAP also provides enhanced training efficiency and stability and can effectively perform pen-testing over large-scale networks with up to 500 ***,the proposed agent is also able to discover pareto-dominant strategies that are both diverse and effective in achieving multiple objectives.

关键词： network security penetration testing reinforcement learning artificial intelligence

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Enhanced Privacy-Preserving WiFi Fingerprint Localization from CL Encryption

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Chinese Journal of Electronics 2024年第6期33卷 1435-1446页

作者： Zhiwei WANG Qiuchi ZHU Zhenqi ZHANG School of Computer Nanjing University of Posts and Telecommunications State Key Laboratory of Public Big Data Guizhou University Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu University of Information Engineering Key Laboratory of Computational Science and Application of Hainan Province Hainan Normal University

The WiFi fingerprint-based localization method is considered one of the most popular techniques for indoor localization. In INFOCOM'14, Li et al. proposed a wireless fidelity(WiFi) fingerprint localization system based on Paillier encryption, which is claimed to protect both client C 's location privacy and service provider S's database privacy. However, Yang et al. presented a practical data privacy attack in INFOCOM'18, which allows a polynomial time attacker to obtain S's database. We propose a novel WiFi fingerprint localization system based on CastagnosLaguillaumie(CL) encryption, which has a trustless setup and is efficient due to the excellent properties of CL encryption. To prevent Yang et al.'s attack, the system requires that S selects only the locations from its database that can receive the nonzero signals from all the available access points in C 's nonzero fingerprint in order to determine C's location. Security analysis shows that our scheme is secure under Li et al.'s threat model. Furthermore, to enhance the security level of privacy-preserving WiFi fingerprint localization scheme based on CL encryption, we propose a secure and efficient zero-knowledge proof protocol for the discrete logarithm relations in C's encrypted localization queries.

关键词： Location awareness Threat modeling Data privacy Protocols Databases Fingerprint recognition Trustless services Encryption Servers Wireless fidelity

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Smart Camping Management Asset using Frequent Pattern-Growth Algorithm 11

Smart Camping Management Asset using Frequent Pattern-Growth...

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11th International Conference on Information and Communication Technology, ICoICT 2023

作者： Hussain, Hussain Pahlevi, Rizka Reza Wardana, Aulia Arif Mugitama, Satria Akbar Telkom University School of Computing Bandung Indonesia Wroclaw University of Science and Technology Computer Science and System Engineering Poland

ISBN: (纸本)9798350321982

Managing camping tools is crucial to ensure the safety of campers and extend the life of the tools. Frequent Pattern-Growth (FP-Growth) Association Rule Algorithms have emerged as a powerful tool for managing assets effectively. This algorithm is used to identify frequent patterns in large datasets, making it an efficient method for discovering association rules to help organizations make informed decisions about their assets. In the context of asset management, FP-Growth Association Rule Algorithms can be used to identify patterns in asset usage, maintenance, and replacement. By analyzing historical data, the algorithm can identify which assets are most frequently used, which require the most maintenance, and which are most likely to fail. This paper proposes managing the assets of camping tools using FP-growth and radio frequency identification (RFID) cards for asset tags. The proposed management asset tools are built on an Android operating system smartphone. This research using 30%, 60%, and 80% minimum support. The experiment results show that the minimum support affects the time needed to build an association. A small minimum support requires a longer time, while a large minimum support requires a faster time. The confidence value is used to determine the relevance of the FP-Growth output results. The results of the tests show that the difference in minimum support affects the speed of building associations, and the confidence value is proportional to the minimum support used. This research can be used for other case studies, using equipment data other than equipment for outdoor activities. © 2023 IEEE.

关键词： Association rules

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An Improved Semi-Supervised Learning-Based U-Net for Underwater Image Enhancement 5

An Improved Semi-Supervised Learning-Based U-Net for Underwa...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Han, Jiqun Liu, Xinyu Tan, Dongxu Zhu, Jiehui School of Computer Science and Engineering Northeastern University Shenyang China Guangdong Institute of Intelligent Unmanned System Technology Research and Development Department Guangzhou China

ISBN: (纸本)9798350385557

The attenuation and scattering of light in underwater environments often lead to degradation issues such as image blurring, color cast, and low contrast. Due to the lack of high-quality paired datasets, the performance of deep learning-based methods is limited. To address these challenges, we propose an improved U-Net architecture based on semi-supervised learning for underwater image enhancement. First, a multi-scale input-level fusion module is proposed to allow our network to fully exploit global spatial information from input images, natural light fields, and depth maps. Second, a multi-branch hybrid convolutional attention residual block is designed to improve the network's feature extraction capability and focus on important spatial and channel information in underwater images. Finally, a semi-supervised learning approach is used to fully exploit unpaired datasets, improving the generalization ability of the network model. Through qualitative and quantitative analysis, the proposed network achieves satisfactory image enhancement results in various underwater scenarios. © 2024 IEEE.

