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IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

Digital Communications and Networks 2024年第1期10卷 190-204页

作者： Farhan Ullah Shamsher Ullah Gautam Srivastava Jerry Chun-Wei Lin School of Software Northwestern Polytechnical UniversityXian710072ShaanxiChina Knowledge Unit of Systems and Technology University of Management and TechnologySialkot 51040Pakistan Department of Math and Computer Science Brandon UniversityBrandonMB R7A 6A9Canada Department of Computer Science and Math Lebanese American UniversityBeirut1102Lebanon Western Norway University of Applied Sciences BergenNorway Research Centre for Interneural Computing China Medical UniversityTaichungTaiwanChina

A network intrusion detection system is critical for cyber security against llegitimate *** terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,*** terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal *** is challenging to identify a specific attack due to complex features and data imbalance *** address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced ***,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,***,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic ***,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority ***,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network ***,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep *** experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and *** explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

关键词： Network intrusion detection Transfer learning Features extraction Imbalance data Explainable AI Cybersecurity

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Quantum Machine Learning Techniques based on Nurturing Agri-Ontology Framework in Agricultural science 3

Quantum Machine Learning Techniques based on Nurturing Agri-...

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3rd International Conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Deepa, R. Jayalakshmi, V. Thilakavathy, P. Bennet Prabhu, A. Surendran, R. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India Drbccc Hindu College Department of Computer Applications Chennai India Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Kone Elevator India Technology & Engineering Centre Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

The absence of a consistent ontological framework often impedes efficient knowledge organization and sharing within the agricultural community, despite the quantity of data created in agricultural research. The lack of defined vocabulary and conceptual frameworks hinders data integration, agricultural research, and innovation by preventing interoperability between databases and systems. To address these issues, this research work proposes the Nurturing Agri-Ontology Framework (NAOF), which offers a systematic approach to creating and updating agricultural ontologies. Ontologies that faithfully portray agricultural ideas and connections can be more easily created with the help of NAOF, which combines ideas from knowledge management, agricultural science, and ontology engineering. NAOF aims to ensure that the ontologies produced reflect the wide range of viewpoints and terminology used in agricultural research by encouraging cooperation among stakeholders, ontology engineers, and domain specialists. In this study, the improvement of knowledge organization, sharing and integration in the agricultural area is shown with the application of NAOF. Using this research study, scientists can build ontologies that improve agricultural decision-making, data interoperability, and the discovery of new information. Using quantum machine learning techniques, a strong foundation of knowledge can be laid for several agricultural applications, such as precision agriculture, crop management, and the formulation of agricultural policies. Ontology coverage, interoperability, scalability, and usability are established evaluation parameters that are used to assess the efficacy of NAOF in this research work. The analysis shows that NAOF effectively solves the problems and creates a better agricultural knowledge ecosystem by thoroughly evaluating it using these standards. © 2024 IEEE.

关键词： Ontology

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Feature Attention as a Control Mechanism for the Balance of Speed and Accuracy in Visual Search

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Computational Brain and Behavior 2023年第3期6卷 503-512页

作者： Griffith, Thom Townend, Florence J. Baker, Sophie-Anne Lepora, Nathan F. Department of Engineering Maths University of Bristol University Walk Bristol BS8 1TW United Kingdom Centre for Medical Image Computing Department of Computer Science University College London Gower Street London WC1E 6BT United Kingdom

Finding an object amongst a cluttered visual scene is an everyday task for humans but presents a fundamental challenge to computational models performing this feat. Previous attempts to model efficient visual search have focused on locating targets as swiftly as possible, but so far have not considered balancing the costs of lengthy searches against the costs of making errors. Here, we propose a neuro-inspired model of visual search that offers an attention-based control mechanism for this speed-accuracy trade-off. The model combines a goal-based fixation policy, which captures human-like behaviour on a simple visual search task, with a deep neural network that carries out the target detection step. The neural network is patched with a target-based feature attention model previously applied to standalone classification tasks. In contrast to image classification, visual search introduces a time component, which places an additional demand on the model to minimise the time cost of the search whilst also maintaining acceptable accuracy. The proposed model balances these two costs by modulating the attentional strength given to characteristic features of the target class, thereby minimising an associated cost function. The model offers a method for optimising the costs of visual search and demonstrates the value of a decision theoretic approach to modelling more complex visual tasks involving attention. © 2023, The Author(s).

