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Insider Attack Detection Using Deep Belief Neural Network in Cloud computing

Computer Systems Science & engineering 2022年第5期41卷 479-492页

作者： A.S.Anakath R.Kannadasan Niju P.Joseph P.Boominathan G.R.Sreekanth School of Computing E.G.S.Pillay Engineering CollegeNagapattinam611002TamilnaduIndia Department of Software Systems School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014TamilnaduIndia Department of Computer Science CHRIST(Deemed to be University)Bengaluru560029India Department of Information Security School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014TamilnaduIndia Department of Computer Science and Engineering Kongu Engineering CollegeErode638060TamilnaduIndia

Cloud computing is a high network infrastructure where users,owners,third users,authorized users,and customers can access and store their information *** use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing *** cloud is nowadays highly affected by internal threats of the *** applications such as banking,hospital,and business are more likely affected by real user *** intruder is presented as a user and set as a member of the *** becoming an insider in the network,they will try to attack or steal sensitive data during information sharing or *** major issue in today's technological development is identifying the insider threat in the cloud *** data are lost,compromising cloud users is *** and security are not ensured,and then,the usage of the cloud is not *** solutions are available for the external security of the cloud ***,insider or internal threats need to be *** this research work,we focus on a solution for identifying an insider attack using the artificial intelligence *** insider attack is possible by using nodes of weak users’*** will log in using a weak user id,connect to a network,and pretend to be a trusted ***,they can easily attack and hack information as an insider,and identifying them is very *** types of attacks need intelligent solutions.A machine learning approach is widely used for security *** date,the existing lags can classify the attackers *** information hijacking process is very absurd,which motivates young researchers to provide a solution for internal *** our proposed work,we track the attackers using a user interaction behavior pattern and deep learning *** usage of mouse movements and clicks and keystrokes of the real user is stored in a *** deep belief neural

关键词： Cloud computing security insider attack network security privacy user interaction behavior deep belief neural network

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Imaging Modalities in Brain Cancer Detection and Diagnosis

Imaging Modalities in Brain Cancer Detection and Diagnosis

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Basheer Almuhaya Bishal Saha Mahmood A. Bazel Rehab A. A. Mohammed Ahmed A. A. Mohammed Ezzaldeen Esmail School of CSE Lovely Professional University Phagwara Punjab India Faculty of Engineering and Computing University of Science and Technology Taiz City Yemen Software Engineering Dept Taiz University Taiz Yemen

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

Brain cancer is a highly heterogeneous and complex illness, making diagnosis and treatment extremely difficult. The purpose of this study is to clarify how different data types—such as imaging, genomic, proteomic, and clinical data—affect brain tumor characterizations and therapeutic decision-making. This research explores the application of customized medicine, the molecular categorization of brain tumors, and the monitoring of disease progression. Additionally, a comparative examination of various imaging modalities, including fMRI, MRI, CT, PET, and SPECT, is conducted. As a result, this study highlights the importance of many data kinds in tracking disease development, identifying brain tumor subtypes, and devising tailored treatments. These data types include imaging, genomic, proteomic, and clinical data. It also emphasizes the difficulties and possibilities associated with combining multimodal data, with the goal of stimulating more research on the identification and management of brain cancer. Ultimately, the analysis highlights the current obstacles and potential paths for combining and evaluating multimodal data to improve the prognosis for brain cancer.

关键词： Reviews Imaging Brain cancer Genomics Proteomics Functional magnetic resonance imaging Bioinformatics

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Schrödinger Mechanisms: Optimal Differential Privacy Mechanisms for Small Sensitivity

Schrödinger Mechanisms: Optimal Differential Privacy Mechan...

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IEEE International Symposium on Information Theory

作者： Wael Alghamdi Shahab Asoodeh Flavio P. Calmon Juan Felipe Gomez Oliver Kosut Lalitha Sankar School of Engineering and Applied Sciences Harvard University Department of Computing and Software McMaster University School of Electrical Computer and Energy Engineering Arizona State University

We consider the problem of designing optimal differential privacy mechanisms with a favorable privacy-utility tradeoff in the limit of a large number n of compositions (i.e., sequential queries). Here, utility is measured by the average distance between the mechanism's input and output, evaluated by a cost function c. We show that if n is sufficiently large and the sensitivities of all queries are small, then the optimal additive noise mechanism has probability density function fully characterized by the ground-state eigenfunction of the Schrödinger operator with potential c. This leads to a family of optimal mechanisms, dubbed the Schrödinger mechanisms, depending on the choice of the cost function. Instantiating this result, we demonstrate that for c(x) = x 2 the Gaussian mechanism is optimal, and for c(x) = |x|, the optimal mechanism is obtained by the Airy function, thereby leading to the Airy mechanism.