关键词： Image enhancement

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Enhanced Flexible Ramping Product Formulation for Alleviating Capacity Shortage in Look-ahead Commitment

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Journal of Modern Power systems and Clean Energy 2022年第4期10卷 850-860页

作者： Hyeongon Park Bing Huang Ross Baldick Department of Safety Engineering Pukyong National UniversityBusan 48513South Korea Midcontinent Independent System Operator CarmelIN 46032USA Department of Electrical&Computer Engineering at The University of Texas at Austin AustinUSA

The roll-out of a flexible ramping product provides independent system operators(ISOs)with the ability to address the issues of ramping capacity *** procure flexible ramping capability by committing more generating units or reserving a certain amount of headrooms of committed *** this paper,we raise the concern of the possibility that the procured flexible ramping capability cannot be deployed in realtime operations due to the unit shut-down in a look-ahead commitment(LAC)*** a solution to the issues of ramping capacity shortage,we provide a modified ramping product formulation designed to improve the reliability and reduce the expected operating *** trajectories of start-up and shutdown processes are also considered in determining the ramping capability.A new optimization problem is formulated using mixed integer linear programming(MILP)to be readily applied to the practical power system *** performance of this proposed method is verified through simulations using a small-scale system and IEEE 118-bus *** simulation results demonstrate that the proposed method can improve the generation scheduling by alleviating the ramping capacity shortages.

关键词： Flexible ramping product look-ahead commitment(LAC) mixed integer linear programming(MILP) reliability

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Deep Learning-based Mortality Prediction of COVID-19 Patients with Cardiovascular Disease 9

Deep Learning-based Mortality Prediction of COVID-19 Patient...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Zheng, Huilin Liang, Yingyi Zhang, Daming Shi, Kai Dong, Luxi Xie, Xiaolan Guilin University of Technology Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology College of Computer Science and Engineering Guilin China

ISBN: (纸本)9798350391367

Coronavirus disease 2019 (COVID-19) is one of the most dangerous diseases in recorded human history due to its high contagiousness and rapid dissemination. Moreover, people who already have medical issues like cardiovascular disease (CVD) have a higher risk of getting seriously ill. Therefore, this study proposes a deep learning-based prediction model for the mortality of COVID-19 patients with CVD. The research substances are introduced as follows. First, we obtain the original dataset from the Kaggle website provided by the Mexican government, and extract our experimental data. After that, we develop a deep learning-based prediction model for the mortality of COVID-19 patients with CVD while applying a 5-fold cross-validation approach for the model training and validation. Finally, we evaluate the performance of the proposed model and also compare it with many common machine learning models. In our experimental results, the proposed model achieved higher performance than all of the other models. It facilitates decision-making and provides an accurate mortality prediction for COVID-19 patients with CVD. © 2024 IEEE.

关键词： COVID-19

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Learning to control the complex light propagation through few-mode fiber without a reference wave

Learning to control the complex light propagation through fe...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Rothe, Stefan Krause, David Zhang, Qian Pohle, Dennis Koukourakis, Nektarios Czarske, Jürgen W. Technische Universitat Dresden Faculty of Electrical and Computer Engineering Laboratory of Measurement and Sensor System Technique Dresden01062 Germany

We show reference-less transmission matrix measurements using a neural network operating with up to 23 modes in real-time. We use synthetic training data enabling optical predistortion through an SLM for light control... 详细信息

ISBN: (纸本)9781957171258

关键词： Light propagation

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Recommending Public Transit Route: Ant-Colony Optimization or Dijkstra?

Recommending Public Transit Route: Ant-Colony Optimization o...

