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Wrapper Based Linear Discriminant Analysis(LDA)for Intrusion Detection in IIoT

computer Systems science & Engineering 2023年第5期45卷 1625-1640页

作者： B.Yasotha T.Sasikala M.Krishnamurthy Department of Computer Science and Engineering MNM Jain Engineering CollegeChennai600097India Department of School of Computing Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering KCG College of TechnologyChennai600097India

The internet has become a part of every human ***,various devices that are connected through the internet are ***,the Industrial Internet of things(IIoT)is an evolutionary technology interconnecting various industries in digital platforms to facilitate their ***,IIoT is being used in various industrial fields such as logistics,manufacturing,metals and mining,gas and oil,transportation,aviation,and energy *** is mandatory that various industrial fields require highly reliable security and preventive measures against *** detection is defined as the detection in the network of security threats targeting privacy information and sensitive *** Detection Systems(IDS)have taken an important role in providing security in the field of computer *** of intrusion is completely based on the detection functions of the *** an IIoT network expands,it generates a huge volume of data that needs an IDS to detect intrusions and prevent network *** research works have been done for preventing network *** day,the challenges and risks associated with intrusion prevention are increasing while their solutions are not properly *** this regard,this paper proposes a training process and a wrapper-based feature selection With Direct Linear Discriminant Analysis LDA(WDLDA).The implemented WDLDA results in a rate of detection accuracy(DRA)of 97%and a false positive rate(FPR)of 11%using the Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD)dataset.

关键词： Intrusion detection IIoT wrapper support vector machine(SVM) LDA random forest(RF) feature selection

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Knowledge-enriched joint-learning model for implicit emotion cause extraction

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CAAI Transactions on Intelligence Technology 2023年第1期8卷 118-128页

作者： Chenghao Wu Shumin Shi Jiaxing Hu Heyan Huang School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications BeijingChina

Emotion cause extraction(ECE)task that aims at extracting potential trigger events of certain emotions has attracted extensive attention ***,current work neglects the implicit emotion expressed without any explicit emotional keywords,which appears more frequently in application *** lack of explicit emotion information makes it extremely hard to extract emotion causes only with the local ***,an entire event is usually across multiple clauses,while existing work merely extracts cause events at clause level and cannot effectively capture complete cause event *** address these issues,the events are first redefined at the tuple level and a span-based tuple-level algorithm is proposed to extract events from different *** on it,a corpus for implicit emotion cause extraction that tries to extract causes of implicit emotions is *** authors propose a knowledge-enriched jointlearning model of implicit emotion recognition and implicit emotion cause extraction tasks(KJ-IECE),which leverages commonsense knowledge from ConceptNet and NRC_VAD to better capture connections between emotion and corresponding cause *** on both implicit and explicit emotion cause extraction datasets demonstrate the effectiveness of the proposed model.

关键词： emotion cause extraction external knowledge fusion implicit emotion recognition joint learning

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A Review of Lightweight Security and Privacy for Resource-Constrained IoT Devices

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computers, Materials & Continua 2024年第1期78卷 31-63页

作者： Sunil Kumar Dilip Kumar Ramraj Dangi Gaurav Choudhary Nicola Dragoni Ilsun You Department of Computer Science and Engineering National Institute of Technology JamshedpurJamshedpurIndia School of Computing Science and Engineering VIT University BhopalBhopal466114India Center for Industrial Software The Maersk Mc-Kinney Moller InstituteUniversity of Southern DenmarkSonderborgOdenseDenmark Department of Applied Mathematics and Computer Science Technical University of DenmarkLyngby2800Denmark Department of Information Security Cryptologyand MathematicsKookmin UniversitySeoulSouth Korea

The widespread and growing interest in the Internet of Things(IoT)may be attributed to its usefulness in many different *** settings are probed for data,which is then transferred via linked *** are several hurdles to overcome when putting IoT into practice,from managing server infrastructure to coordinating the use of tiny *** it comes to deploying IoT,everyone agrees that security is the biggest *** is due to the fact that a large number of IoT devices exist in the physicalworld and thatmany of themhave constrained resources such as electricity,memory,processing power,and square *** research intends to analyse resource-constrained IoT devices,including RFID tags,sensors,and smart cards,and the issues involved with protecting them in such restricted *** lightweight cryptography,the information sent between these gadgets may be *** order to provide a holistic picture,this research evaluates and contrasts well-known algorithms based on their implementation cost,hardware/software efficiency,and attack resistance *** also emphasised how essential lightweight encryption is for striking a good cost-to-performance-to-security ratio.

