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Ps-IPs: Deploying Intrusion Prevention system with machine learning on programmable switch

Future Generation computer systems 2024年 152卷 333-342页

作者： Lee, Alan Y.-P. Wang, Michael I.-C. Hung, Chi-Hsiang Wen, Charles H.-P. Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taiwan Department of Electrical and Computer Engineering New York University Tandon School of Engineering United States Advanced SDN Technology Research Center National Yang Ming Chiao Tung University Taiwan

Intrusion prevention is significant to avoid device damage and financial losses. researchers have proposed various Intrusion Prevention systems (IPs) to prevent malware, including traditional and sDN-based IPs. However, existing IPss suffer from low throughput problems caused by detection and rule-installation delays. Here, we propose a programmable switch-base IPs (named Ps-IPs), which utilizes the switch CPU and pipeline to detect malware. Ps-IPs consists of four main components: (1) parser, (2) flow filter, (3) recirculation director, and (4) malware detector. According to the experiment, Ps-IPs achieves a 183X throughput than the sDN-based IPs. The response time of Ps-IPs is also reduced by 99.99%, showing that Ps-IPs effectively prevents malware with a single programmable switch. © 2023

关键词： Machine learning

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Parallelism in GNN: Possibilities and Limits of Current Approaches 15th

Parallelism in GNN: Possibilities and Limits of Current A...

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Mele, Valeria Carracciuolo, Luisa Romano, Diego University of Naples "Federico II" Naples Italy Italian National Research Council - CNR Rome Italy

ISBN: (纸本)9783031856990

Graph Neural Networks (GNNs) have emerged as powerful tools for learning on graph-structured data, demonstrating state-of-the-art performance in various applications such as social network analysis, biological network modeling, and recommendation systems. However, the computational complexity of GNNs poses significant challenges for scalability, particularly with large-scale graphs. Parallelism in GNNs addresses this issue by distributing computation across multiple processors, using techniques such as data parallelism and model parallelism. Data parallelism involves partitioning the graph data across different processors, while model parallelism splits the neural network’s layers or operations. These parallelization strategies, along with optimizations such as asynchronous updates and efficient communication protocols, enable GNNs to handle larger graphs and improve training efficiency. This work explores the key computational kernels and looks for those where parallelism could significantly enhance the scalability and performance of GNNs, highlighting the algebraic aspects of each one. This is the first step to better compare recent advances and their implications for future research. © The Author(s), under exclusive license to springer Nature switzerland AG 2025.

关键词： Graph neural networks

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Pre-Impact and Impact Fall Detection based on a Multimodal sensor Using a Deep Residual Network

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Intelligent Automation & soft Computing 2023年第6期36卷 3371-3385页

作者： Narit Hnoohom sakorn Mekruksavanich Anuchit Jitpattanakul Image Information and Intelligence Laboratory Department of Computer EngineeringFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Computer Engineering School of Information and Communication TechnologyUniversity of PhayaoPhayaoThailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok 10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North BangkokBangkok 10800Thailand

Falls are the contributing factor to both fatal and nonfatal injuries in the ***,pre-impact fall detection,which identifies a fall before the body collides with the floor,would be ***,researchers have turned their attention from post-impact fall detection to pre-impact fall ***-impact fall detection solutions typically use either a threshold-based or machine learning-based approach,although the threshold value would be difficult to accu-rately determine in threshold-based ***,while additional features could sometimes assist in categorizing falls and non-falls more precisely,the esti-mated determination of the significant features would be too time-intensive,thus using a significant portion of the algorithm’s operating *** this work,we developed a deep residual network with aggregation transformation called FDsNeXt for a pre-impact fall detection approach employing wearable inertial *** proposed network was introduced to address the limitations of fea-ture extraction,threshold definition,and algorithm *** training on a large-scale motion dataset,the KFall dataset,and straightforward evaluation with standard metrics,the proposed approach identified pre-impact and impact falls with high accuracy of 91.87 and 92.52%,*** addition,we have inves-tigated fall detection’s performances of three state-of-the-art deep learning models such as a convolutional neural network(CNN),a long short-term memory neural network(LsTM),and a hybrid model(CNN-LsTM).The experimental results showed that the proposed FDsNeXt model outperformed these deep learning models(CNN,LsTM,and CNN-LsTM)with significant improvements.