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2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Jabbar, Abdul Pinandito, Aryo Universitas Brawijaya Faculty of Computer Science Informatics Engineering Department Malang Indonesia Universitas Brawijaya Faculty of Computer Science Information System Department Malang Indonesia

ISBN: (纸本)9798350369359

Advances in algorithmic studies have been made recently, especially in the area of finding the shortest path. Despite taking a greedy approach, the Dijkstra algorithm is well recognized for its efficacy and has established itself as the industry standard for identifying the shortest path in a graph. Conversely, scientists have embraced the Ant-Colony Optimization (ACO) approach, which draws inspiration from the behavior of ants. This study examines the two methods and determines if, given equivalent resources, ACO can generate Dijkstra-like recommendations. Research data is subjected to both algorithms, and the outcomes are presented visually. In data measurement, Dijkstra is processed first to determine the best path, and then the ACO method is applied to produce findings that are similar to Dijkstra. In data measurement, Dijkstra is processed first to determine the best path, and then the ACO method is applied to produce findings that are similar to Dijkstra. Memory consumption, computation time, initial memory allocation, and final memory allocation are evaluation parameters. ACO is more efficient than Dijkstra in terms of execution time and number of pathways traversed, according to the study's repeated evaluation methodology. This iterative assessment procedure is carried out to address the possibility of single measurements resulting from the JavaScript Garbage Collector. The study's findings imply that a larger memory may be necessary for ACO to deliver the desired outcomes. These results could also be impacted by each algorithm's distinct qualities. © 2023 IEEE.

关键词： Ant colony optimization

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MPI Allgather Utilizing CXL Shared Memory Pool in Multi-Node Computing systems

MPI Allgather Utilizing CXL Shared Memory Pool in Multi-Node...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Ahn, Hooyoung Kim, Seonyoung Park, Yoomi Han, Woojong Ahn, Shinyoung Tran, Tu Ramesh, Bharath Subramoni, Hari Panda, Dhabaleswar K. Supercomputing System Research Section Electronics and Telecommunications Research Institute Korea Republic of The Ohio State University Department of Computer Science and Engineering United States

ISBN: (纸本)9798350362480

In Artificial Intelligence (AI) and high-performance computing (HPC), growing data and model sizes require distributed processing across multiple nodes due to single-node limitations, increasing inter-node communication. To address these challenges, we propose a novel MPI allgather method leveraging CXL technology, which supports composable architectures and dynamic resource allocation in data centers and HPC systems. Notably, CXL 3.1 facilitates cache coherence among nodes. The proposed allgather method uses the CXL shared memory pool as a communication buffer, outperforming existing algorithms for two reasons: First, CXL provides lower latency than Ethernet and IB, and second, by using the CXL shared memory pool as a shared communication buffer across multiple nodes, it significantly reduces the number of communications. To the best of our knowledge, this work is the first to explore combining MPI collective communication with CXL technology to optimize MPI allgather. Our proposed allgather method significantly reduces communication latency compared to traditional allgather methods by up to 42.14x, with a minimum improvement of 2.91x, as measured using the OSU Micro-Benchmark (OMB), a standard MPI benchmarking suite. © 2024 IEEE.

关键词： Resource allocation

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Decision Support system For The Coaching Staff in the Process of Preliminary Selection of Players 18

Decision Support System For The Coaching Staff in the Proces...

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18th IEEE International Conference on computer Science and Information Technologies, CSIT 2023

作者： Hovorushchenko, Tetiana Hnatchuk, Yaroslav Hnatchuk, Alina Fehyr, Oleksandr Computer Engineering and Information System Department Khmelnytskyi Ukraine Theory and Methods of Physical Education and Sports Department Khmelnytskyi Ukraine

ISBN: (纸本)9798350360462

Nowadays in Ukraine, there is a need to automate the routine work of coaches and sports managers in the preliminary selection of athletes. In the process of selecting athletes for competitions of various levels, the head coach and coaching staff need to process large amounts of information about the applicants. This is especially important at the stages of preliminary selection, when the number of players to be selected for preliminary training camps is quite large, and the amount of information about each individual athlete increases accordingly. Our research has shown that despite the rapid development and application of information technology in the sports industry, there is currently no intelligent sports-specific DSS in Ukraine that helps coaching staff and sports managers automate the routine and time-consuming process of preliminary selection of athletes. Taking into account the large amount and heterogeneity of data that must be processed for decision-making by the coaching staff during the preliminary selection of players, a decision support system for preliminary selection of players is proposed. The system is based on the method of supporting decision-making by the coaching staff in the process of preliminary selection of athletes. The result of the decision support system is a recommended list of 18 players who have been selected according to the specified criteria out of a total of 24 players. If the coaching staff is not satisfied with the recommended list, it is possible to set additional criteria and use the decision support method to obtain a more accurate result. The results can also be implemented in other team sports. © 2023 IEEE.

关键词： Decision support systems

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