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Interactive and Explainable Optimized Learning for DDoS Detection in Consumer IoT Networks

IEEE Transactions on Consumer Electronics 2024年

作者： Saiyed, Makhduma F Al-Anbagi, Irfan Hossain, M. Shamim University of Regina Faculty of Engineering and Applied Science ReginaSKS4S 0A2 Canada King Saud University College of Computer and Information Sciences Department of Software Engineering Riyadh12372 Saudi Arabia

The integration of Internet of Things (IoT) in consumer environments enhances convenience and security while increasing Human-computer Interaction (HCI). However,this increased interactivity has also increased the vulnerability of Consumer IoT (CIoT) networks to cyber threats,mainly Distributed Denial of Service (DDoS) attacks. The DDoS attacks,which vary in volume,present substantial challenges to these networks' operational integrity and customer trust. This paper introduces the Artificial Intelligence (AI)-driven (ADEPT) system that utilizes explainable and optimized deep-ensemble learning with pruning for DDoS detection. The system uses attention-based ensemble DL for DDoS detection,combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. To address the resource constraints of edge devices in CIoT networks,the system uses Differential Evolution (DE)-based pruning and quantization techniques,optimizing the model for efficient deployment on edge nodes while preserving high performance. An HCI interface is designed to allow network administrators and researchers to engage with the system through dynamic visualizations,facilitating complex data interpretation and empowering administrators to refine detection strategies. The interface,integrating SHapley Additive exPlanations (SHAP) and risk assessment,enhances model transparency and interpretability,highlighting the synergy of HCI and AI. The ADEPT system is evaluated using an experimental testbed and CIoT datasets and has demonstrated over 90% accuracy in detecting high-and low-volume DDoS attacks. © 1975-2011 IEEE.

关键词： Risk assessment

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LDA-UNet: A Lightweight UNet with Multi-Dimensional Selective Attention for Efficient Skin Lesion Segmentation

LDA-UNet: A Lightweight UNet with Multi-Dimensional Selectiv...

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Artificial Intelligence and computer engineering (ICAICE), International Conference on

作者： Qingyang Gao Jingjing Ma Faculty of Software and Computer Engineering ITMO University Saint Petersburg Russian Federation College of Science Sichuan Vocational College of Finance and Economics Chengdu China

ISBN: (数字)9798331528911

ISBN: (纸本)9798331528928

Different UNet models do well in tasks like semantic segmentation. Yet, these models still do not use detailed features enough and have weak global feature understanding. UNet links the encoder and decoder with skip connections. This lets the decoder get detailed info from the encoder, which helps the model work better. But having skip connections for just one set of encoder and decoder is not enough to use all the multi-level detailed features. Plus, UNet builds the encoder and decoder with convolution methods. Because convolutional operations have a small view of the data, the model's ability to understand the whole set of data is still low. To solve the problems of weak feature use and poor overall modeling, we suggest a simple fix: a light Multi-Dimensional Selective Attention Enhanced UNet (LDA-UNet). To get around the small view of the UNet design, we add the Multi-Dimensional Selective Attention Block. This part boosts the model's global skill and widens the view while using fewer parameters. To fix the issue of low use of multi-level features, we use a multi-level skip connection method. This helps the decoder reach features at different sizes and improves detail capture. We tested the model on the ISIC-2018 dataset, and the results show that, when compared to other methods, this algorithm does a better job at segmentation while using fewer parameters.

关键词： Training Databases Convolution Semantic segmentation Skin Data models Decoding Lesions Artificial intelligence Diseases

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Sentiment Analysis on Students’ Evaluation of Higher Educational Institutions

Sentiment Analysis on Students’ Evaluation of Higher Educat...