关键词： CNN Feature attention Speed-accuracy trade-off Visual search

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POLAFFINI: Efficient feature-based polyaffine initialization for improved non-linear image registration

arXiv

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arXiv 2024年

作者： Legouhy, Antoine Callaghan, Ross Azadbakht, Hojjat Zhang, Hui Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom AINOSTICS ltd. Manchester United Kingdom

This paper presents an efficient feature-based approach to initialize non-linear image registration. Today, nonlinear image registration is dominated by methods relying on intensity-based similarity measures. A good estimate of the initial transformation is essential, both for traditional iterative algorithms and for recent one-shot deep learning (DL)-based alternatives. The established approach to estimate this starting point is to perform affine registration, but this may be insufficient due to its parsimonious, global, and non-bending nature. We propose an improved initialization method that takes advantage of recent advances in DL-based segmentation techniques able to instantly estimate fine-grained regional delineations with state-of-the-art accuracies. Those segmentations are used to produce local, anatomically grounded, featurebased affine matchings using iteration-free closed-form expressions. Estimated local affine transformations are then fused, with the log-Euclidean polyaffine framework, into an overall dense diffeomorphic transformation. We show that, compared to its affine counterpart, the proposed initialization leads to significantly better alignment for both traditional and DL-based non-linear registration algorithms. The proposed approach is also more robust and significantly faster than commonly used affine registration algorithms such as FSL FLIRT. © 2024, CC BY.

关键词： image registration

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Harvesting Knowledge: An Innovative Ontology Framework for Agricultural Advancement using NPL methodologies 3

Harvesting Knowledge: An Innovative Ontology Framework for A...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Deepa, R. Das, Manisha Thilakavathy, P. Bennet Prabhu, A. Surendran, R. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India Andhrapradesh Rajam India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Computer Science and Engineering Chennai India Kone Elevator India Technology & Engineering Centre Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350369083

This paper introduces Agricultural Advancement using NLP (AA-NLP) techniques as a strategy to overcome the limitations of traditional agricultural management information systems. There is a lot of unstructured data available but this situation makes agricultural research inefficient because of its knowledge extraction and usage failure. This problem is addressed by combining an ontology framework developed for agricultural information management with Natural Language Processing (NLP) methods. The solution, AA-NLP, utilizes modern NLP algorithms which extract, classify and evaluate agricultural information from many textual sources that include research papers, reports and online repositories. AA-NLP achieves effective information retrieval and exploitation through named entity identification, sentiment analysis and semantic similarity as well as other techniques to convert unstructured input into structured ontological representations. Applying AA-NLP has several benefits such as increased discoverability of relevant agricultural information, automatic classification of scientific findings, and generation of actionable insights for decision making in farming practices, crop management and formulation of agricultural policies. The effectiveness of AA-NLP was analyzed in this paper by measuring the precision associated with knowledge extraction and ontology creation through various performance metrics including accuracy, sustainability farming practices, agricultural productivity and precision. © 2024 IEEE.

关键词： Semantics

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Detecting Fake Political News on Social Media by Support Vector Machine Compared to Naive Bayes 2

Detecting Fake Political News on Social Media by Support Vec...

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2nd International Conference Computational and Characterization Techniques in Engineering and sciences, IC3TES 2024

作者： Sireesha, M. Tamilselvan, S. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Cloud Computing and Image Processing Tamil Nadu Chennai India

ISBN: (纸本)9798350364699

The primary objective is to enhance the precision of the Support Vector Machine and evaluate it in comparison to the Naive Bayes algorithm in terms of its ability to forecast fake political news on social media networks. This research work focuses on two groups: Support Vector Machine and Naive Bayes Algorithm, where each group is executed for 10 times. The ClinCalc software was used in calculating samples in which the alpha value is considered as 0.05, and the confidence interval of 95%. Result: There is a statistically significant difference between the algorithm used by Improved Support Vector Machine and Naive Baye with P value as 0.001 (Independent Sample t-Test P © 2024 IEEE.

关键词： Cloud computing Innovative Fake News Detection Internet Access Naive Bayes Support Vector Machine

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SCREENER: A general framework for task-specific experiment design in quantitative MRI

arXiv

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arXiv 2024年

作者： Zheng, Tianshu Wang, Zican Bray, Timothy Alexander, Daniel C. Wu, Dan Zhang, Hui College of Biomedical Engineering & Instrument Science Zhejiang University Zhejiang Hangzhou China Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom

