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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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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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Private Blockchain-Based Platform for Cool Box Truck Based on LoRa Communication Protocol 1

Private Blockchain-Based Platform for Cool Box Truck Based o...

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1st IEEE International Symposium on Consumer Technology, ISCT 2024

作者： Prayudi, Agus Hakim, Oktafian Sultan Zainuddin, Muhammad Agus Sukaridhoto, Sritrusta Budiarti, Rizqi Putri Nourma Politeknik Elektronika Negeri Surabaya Department of Electrical Engineering Surabaya Indonesia Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia Universitas Nadhlatul Ulama Surabaya Department of Information System Surabaya Indonesia

ISBN: (纸本)9798350365191

The Internet of Things utilizes software, embedded system, sensor, and network connectivity to control and monitor business processes such as supply chains. One of the implementations of IoT is LoRa (Long Range) which enables data transactions in long-range without using radio frequency. Currently, a combination of IoT and blockchain is able to increase the security level of IoT applications. Implementation of IoT in the supply chain has been done in several research by implementing blockchain and LoRa. Approximately 6.43% of the Gross Domestic Product (GDP) is attributed to logistics, with cold and hot logistics constituting around 11%. Among these, cold chain logistics, a specialized form, accounts for 9.7% of tonnage. Cold chain logistics, characterized by comprehensive temperature control, is a sophisticated system closely intertwined with advanced technology. Despite its complexity, there are still technological drawbacks leading to significant material losses on an annual basis. In this research, to overcome that problem, we proposed Blockchain-Based Monitoring system on Cool Box Trucks using LoRa. The proposed system implemented LoRa for data communication and Blockchain as decentralized databases. We simulated the LoRa environment by using FLoRa framework to obtain the optimal parameters such as Transmission Power (TP), Packet Delivery Ratio (PDR), and Spread Factor (SF) that used in TTGO T-Beam V1. © 2024 IEEE.

关键词： Trucks

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Research on Image Captioning Based on Vision-language Pre-trained Models 9

Research on Image Captioning Based on Vision-language Pre-tr...

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9th International Conference on Big Data and Information Analytics, BigDIA 2023

作者： Zou, Shiwei Xie, Yuxiang Yan, Jie Wei, Yingmei Luan, Xidao National University of Defense Technology Department of System Engineering Hunan China National University of Defense Technology Key Laboratory of Big Data Department of System Engineering Changsha Hunan China Changsha University Department of Computer Science Hunan China

ISBN: (纸本)9798350330076

Image captioning is a vision-language task that targets at describing an image by generating a coherent sentence automatically. This technology allows computers to understand and describe images like humans, enabling further processing of images. It combines computer vision and natural language processing, which has broad applications. This paper investigates the use of vision-language pre-trained models for image captioning. In this article, an encoder-decoder hybrid model based on PVT and BERT is pre-trained on a large number of image-text pairs, possessing both understanding and generation capabilities. It consists of three components: 1) an image encoder and a text encoder to extract image and text features respectively;2) a multi-modal encoder to align the two modalities;and 3) a text decoder to generate text descriptions. This paper focuses on enhancing the image encoder of the model, which is crucial for image captioning models as it extracts high-resolution image features. Reducing training time and computational costs can also improve the applicability of pre-trained models. Improvements are made to the original model by modifying the ViT model architecture of the image encoding module. A feature pyramid structure and a spatial reduction attention mechanism is introduced to extract high-resolution image features while reducing computational complexity and memory usage, thereby enhancing the universality of the model. The model adopts a pure Transformer architecture, discarding the convolutional structure, and achieves excellent performance. © 2023 IEEE.