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International Conference on Intelligent and Fuzzy Systems, INFUS 2020

作者： Toçoğlu, Mansur Alp Onan, Aytuğ Department of Software Engineering Faculty of Technology Manisa Celal Bayar University Manisa45400 Turkey Department of Computer Engineering Faculty of Engineering and Architecture İzmir Katip Çelebi University İzmir35620 Turkey

ISBN: (纸本)9783030511555

Sentiment analysis is the method of identifying and classifying the views of users from text documents into different sentiments, such as positive, negative or neutral. Sentiment analysis can be employed to extract structured and informative knowledge from unstructured text pieces. This knowledge can serve as an important source of information for decision support systems and individual decision makers. Sentiment analysis plays an important role in many fields like education, where student input is crucial for assessing the effectiveness of educational institutions. In this paper, we present a machine learning based approach to sentiment analysis on students’ evaluation of higher educational institutions. We analyze a corpus containing approximately 700 student reviews written in Turkish, with the use of conventional text representation schemes and machine learning classifiers. In the experimental analysis, three conventional text representation schemes (i.e., term-presence, term-frequency, TF-IDF scheme) and three N-gram models (1-gram, 2-gram and 3-gram) have been considered in conjunction with four classifiers (i.e., support vector machines, Naïve Bayes, logistic regression, and random forest algorithm). The predictive performance of four ensemble learners (i.e., AdaBoost, Bagging and Random Subspace and voting algorithm) have been also evaluated. The empirical results indicate that machine learning based approach yields promising results on students’ evaluation of higher educational institutions. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sentiment analysis

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Automated Aortic Valve Calcific Area Segmentation in Echocardiography Images Using Fully Convolutional Neural Networks

Automated Aortic Valve Calcific Area Segmentation in Echocar...

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International Conference on Telecommunications and Signal Processing (TSP)

作者： Mervenur Cakir Murat Ekinci Elif Baykal Kablan Mursel Sahin Department of Software Engineering Karadeniz Technical University Trabzon Turkey Department of Computer Engineering Karadeniz Technical University Trabzon Turkey Department of Cardiology Faculty of Medicine Karadeniz Technical University Trabzon Turkey

ISBN: (数字)9798350365597

ISBN: (纸本)9798350365603

Aortic valve calcium scoring is extensively utilized for diagnosing, treating, monitoring, and assessing the risk of aortic stenosis and coronary artery disease. The gold standard method for determining aortic valve calcium score is computed tomography (CT). However, CT is expensive and exposes patients to radiation. Alternatively, echocardiography, which is a cheaper and radiation-free method, may not provide an objective result due to inter- and intra-observer variability among cardiologists. Therefore, automated measurement of aortic valve calcium score is essential. In this paper, a fully convolutional neural network approach is proposed for the segmentation of aortic valve calcified regions from echocardiography images, as the first step in predicting aortic valve calcium score. This aims to achieve a more objective, faster, easier, less costly, and radiation-free automatic calculation of aortic valve calcium score. The proposed method achieves a precision of 86.3%, recall of 80.27%, a dice coefficient of 76.27%, and a Jaccard index of 61.9% on the new dataset including 118 echocardiography images across 82 individual patients. These results show the feasibility and potential efficacy of fully automating aortic valve calcium scoring from echocardiography images.

关键词： Training Visualization Image segmentation Echocardiography Calcium Computed tomography Valves

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Explainable-AI-based two-stage solution for WSN object localization using zero-touch mobile transceivers

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Science China(Information Sciences) 2024年第7期67卷 27-45页

作者： Kai FANG Junxin CHEN Han ZHU Thippa Reddy GADEKALLU Xiaoping WU Wei WANG School of Mathematics and Computer Science Zhejiang A&F University School of Software Dalian University of Technology Faculty of Applied Sciences Macao Polytechnic University Department of Electrical and Computer Engineering Lebanese American University School of Information Engineering Huzhou University Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University