关键词： IoT a sensor device lightweight cryptography block cipher smart card security and privacy

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Alzheimer's Disease Classification via Pre-trained Convolutional Neural Network Variants Utilizing Multi-level Thresholding based on Kapur's Entropy 2

Alzheimer's Disease Classification via Pre-trained Convoluti...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Mahendran, Nivedhitha Durai Raj Vincent, P.M. Srm Institute of Science and Technology School of Computing Technologies Department of Networking and Communications Kattankulathur Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350372892

Neurological diseases are the disorders of brain and the nerves that run throughout the human body. There is a range of symptoms that arise as a result of chemical, structural, or electrical modifications in the regions of brain. One such neurological disease is the Alzheimer's Disease (AD), is considered as a slow and progressive disease that majorly affects the cognitive abilities of the individual. It is most common forms of dementia without any proper cure. However, early diagnosis and treatment might slow down the symptoms and improve the lifestyle of the individual. The most common techniques to identify the neurological disorders are imaging of the brain regions, namely, CT, MRI, PET, etc. The learning approaches, such as Deep Learning and Machine Learning are identified to be effective in analyzing these images to provide better accuracies in detecting the AD. In this study, four versions of Convolutional Neural Networks (CNN) are used to classify AD and Control cases. Further, the Kapur's thresholding technique is also implemented to segment the images for improving the classification accuracy of AD. From the results shown in the study, the inference shows that the VGG16 of CNN provides considerable improvement than the other deep learning models before and after the implementation of thresholding. © 2024 IEEE.

关键词： Convolutional neural networks

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Parallel Software-Based Self-Testing with Bounded Model Checking for Kilo-Core Networks-on-Chip

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Journal of computer science & Technology 2023年第2期38卷 405-421页

作者：张颖季鹏飞朱潘玮 Zebo Peng 李华伟江建慧 School of Software Engineering Tongji UniversityShanghai 200092China Department of Computer and Information Science Linkoping UniversityLinkoping 58183Sweden Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Online testing is critical to ensuring reliable operations of the next generation of supercomputers based on a kilo-core network-on-chip(NoC)interconnection *** present a parallel software-based self-testing(SBST)solution that makes use of the bounded model checking(BMC)technique to generate test sequences and parallel *** this method,the parallel SBST with BMC derives the leading sequence for each router’s internal function and detects all functionally-testable faults related to the function.A Monte-Carlo simulation algorithm is then used to search for the approximately optimum configuration of the parallel packets,which guarantees the test quality and minimizes the test ***,a multi-threading technology is used to ensure that the Monte-Carlo simulation can reach the approximately optimum configuration in a large random space and reduce the generating time of the parallel *** results show that the proposed method achieves a high fault coverage with a reduced test ***,by performing online testing in the functional mode with SBST,it effectively avoids the over-testing problem caused by functionally untestable turns in kilo-core NoCs.