关键词： Image enhancement

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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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Deployment of Polar Codes for Mission-Critical Machine-Type Communication Over Wireless Networks

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computers, Materials & Continua 2022年第4期71卷 573-592页

作者： Najib Ahmed Mohammed Ali Mohammed Mansoor Rodina Binti Ahmad Saaidal Razalli Bin Azzuhri Department of Software Engineering Faculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala Lumpur Malaysia 2Department of Computer System&TechnologyFaculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala LumpurMalaysia

Mission critical Machine-type Communication(mcMTC),also referred to as Ultra-reliable Low Latency Communication(URLLC),has become a research *** is primarily characterized by communication that provides ultra-high reliability and very low latency to concurrently transmit short commands to a massive number of connected *** the reduction in physical(PHY)layer overhead and improvement in channel coding techniques are pivotal in reducing latency and improving reliability,the current wireless standards dedicated to support mcMTC rely heavily on adopting the bottom layers of general-purpose wireless standards and customizing only the upper *** mcMTC has a significant technical impact on the design of all layers of the communication protocol *** this paper,an innovative bottom-up approach has been proposed for mcMTC applications through PHY layer targeted at improving the transmission reliability by implementing ultra-reliable channel coding scheme in the PHY layer of IEEE 802.11a standard bearing in mind short packet transmission *** achieve this aim,we analyzed and compared the channel coding performance of convolutional codes(CCs),low-density parity-check(LDPC)codes,and polar codes in wireless network on the condition of short data packet *** Viterbi decoding algorithm(VA),logarithmic belief propagation(Log-BP)algorithm,and cyclic redundancy check(CRC)successive cancellation list(SCL)(CRC-SCL)decoding algorithm were adopted to CC,LDPC codes,and polar codes,***,a new PHY layer for mcMTC has been *** reliability of the proposed approach has been validated by simulation in terms of Bit error rate(BER)and packet error rate(PER)***-to-noise ratio(SNR).The simulation results demonstrate that the reliability of IEEE 802.11a standard has been significantly improved to be at PER=10−5 or even better with the implementation of polar *** results also show that the general-purpose wireless net

关键词： Machine-type communication mission-critical applications MTC URLLC M2M wireless networks polar codes

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Acute Lymphoblastic Leukemia Detection Using DenseNet Model from Microscopic Blood Smear Images

Acute Lymphoblastic Leukemia Detection Using DenseNet Model ...

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International Conference on Data Science and Network engineering, ICDSNE 2023

作者： Prasath, Navamani Jasthi, Monika Saikia, Rabul Loganathan, Muralidaran Devi, Salam Shuleenda Department of Electronics and Communication Engineering NIT Meghalaya Meghalaya Shillong793003 India Department of Computer Science and Information System Engineering St. Joseph University Dae Es Salaam Tanzania United Republic of

ISBN: (纸本)9789819967544

Acute lymphoblastic leukemia (ALL) is a cancerous condition that affects the bone marrow and blood. It is a fast developing illness that, if not identified and treated as soon as possible, could be fatal. ALL is often identified by looking at blood and bone marrow smears under a microscope. Leukemia can be detected and classified using detailed cytochemical tests. However, these procedures are expensive, time-consuming, and dependent on the knowledge and skills of the specialists involved. Using image processing techniques that examine microscopic blood smear images to search for the leukemic cells, leukemia can be detected. These methods are simple, quick, cost-effective, and unaffected by the judgments of experts. The suggested study describes a computer-aided diagnosis method that uses deep convolutional neural networks (CNNs) that have already been trained to compare leukemia images to normal images. The public dataset ALL-IDB 2 was used for the proposed research. The study uses the pre-trained model DenseNet-201 for performing the classification. With the DenseNet201 pre-trained networks employed in the study for the ALL_IDB2 dataset, a classification accuracy of 94.6% is achieved. In all of the classifications carried out, optimization strategies such as cross-validation, fine-tuning, and real-time augmentation are also compared. Also use Pre-trained series models like ResNet-50, VGG-19, Inceptionv3, MobileNet-v2, Xceptionv3, and VGG-16 for performing the comparison. The experimental result gives an improvement in accuracy (17.76, 10.6, and 13.2%) in comparison to the other approaches namely, residual neural network, customized combined CNN, and conVNet neural network, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： computer aided diagnosis

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Adaptive Clustering Algorithm for Big Health Data Classification 3

Adaptive Clustering Algorithm for Big Health Data Classifica...