Artificial intelligence technology is widely used in the field of wireless sensor networks(WSN).Due to its inexplicability, the interference factors in the process of WSN object localization cannot be effectively eliminated. In this paper, an explainable-AI-based two-stage solution is proposed for WSN object localization. In this solution, mobile transceivers are used to enlarge the positioning range and eliminate the blind area for object localization. The motion parameters of transceivers are considered to be unavailable,and the localization problem is highly nonlinear with respect to the unknown parameters. To address this,an explainable AI model is proposed to solve the localization problem. Since the relationship among the variables is difficult to fully include in the first-stage traditional model, we develop a two-stage explainable AI solution for this localization problem. The two-stage solution is actually a comprehensive consideration of the relationship between variables. The solution can continue to use the constraints unused in the firststage during the second-stage, thereby improving the performance of the solution. Therefore, the two-stage solution has stronger robustness compared to the closed-form solution. Experimental results show that the performance of both the two-stage solution and the traditional solution will be affected by numerical changes in unknown parameters. However, the two-stage solution performs better than the traditional solution, especially with a small number of mobile transceivers and sensors or in the presence of high noise. Furthermore,we have also verified the feasibility of the proposed explainable-AI-based two-stage solution.

关键词： explainable AI object localization semidefinite relaxation mobile transceiver two-stage solution closed-form solution

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Compact NSGA-II for Multi-objective Feature Selection*

arXiv

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arXiv 2024年

作者： Miyandoab, Sevil Zanjani Rahnamayan, Shahryar Bidgoli, Azam Asilian Department of Electrical Computer and Software Engineering Ontario Tech University OshawaON Canada Department of Engineering Brock University St. CatharinesON Canada Faculty of Science Wilfrid Laurier University WaterlooON Canada

Feature selection is an expensive challenging task in machine learning and data mining aimed at removing irrelevant and redundant features. This contributes to an improvement in classification accuracy, as well as the budget and memory requirements for classification, or any other post-processing task conducted after feature selection. In this regard, we define feature selection as a multi-objective binary optimization task with the objectives of maximizing classification accuracy and minimizing the number of selected features. In order to select optimal features, we have proposed a binary Compact NSGA-II (CNSGA-II) algorithm. Compactness represents the population as a probability distribution to enhance evolutionary algorithms not only to be more memory-efficient but also to reduce the number of fitness evaluations. Instead of holding two populations during the optimization process, our proposed method uses several Probability Vectors (PVs) to generate new individuals. Each PV efficiently explores a region of the search space to find non-dominated solutions instead of generating candidate solutions from a small population as is the common approach in most evolutionary algorithms. To the best of our knowledge, this is the first compact multiobjective algorithm proposed for feature selection. The reported results for expensive optimization cases with a limited budget on five datasets show that the CNSGA-II performs more efficiently than the well-known NSGA-II method in terms of the hypervolume (HV) performance metric requiring less memory. The proposed method and experimental results are explained and analyzed in detail. Copyright © 2024, The Authors. All rights reserved.

关键词： Feature Selection

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Improving Code Coverage for Structural Testing by using Parametric Inter-procedural Slicing

Improving Code Coverage for Structural Testing by using Para...

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computer Applications (ICCA), IEEE Conference on

作者： Myint Myitzu Aung Kay Thi Win Software Engineering Lab University of Computer Studies Mandalay Mandalay Myanmar Faculty of Information Science University of Computer Studies Mandalay Mandalay Myanmar

Structural testing is one of the testing techniques in software testing. It is the testing process of internal structure in the given source code by comparing the expected result and real results and finding out faults. To complete the structural testing, it takes a long time and needs to improve code coverage. Thus, this paper focus on improving code coverage of structural testing by using parametric inter-procedural slicing with backward slicing technique. In program slicing, there are two types of slicing: forward and backward slicing depending on the slicing direction. In this paper, backward slicing is used and it starts at the criteria for the slice consideration. The parametric inter-procedural slicing is one of the program slicing techniques which is an algorithm that takes the slice criteria as a parameter and work to handle inter-procedural data flow by considering many possible dependence relations. By slicing the original input program before measuring code coverage, the input program is simplified and excluded unexecutable statements. Therefore, the proposed system in this paper can reduce the complexity of the source code and improve code coverage for structural testing. Moreover, the used dataset in experiment is benchmark dataset in C and it is converted to Java for the improvement of language oriented.