关键词： software-based self-testing(SBST) parallel test kilo-core networks-on-chip(NoCs) online testing

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Biomedically Informed ECG Synthesis: Customizing Cardiac Cycle Phases with Diffusion Model

Biomedically Informed ECG Synthesis: Customizing Cardiac Cyc...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Yuxin Ma, Jing Wang, Wei Wu, Zhihao Dong, Suyu Luo, Gongning Wang, Kuanquan Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Northeast Forestry University College of Computer and Control Engineering Harbin China Harbin Institute of Technology Faculty of Computing Harbin China

ISBN: (纸本)9798350386226

Cardiovascular diseases are a major global health challenge, with electrocardiography (ECG) being critical for diagnosis and monitoring. As artificial intelligence and automated ECG diagnostic technologies rapidly advance, the demand for large-scale ECG databases continues to grow. Generative ECG has become a mainstream method to enhance database size and diversity. However, existing methods typically generate ECG randomly or focus on limited physiological categories, lacking the ability to synthesize ECG with varying physiological features and cardiac cycles, which is crucial for various practical applications. In response to this need, we propose a novel approach introducing a diffusion model called DIFF-ECG to generate precisely customized ECG that accurately reflect diverse cardiac conditions. Segmentation-based quality assessments confirmed that the synthesized ECG accurately followed the specified cardiac cycle information, with our model significantly outperforming baseline diffusion and GAN-based methods. Therefore, our approach addresses the critical need for generating clinically relevant and customizable ECG, contributing significantly to the field of automated cardiac disease diagnosis. By enabling fine-tuning of cardiac cycle phases, our method significantly expands the application range of generative ECG, potentially improving the diagnostic accuracy for rare diseases and advancing personalized medicine. © 2024 IEEE.

关键词： Electrocardiography

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Enhanced Detection of Eye Coloboma through Global Attention U-Net (GA-UNet) for Accurate Ophthalmic Image Segmentation 4

Enhanced Detection of Eye Coloboma through Global Attention ...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Dhayanithi, A. Bala, K. Bharath Institute of Higher Education and Research School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai600073 India

ISBN: (纸本)9798331508845

Eye coloboma is classified as a congenital eye disease that results in developmental anomalies of the eye that may result in eye diseases that essentially alter visions. Fractional and timely diagnosis still matter significantly in its effective managing and treating. This work presents a solution with a Transformer-Enhanced U-Net model that was developed with Transfer Learning to help augment the market's ability to identify and delineate eye coloboma from medical images. This work further extend the gains made in U-Net, that has achieved fairly well especially in the biomedical image segmentation problem, with the inclusion of transformer modules for the incorporation of the long range dependencies and contextual information. This enhancement help the model to attend more on such small features as coloboma and overall diagnostic accuracy in addition. By using Transfer Learning with a pretrained backbone such as Efficient Net, feature representations from large pre-existing datasets are fed to the system as a solution to the reduction of training time and while the performance of the system drops no further effect is *** approach involves the use of fine-tuning with Transformer-Enhanced U-Net model utilizing a subset of the eye images, which are labelled according to the existence of coloboma. Next, by adopting the self-attention mechanism, the model becomes able to pay attention to the relevant areas in the image, and hence is sensitive to any form of coloboma. In initial experiments, this scheme outperforms the standard convolutional networks for segmenting complex regions with minimal false negative rates. Moreover, by comparing the features extracted at deep layers with ground truth, the effectiveness of the model to realistic clinical usage is established given that the model is robust to changes in imaging conditions. The purpose of this research therefore is to develop an Auto-Generated diagnostic tool that will be more reliable as it assists the

关键词： Image segmentation

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Achieving Privacy-Preserving Optimizer Architecture for Intent Execution on EVM Blockchain 6

Achieving Privacy-Preserving Optimizer Architecture for Inte...

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6th ACM International Symposium on Blockchain and Secure Critical Infrastructure, BSCI 2024

作者： Wang, Weijie Deng, Haotian Liang, Jinwen Zhang, Chuan School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China Department of Computing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9798400706387