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3rd IEEE Asian Conference on Innovation in Technology, ASIANCON 2023

作者： Bhargava, Neeraj Rathore, Pramod Singh Jha, Meenakshi Goswami, Ankur School of Engineering & System Sciences Mds University Ajmer India Manipal University Department of Computer and Communication Engineering Jaipur India Mds University Ajmer India Govt. Engineering College Department of Computer Applications Bikaner India

ISBN: (纸本)9798350302288

This paper presents an analysis of tobacco and alcohol consumption among different age groups, using data collected by the World Health Organization (WHO). The analysis quantifies alcohol and tobacco consumption in grams per day, aggregating the total amount consumed by each age group. Consumption of alcohol is considered not only through wine, but also through other mediums such as medicine, inhalers, etc. while tobacco is consumed through smoking, dry tobacco, etc. Our analysis also takes into account whether the cases of consumption were controlled or uncontrolled. The majority of cases analyzed in our study were found to be controlled. Finally, we present graphical representations of the analysis results, highlighting the dependencies between different variables. © 2023 IEEE.

关键词： Tobacco

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Vacancy Modulated Analog Resistive Switching Memory Device Based on Bilayer of Zn@ZnO/ZnO for Neuromorphic Computing 30

Vacancy Modulated Analog Resistive Switching Memory Device B...

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30th IEEE International Conference on Electronics, Circuits and systems, ICECS 2023

作者： Khan, Muhammad Umair Mohammad, Baker Khalifa University System on Chip Center Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350326499

This work proposed a vacancy-modulated analog resistive random-access memory (ReRAM) for neuromorphic computing, utilizing a structure composed of Ag/Zn@ZnO/ZnO/FTO. The resistive switching behavior can be modulated by the movement of oxygen vacancies under positive or negative voltages. This characteristic leads to the desired resistive switching behavior for neuromorphic computing. The Ag/Zn@ZnO/ZnO/FTO based analog ReRAM exhibits several desirable features, including a low operation current, forming-free operation, and self-compliance. The conductance change rate of the device can be adjusted efficiently by modifying the amplitudes and pulse intervals of spike pulses. The device can be used for short-time memory (STM) and long-term memory (LTM) memory operations with good retention time. These characteristics align with the requirements for next-generation ReRAM. The introduction of the Ag/Zn@ZnO/ZnO/FTO-based device structure paves the way for the future development of neuromorphic computing memory applications. © 2023 IEEE.

关键词： Zinc oxide

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Comparing the Effectiveness of Machine Learning and Deep Learning Techniques for Feature Envy Detection in Software systems 1

Comparing the Effectiveness of Machine Learning and Deep Lea...

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1st International Conference of Intelligent Methods, systems and Applications, IMSA 2023

作者： Menshawy, Rana S. Yousef, Ahmed H. Salem, Ashraf Faculty of Engineering Computer and System Department Ain Shams University Cairo Egypt Egypt University of Informatics Cairo Egypt

ISBN: (纸本)9798350335569

Code smells are common in poorly designed software that can hinder code maintainability. Automatic detection of design flaws assists developers in identifying code smells in theirsoftware programs to avoid low-quality delivery. However, the interpretation of code smells is subjective, and existing machine learning-based detection tools are limited in their ability to capture complex semantic relationships in textual code. To address this limitation, the paper proposes a detection system that exploits machine learning and deep learning techniques and compares their effectiveness in identifying the feature envy code smell. The study applies six deep learning techniques based on code semantics features extracted from abstract syntax trees, and eleven machine learning techniques based on code structural features. The results show that both approaches are effective in detecting code smells, with Random Forest achieving exceptional performance among the machine learning classifiers. © 2023 IEEE.

关键词： Deep learning

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