关键词： Pre-impact fall detection deep learning wearable sensor deep residual network

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A method to predict the peak shear strength of rock joints based on machine learning

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Journal of Mountain science 2023年第12期20卷 3718-3731页

作者： BAN Li-ren ZHU Chun HOU Yu-hang DU Wei-sheng QI Cheng-zhi LU Chun-sheng School of Civil and Transportation Engineering Beijing University of Civil Engineering and ArchitectureBeijing 100044China Key Laboratory of Construction and Safety of Water Engineering of the Ministry of Water Resources China Institute of Water Resources and Hydropower ResearchBeijing 100038China School of Earth Sciences and Engineering Hohai UniversityNanjing 210098China Anhui Province Key Laboratory of Building Structure and Underground Engineering Anhui Jianzhu UniversityHefei 230601China Deep Mining and Rock Burst Research Institute China Coal Research InstituteBeijing 100013China School of Civil and Mechanical Engineering Curtin UniversityWestern Australia 6845Australia

In geotechnical and tunneling engineering,accurately determining the mechanical properties of jointed rock holds great significance for project safety *** shear strength(Pss),being the paramount mechanical property of joints,has been a focal point in the research *** are limitations in the current peak shear strength(Pss)prediction models for jointed rock:(i)the models do not comprehensively consider various influencing factors,and a Pss prediction model covering seven factors has not been established,including the sampling interval of the joints,the surface roughness of the joints,the normal stress,the basic friction angle,the uniaxial tensile strength,the uniaxial compressive strength,and the joint size for coupled joints;(ii)the datasets used to train the models are relatively limited;and(iii)there is a controversy regarding whether compressive or tensile strength should be used as the strength term among the influencing *** overcome these limitations,we developed four machine learning models covering these seven influencing factors,three relying on support Vector Regression(sVR)with different kernel functions(linear,polynomial,and Radial Basis Function(RBF))and one using deep learning(DL).based on these seven influencing factors,we compiled a dataset comprising the outcomes of 493 published direct shear tests for the training and validation of these four *** compared the prediction performance of these four machine learning models with Tang’s and Tatone’s *** prediction errors of Tang’s and Tatone’s models are 21.8%and 17.7%,respectively,while sVR_linear is at 16.6%,sVR_poly is at 14.0%,and sVR_RBF is at 12.1%.DL outperforms the two existing models with only an 8.5%***,we performed shear tests on granite joints to validate the predictive capability of the DL-based *** the DL approach,the results suggest that uniaxial tensile strength is recommended as the material strength term in the Pss model for m

关键词： Peak shear strength Rock joints Prediction model Machine learning Deep learning

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Augmenting Internet of Medical Things security:Deep Ensemble Integration and Methodological Fusion

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computer Modeling in Engineering & sciences 2024年第12期141卷 2185-2223页

作者： Hamad Naeem Amjad Alsirhani Faeiz MAlserhani Farhan Ullah Ondrej Krejcar Faculty of Informatics and Management Center for Basic and Applied ResearchUniversity of Hradec KraloveHradec Kralove50003Czech Republic Department of Computer Science College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Engineering&Networks College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Cybersecurity Center Prince Mohammad Bin Fahd UniversityAl JawharahKhobarDhahran34754Saudi Arabia

When it comes to smart healthcare business systems,network-based intrusion detection systems are crucial for protecting the system and its networks from malicious network *** protect IoMT devices and networks in healthcare and medical settings,our proposed model serves as a powerful tool for monitoring IoMT *** study presents a robust methodology for intrusion detection in Internet of Medical Things(IoMT)environments,integrating data augmentation,feature selection,and ensemble learning to effectively handle IoMT data *** rigorous preprocessing,including feature extraction,correlation removal,and Recursive Feature Elimi-nation(RFE),selected features are standardized and reshaped for deep learning *** using the BAT algorithm enhances dataset *** deep learning models,Transformer-based neural networks,self-attention Deep Convolutional Neural Networks(DCNNs),and Long short-Term Memory(LsTM)networks,are trained to capture diverse data *** predictions form a meta-feature set for a subsequent meta-learner,which combines model *** classifiers validate meta-learner features for broad algorithm *** comprehensive method demonstrates high accuracy and robustness in IoMT intrusion *** were conducted using two datasets:the publicly available WUsTL-EHMs-2020 dataset,which contains two distinct categories,and the CICIoMT2024 dataset,encompassing sixteen *** results showcase the method’s exceptional performance,achieving optimal scores of 100%on the WUsTL-EHMs-2020 dataset and 99%on the CICIoMT2024.