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Pipeline Representation Using Generic Parallel Algorithms in Heterogeneous Parallel Computing. A comparative analysis 36

Pipeline Representation Using Generic Parallel Algorithms in...

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36th European Modeling and Simulation Symposium, EMSS 2024, Held at the 21st International Multidisciplinary Modeling and Simulation Multiconference, I3M 2024

作者： Rossainz-López, Mario Sánchez-Rinza, Bárbara López-Sosa, Zuriel Capel-Tuñon, Manuel Faculty of Computer Science Autonomous University of Puebla Av. San Claudio and 14 Sur Street San Manuel PueblaC.P. 72570 Mexico Software Engineering Department College of Informatics and Telecommunications ETSIIT University of Granada Daniel Saucedo Aranda s/n Granada18071 Spain

ISBN: (纸本)9791281988026

Using Parallel Objects and Structured Parallel Programming, the parallel representation of the Communication Pattern between Processes called Pipeline is shown, whose implementation is carried out through different models of Generic Parallel Algorithms within Heterogeneous Parallel Computing (HPC): As a Parallel Design Pattern or PDP, as High-Level Parallel Composition or HLPC and as Flow Graph Interfaces or FGI through programming with message passing. An original and effective development methodology for new programmers in parallelism is proposed for each of them. In these three proposals for Generic Parallel Algorithms, the same problem-example is solved as a case study, and a comparative analysis of their models and designs is carried out, as well as the performance obtained in the execution of said proposals and demonstrate their usefulness, programmability, and performance. The objective of this work is to provide the programmer and/or novice user with different multicore programming approaches so that without much effort they can develop their programs according to a sequential programming style, obtaining automatically, easily and the counterpart parallelization of your code with the help of a specific programming environment like the one proposed. © 2024 The Authors.

关键词： Structured programming

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Editorial:Special Topic on Federated Learning for IoT and Edge Computing

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ZTE Communications 2022年第3期20卷 1-2页

作者： PAN Yi CUI Laizhong CAI Zhipeng LI Wei Faculty of Computer Science and Control Engineering at Shenzhen Institute of Advanced Technology Chinese Academy of SciencesChina the Academy of the United Nations Sciences and Technology Organization Ukrainian Academy of Engineering Sciences American Institute for Medical and Biological Engineering Institute of Engineering Technology the College of Computer Science and Software Engineering at Shenzhen University China Department of Computer Science and the College of Business at Georgia State University USA the Center for Distributed and High Performance Computing School of Computer ScienceThe University of Sydney the Australia-China Joint Research Center on Energy Informatics and Demand Response Technologies

Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of *** is challenging and infeasible to transfer an... 详细信息

关键词： cloud generating data.

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Emotion Based Signal Enhancement Through Multisensory Integration Using Machine Learning

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computers, Materials & Continua 2022年第6期71卷 5911-5931页

作者： Muhammad Adnan Khan Sagheer Abbas Ali Raza Faheem Khan T.Whangbo Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Engineering Gachon UniversitySeongnam13557Korea

Progress in understanding multisensory integration in human have suggested researchers that the integration may result into the enhancement or depression of incoming *** is evident based on different psychological and behavioral experiments that stimuli coming from different perceptual modalities at the same time or from the same place,the signal having more strength under the influence of emotions effects the response *** research inmultisensory integration has not studied the effect of emotions despite its significance and natural influence in multisensory enhancement or ***,there is a need to integrate the emotional state of the agent with incoming stimuli for signal enhancement or *** this study,two different neural network-based learning algorithms have been employed to learn the impact of emotions on signal enhancement or *** was observed that the performance of a proposed system for multisensory integration increases when emotion features were present during enhancement or depression of multisensory signals.

关键词： Multisensory integration sensory enhancement depression emotions

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