Intention adoption represents a migration from an imperative to a declarative paradigm, which is expected to significantly improve the user experience in blockchain. While the development of account abstraction has expanded the range of possibilities for intent expression, the architecture for intent-centric collaboration and coordination remains underdeveloped. Existing works depend on trusted centralized entities and employ broadcasts to the network to disseminate user intent, risking single points of failure and privacy leakage. In addition, no standard intent architecture has been proposed. In this paper, we propose a novel decentralized privacy-preserving optimizer architecture for executing intents on the EVM blockchain. We first propose standard intent structures and zero-knowledge intent structures. Our architecture allows users to transform raw intent structures into zero-knowledge intent structures and send them to a set of trusted optimizers. The optimizers can optimize and execute the intents without revealing the content of the intents. We use zero-knowledge proof and public-key encryption to ensure the privacy and correctness of the intents, and smart contracts to ensure the security and verifiability of the execution. We also design a quantization function to evaluate and compare solutions from different optimizers. We analyze the security and performance of our architecture and show that it can achieve privacy, efficiency, and scalability of intent execution on the EVM blockchain. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Differential privacy

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Unsupervised Domain Adaptation for Entity Blocking Leveraging Large Language Models

Unsupervised Domain Adaptation for Entity Blocking Leveragin...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Wang, Yaoshu Yan, Mengyi Shenzhen Institute of Computing Sciences Fundamental Research Department Shenzhen China Beihang University Computer Science Department Beijing China

ISBN: (纸本)9798350362480

Entity blocking, which aims to find all potentially matched tuple pairs in large-scale data, is an important step for entity resolution. It is non-trivial because it needs to consider both of the effectiveness and efficiency, and the emergence of representation learning has made it possible. Although there exist existing representation learning models for entity blocking, all of them require self-curated training instances in the target domain, which limits their capabilities for unseen data. In this paper, we propose UDAEB, a framework for Unsupervised Domain Adaptation for Entity Blocking that is fine-tuned between the source and target domains using contrastive learning by leveraging the capabilities of LLMs. UDAEB first adopts the adversarial learning strategy to reduce the distribution discrepency between source and target domains as the warmup step. Based on the initially learned representations, UDAEB involves pre-trained LLMs to enrich robust and distinguishable attributes for source and target domains. Furthermore, we propose an iterative step to fine-tune entity blocking model by selecting high-quality training instances with pseudo-labels by leveraging LLMs. Finally we conduct comprehensive experiments to show UDAEB has the superior performance against the state-of-the-art algorithms with aspects of the pair completeness (PC), pair quality (PQ) and the candidate set size ratio (CSSR). © 2024 IEEE.

关键词： Contrastive Learning

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Video Anomaly Detection using Factorized Self-Attention Transformer 2

Video Anomaly Detection using Factorized Self-Attention Tran...

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2nd International Conference on Computational Intelligence and Network Systems, CINS 2024

作者： Karthik, R. Adithya, S. Shalmiya, P. Subramaniyaswamy, V. School of Computing SASTRA Deemed University Thanjavur India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798331504106

ISBN: (纸本)9798331504106

Recently, video anomaly detection has seen significant interest in computer vision for identifying unexpected events in video streams using computational cameras, which has quite important applications in video surveillance, security, safety, and industrial control. Existing methods heavily rely on handcrafted features and rule-based algorithms to detect such anomalies, but these models typically tend to fail to generalize scenarios. Recently, with the success of deep learning-based approaches, particularly deep convolutional and recurrent neural networks, state-of-the-art models have been proposed to exploit the capability of deep learning to learn complex patterns from raw video without handcrafted features. Although deep neural networks have been shown to be the suitable solution to automatically capture complex relationships, there are still issues with addressing a wide spectrum of scenarios and acquiring large, well-labelled training datasets. In this research, a transformer-based architecture is proposed for anomaly detection using labelled training data. A Video Vision Transformer (ViT) is used to extract complex visual patterns from video frames, while audio data is encoded as spectrograms and transformed into audio features using an audio transformer. The outputs of the transformers are fused using a cross-modal relation-aware network and fed to a robust temporal feature magnitude (RTFM) learning module, which provides an anomaly score and classifies the input as anomaly or normal. Furthermore, different evaluation metrics are used to measure how well the model works, highlighting its ability. The proposed model is effective since transformer architectures are capable of grasping rich temporal relationships. ©2024 IEEE.

关键词： Recurrent neural networks

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