关键词： Cyberattack ensemble learning feature selection intrusion detection smart cities machine learning BAT augmentation

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CNN Issues in skin Lesion Classification: Data Distribution and Quantity

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IEEE ACCEss 2025年 13卷 58848-58862页

作者： Ramella, Giuliana serino, Luca Natl Res Council CNR Inst Applicat Calculus M Picone I-80131 Naples Italy Natl Res Council CNR Inst High Performance Comp & Networking I-80131 Naples Italy

Convolutional Neural Networks (CNNs) have become indispensable tools in skin cancer classification, aiding clinical experts to achieve earlier and more accurate diagnoses, improving treatment outcomes, and driving advancements in medical research. Despite their pivotal role, the most popular CNN architecture families exhibit a critical issue related to the distribution and quantity of available data, potentially compromising their performance and generalization abilities. This challenge is commonly overlooked in most skin lesion classification papers, which mainly rely on weighted classification techniques. Directly using appropriately dataset balancing or Transfer learning (TL) methods, as suggested in recent studies, has the potential to deliver more satisfactory results, providing a more effective approach to addressing this issue. In the effort to tackle this problem, we provide a comprehensive quantitative evaluation aimed at identifying the most critical new emerging computational aspects and the related effective techniques. specifically, we propose twelve Computational Models (CMs) based on four prominent CNN models with increasing structural complexity. We assess their effectiveness in both pretrained and unpretrained versions, incorporating TL strategies as well. Our experiments focus on the IsIC 2018 image dataset, a benchmark widely recognized for its extensive application in skin cancer research yet challenged by significant class imbalance issues. To mitigate this, we also randomly extracted a balanced image subset from IsIC 2018 for evaluation purposes. The experimental results, regarding four different scenarios, provide valuable insights into the design and utilization of CNNs for skin lesion classification, laying the groundwork for further investigations.

关键词： Lesions skin Training Convolutional neural networks computer architecture Computational modeling Image resolution Transfer learning Melanoma Image segmentation Balanced image dataset convolutional neural network dermoscopic image skin lesion classification transfer learning unbalanced image dataset

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Fast UAV path planning in urban environments based on three-step experience buffer sampling DDPG

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Digital Communications and Networks 2024年第4期10卷 813-826页

作者： shasha Tian Yuanxiang Li Xiao Zhang Lu Zheng Linhui Cheng Wei she Wei Xie College of Computer Science South-Central Minzu UniversityWuhan430074China Hubei Provincial Engineering Research Center for Intelligent Management of Manufacturing Enterprises Wuhan430074China School of Computer Science Wuhan UniversityWuhan430072China School of Mathematics&Statistics South-Central Minzu UniversityWuhan430074China

The path planning of Unmanned Aerial Vehicle(UAV)is a critical issue in emergency communication and rescue operations,especially in adversarial urban *** to the continuity of the flying space,complex building obstacles,and the aircraft's high dynamics,traditional algorithms cannot find the optimal collision-free flying path between the UAV station and the ***,in this paper,we study the fast UAV path planning problem in a 3D urban environment from a source point to a target point and propose a Three-step Experience Buffer Deep Deterministic Policy Gradient(TsEB-DDPG)*** first build the 3D model of a complex urban environment with buildings and project the 3D building surface into many 2D geometric *** transformation,we propose the Hierarchical learning Particle swarm Optimization(HL-PsO)to obtain the empirical ***,to ensure the accuracy of the obtained paths,the empirical path,the collision information and fast transition information are stored in the three experience buffers of the TsEB-DDPG algorithm as dynamic guidance *** sampling ratio of each buffer is dynamically adapted to the training ***,we designed a reward mechanism to improve the convergence speed of the DDPG algorithm for UAV path *** proposed TsEB-DDPG algorithm has also been compared to three widely used competitors experimentally,and the results show that the TsEB-DDPG algorithm can archive the fastest convergence speed and the highest *** also conduct experiments in real scenarios and compare the real path planning obtained by the HL-PsO algorithm,DDPG algorithm,and TsEB-DDPG *** results show that the TsEBDDPG algorithm can archive almost the best in terms of accuracy,the average time of actual path planning,and the success rate.

关键词： Unmanned aerial vehicle Path planning Deep deterministic policy gradient Three-step experience buffer Particle swarm optimization

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Estimating Key Phenological Dates of Multiple Rice Accessions Using Unmanned Aerial Vehicle-based Plant Height Dynamics for Breeding

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Rice science 2024年第5期31卷 617-628,I0066-I0070页

作者： HONG Weiyuan LI Ziqiu FENG Xiangqian QIN Jinhua WANG Aidong JIN shichao WANG Danying CHEN song State Key Laboratory of Rice Biology and Breeding China National Rice Research InstituteHangzhou 311400China College of Computer Science and Technology Zhejiang Sci-Tech UniversityHangzhou 310018China College of Agriculture Yangtze UniversityJingzhou 434025China Plant Phenomics Research Centre Academy for Advanced Interdisciplinary StudiesNanjing Agricultural UniversityNanjing 210095China