Quantitative magnetic resonance imaging (qMRI) is increasingly investigated for use in a variety of clinical tasks from diagnosis, through staging, to treatment monitoring. However, experiment design in qMRI, the identification of the optimal acquisition protocols, has been focused on obtaining the most precise parameter estimations, with no regard for the specific requirements of downstream tasks. Here we propose SCREENER: A general framework for task-specific experiment design in quantitative MRI. SCREENER incorporates a task-specific objective and seeks the optimal protocol with a deep-reinforcement-learning (DRL) based optimization strategy. To illustrate this framework, we employ a task of classifying the inflammation status of bone marrow using diffusion MRI data with intravoxel incoherent motion (IVIM) modelling. Results demonstrate SCREENER outperforms previous ad hoc and optimized protocols under clinical signal-to-noise ratio (SNR) conditions, achieving significant improvement, both in binary classification tasks, e.g. from 67% to 89%, and in a multi-class classification task, from 46% to 59%. Additionally, we show this improvement is robust to the SNR. Lastly, we demonstrate the advantage of DRL-based optimization strategy, enabling zero-shot discovery of near-optimal protocols for a range of SNRs not used in training. In conclusion, SCREENER has the potential to enable wider uptake of qMRI in the clinic. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal to noise ratio

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Multimodal Learning in image Processing

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computers, Materials & Continua 2025年第2期82卷 3615-3618页

作者： Zhixin Chen Gautam Srivastava Shuai Liu School of Educational Science Hunan Normal UniversityChangsha410081China Institute of Interdisciplinary Studies Hunan Normal UniversityChangsha410081China Department of Math and Computer Science Brandon UniversityBrandonMB R7A 6A9Canada Research Centre for Interneural Computing China Medical UniversityTaichung406040Taiwan Institute of Engineering and Technology Chitkara UniversityChandigarh140401India

1 Introduction onMultimodal Learning in image Processing IP(image processing),as a classical research domain in computer application technology,has been researched for *** is one of the most important research directions in computer vision,which is the basis for many current hotspots such as intelligent transportation/education/industry,*** image processing is the strongest link for AI(artificial intelligence)applying to real world application,it has been a challenging research field with the development of AI,from DNN(deep convolutional network),Attention/LSTM(long-short term memory),to Transformer/Diffusion/Mamba based GAI(generated AI)models,e.g.,GPT and Sora[1].Today,the description ability of single-model feature limits the performance of image *** comprehensive description of the image is required to match the computational performance of current large scale models.

关键词： image computer LSTM

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AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis of the Femoral Head Detection 37

AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis o...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Chen, Haoyang Liu, Shuai Lu, Feng Li, Wei Sheng, Bin Li, Mi Jin, Hai Zomaya, Albert Y. National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Department of Computer Science and Engineering Shanghai Jiao Tong University China Tongji Hospital Tongji Medical College Huazhong University of Science and Technology China

ISBN: (纸本)9781577358800

Early diagnosis of osteonecrosis of the femoral head (ONFH) can inhibit the progression and improve femoral head preservation. The radiograph difference between early ONFH and healthy ones is not apparent to the naked eye. It is also hard to produce a large dataset to train the classification model. In this paper, we propose Asymmetric-Sensitive Transformer (AsT) to capture the uneven development of the bilateral femoral head to enable robust ONFH detection. Our ONFH detection is realized using the self-attention mechanism to femoral head regions while conferring sensitivity to the uneven development by the attention-shared transformer. The real-world experiment studies show that AsT achieves the best performance of AUC 0.9313 in the early diagnosis of ONFH and can find out misdiagnosis cases firmly. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Large dataset

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CAMIL: CONTEXT-AWARE MULTIPLE INSTANCE LEARNING FOR CANCER DETECTION AND SUBTYPING IN WHOLE SLIDE imageS

arXiv

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arXiv 2023年

作者： Fourkioti, Olga De Vries, Matt Jin, Chen Alexander, Daniel C. Bakal, Chris The Institute of Cancer Research London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London United Kingdom

The visual examination of tissue biopsy sections is fundamental for cancer diagnosis, with pathologists analyzing sections at multiple magnifications to discern tumor cells and their subtypes. However, existing attention-based multiple instance learning (MIL) models used for analyzing Whole Slide images (WSIs) in cancer diagnostics often overlook the contextual information of tumor and neighboring tiles, leading to misclassifications. To address this, we propose the Context-Aware Multiple Instance Learning (CAMIL) architecture. CAMIL incorporates neighbor-constrained attention to consider dependencies among tiles within a WSI and integrates contextual constraints as prior knowledge into the MIL model. We evaluated CAMIL on subtyping non-small cell lung cancer (TCGA-NSCLC) and detecting lymph node (CAMELYON16 and CAMELYON17) metastasis, achieving test AUCs of 97.5%, 95.9%, and 88.1%, respectively, outperforming other state-of-the-art methods. Additionally, CAMIL enhances model interpretability by identifying regions of high diagnostic value1 © 2023, CC BY.

关键词： Lung cancer

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