Efficient and high-quality estimation of key phenological dates in rice is of great significance in breeding work. Plant height(PH) dynamics are valuable for estimating phenological dates. However, research on estimating the key phenological dates of multiple rice accessions based on PH dynamics has been limited. In 2022, field traits were collected using unmanned aerial vehicle(UAV)-based images across 435 plots, including 364 rice varieties. PH, dates of initial heading(IH) and full heading(FH), and panicle initiation(PI), and growth period after transplanting(GPAT) were collected during the rice growth stage. PHs were extracted using a digital surface model(DsM) and fitted using Fourier and logistic models. Machine learning algorithms, including multiple linear regression, random forest(RF), support vector regression, least absolute shrinkage and selection operator, and elastic net regression, were employed to estimate phenological dates. Results indicated that the optimal percentile of the DsM for extracting rice PH was the 95th(R^(2) = 0.934, RMsE = 0.056 m). The Fourier model provided a better fit for PH dynamics compared with the logistic models. Additionally, curve features(CF) and GPAT were significantly associated with PI, IH, and FH. The combination of CF and GPAT outperformed the use of CF alone, with RF demonstrating the best performance among the algorithms. specifically, the combination of CF extracted from the logistic models, GPAT, and RF yielded the best performance for estimating PI(R^(2) = 0.834, RMsE = 4.344 d), IH(R^(2) = 0.877, RMsE = 2.721 d), and FH(R^(2) = 0.883, RMsE = 2.694 d). Overall, UAV-based rice PH dynamics combined with machine learning effectively estimated the key phenological dates of multiple rice accessions, providing a novel approach for investigating key phenological dates in breeding work.

关键词： phenological date plant height unmanned aerial vehicle machine learning rice breeding

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Machine learning-based Prediction of Corrosion Inhibition Efficiency of Expired Pharmaceuticals: Model Development and Application

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Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-12页

作者： Putra, Dzaki Asari surya Ardyansyah, Nibras Bahy Putranto, Nicholaus Verdhy Trisnapradika, Gustina Alfa Akrom, Muhamad Rustad, supriadi Dipojono, Hermawan Kresno Maezono, Ryo Faculty of Computer Science Study Program in Informatics Engineering Universitas Dian Nuswantoro Semarang50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa923-1292 Japan

Corrosion poses a significant challenge in industries due to material degradation and high maintenance costs, making effective inhibitors essential. Recent studies suggest expired pharmaceuticals as alternative corrosion inhibitors, but identifying the most efficient compounds can be time-consuming. This study applies machine learning models, particularly Gradient Boosting Regressor (GBR), to predict corrosion inhibition efficiency (CIE). The developed model is integrated into a web-based application, enabling real-time, accurate CIE predictions. Results show GBR as the more precise model for predicting CIE, with low error rates. The streamlit-based web application offers a user-friendly platform, facilitating quick identification of suitable inhibitors. It is flexible, supporting analysis beyond corrosion inhibitors, and significantly enhances the efficiency of the evaluation process. © The Author(s), under exclusive licence to springer Nature switzerland AG 2025.

关键词： Adversarial machine learning

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An autoencoder-based feature level fusion for speech emotion recognition

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Digital Communications and Networks 2024年第5期10卷 1341-1351页

作者： Peng shixin Chen Kai Tian Tian Chen Jingying National Engineering Research Center for E-Learning National Engineering Laboratory for Educational Big DataCentral China Normal UniversityHubei430079China

Although speech emotion recognition is challenging,it has broad application prospects in human-computer *** a system that can accurately and stably recognize emotions from human languages can provide a better user ***,the current unimodal emotion feature representations are not distinctive enough to accomplish the recognition,and they do not effectively simulate the inter-modality dynamics in speech emotion recognition *** paper proposes a multimodal method that utilizes both audio and semantic content for speech emotion *** proposed method consists of three parts:two high-level feature extractors for text and audio modalities,and an autoencoder-based feature *** audio modality,we propose a structure called Temporal Global Feature Extractor(TGFE)to extract the high-level features of the timefrequency domain relationship from the original speech *** that text lacks frequency information,we use only a Bidirectional Long short-Term Memory network(BLsTM)and attention mechanism to simulate an intra-modal *** these steps have been accomplished,the high-level text and audio features are sent to the autoencoder in parallel to learn their shared representation for final emotion *** conducted extensive experiments on three public benchmark datasets to evaluate our *** results on Interactive Emotional Motion Capture(IEMOCAP)and Multimodal EmotionLines Dataset(MELD)outperform the existing ***,the results of CMU Multi-modal Opinion-level sentiment Intensity(CMU-MOsI)are ***,experimental results show that compared to unimodal information and autoencoderbased feature level fusion,the joint multimodal information(audio and text)improves the overall performance and can achieve greater accuracy than simple feature concatenation.

关键词： Attention mechanism Autoencoder Bimodal fusion Emotion recognition

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