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Semantic learning and attention dynamics for behavioral classification in police narratives

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Smart Health 2024年 32卷

作者： Attota, Dinesh Chowdary Azmee, Abm Adnan Khan, Md. Abdullah Al Hafiz Pei, Yong Thomas, Dominic Nandan, Monica College of Computing and Software Engineering - Kennesaw State University 1100 South Marietta Pkwy Marietta 30060 GA United States

The proactive identification of behavioral health incidents concerns from police reports is a critical yet underexplored area. The law enforcement officers provide follow-up services to improve community life by manually analyzing and identifying generated public narrative reports after the 911 incident calls. Therefore, automatically identifying these behavioral health calls from public narrative reports helps reduce the current manual labor-intensive identification process for law enforcement officers. In this work, we introduce a novel, multi-faceted approach that combines manual expert annotations, natural language processing (NLP), and cutting-edge machine learning strategies to classify and understand these incidents within police narratives efficiently. Our proposed method retrieves relevant narrative reports utilizing domain knowledge as behavioral health cues as terms/keywords. Our approach automatically extracts different categories of behavioral health cues by utilizing the limited domain knowledge enabled behavioral health terms using a cosine similarity-based thresholding approach. The behavioral health classification model employs an automatic attention-aware feature representation of terms/keywords, categories, and narrative reports to identify behavioral health cases with an accuracy of 85%. Extensive evaluation shows that our proposed model outperforms all the state-of-the-art models by approximately 4%. © 2024 Elsevier Inc.

关键词： Attention Behavioral health Bi-LSTM Inverted index Police narratives Text classification

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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation 14

Towards a Deep Learning Pain-Level Detection Deployment at U...

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14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Ismail, Leila Waseem, Muhammad Danish Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately. © 2023 Elsevier B.V.. All rights reserved.

关键词： Computer vision

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Data-Driven Analysis of Climate Change Impacts on Public Health: A Systematic Review and Future Directions

Data-Driven Analysis of Climate Change Impacts on Public Hea...

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IEEE International Conference on Big Data

作者： Sahar Yarmohammadtoosky Ying Xie School of Data Science and Analytics Kennesaw State University Marietta Georgia USA College of Computing and Software Engineering Kennesaw State University Marietta Georgia USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Climate change profoundly impacts human health, ecosystems, and food and water security. This review provides a comprehensive analysis of climate change's effects on human health using data science methods, including statistical and machine learning models, to assess current knowledge. It identifies critical gaps in data collection and methodological limitations across different regions and climates. Key recommendations include improving data resolution and duration to enhance model accuracy, as well as fostering collaboration between data scientists, public health experts, and climate specialists. The review underscores the need for innovative, data-driven models to better predict and address the health impacts of climate change.

关键词： Climate change Analytical models Biological system modeling Ecosystems Machine learning Data science Data models Thermal pollution Public healthcare Meteorology

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Frequency Filtering Network for Undersampled MRI Reconstruction

Frequency Filtering Network for Undersampled MRI Reconstruct...

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Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Xinren Chen Zhaohui Liu Dongping Xiong Lijun Ouyang Xiaozhi Zhang School of Computing/Software University of South China Hengyang China School of Electrical Engineering University of South China Hengyang China

Magnetic resonance imaging (MRI) is an imaging technology widely used in medical clinical diagnosis. Nevertheless, it always takes a long imaging time to obtain the high spatial resolution MR image. Image reconstruction has been playing a crucial role for accelerated MRI. Recently, deep neural networks show potential to significantly speed up MRI. However, these deep learning methods usually learn the feature information in the spatial domain. Although some works have attempted to utilize the frequency information from the k-space to reconstruct MR images, they still suffer from the inability to consider the global information. To address these limitations, in this work, we propose a new residual filter channel attention network (RFCAN) for undersampled MRI reconstruction. Specifically, the proposed model consists of a novel filter channel attention (FCA) in the frequency domain and the multiscale cascade convolution in the spatial domain to learn both the dual-domain features. This model employs the filtering instead of the traditional self-attention mechanism in Transformer to learn k-space information. It can avoid the limitations of the Transformer window to better learn the global information. We also introduce a frequency channel attention to fine-tune the filtering results to better balance the global and local information. Experiment results show that our model consistently outperforms other state-of-the-art methods and can achieve high-quality MRI reconstruction.

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Fast feature selection algorithm of EEG data based on convolutional neural network

Fast feature selection algorithm of EEG data based on convol...

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Intelligent Control, Measurement and Signal Processing and Intelligent Oil Field (ICMSP), International Conference on

作者： Bin Liu Liming Cao Tianke Fang School of Software Engineering Xiamen University of Technology Xiamen China School of Information Technology Mapua University Manila Philippines School of Computing and Information Engineering Xiamen University of Technology Xiamen China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

Electroencephalography (EEG) data is invaluable for investigating EEG physiology, neuroscience and clinical medicine. However, EEG signals are characterized by temporal nature, high dimensionality, and noise, which make it difficult for traditional feature selection methods for EEG data to extract useful information effectively with high computational effort. To solve this problem, this paper proposed a fast feature selection algorithm for multichannel EEG data based on a convolutional neural network (FFSEEG-GNN). Specifically, the algorithm pre-processes the multichannel EEG data, then extracts the waveform features and builds a CUDA architecture using GPUs to convolutional neural network operations. Finally, the most representative subset of features is selected in order of feature importance and feature extraction is performed. In addition, the simulation comparison experiment proves that the algorithm achieves 92.60% in terms of precision rate. Therefore, the program has accurate feature selection capability, which is expected to play an essential role in clinical medicine.

关键词： Accuracy Neuroscience Noise Signal processing algorithms Feature extraction Electroencephalography Physiology Data mining Convolutional neural networks Overfitting

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High Resolution Image Quality Database

High Resolution Image Quality Database

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Huang Huang Qiang Wan Jari Korhonen School of Computer Science and Software Engineering Shenzhen University Shenzhen P.R. China School of Natural and Computing Sciences University of Aberdeen Aberdeen UK

With technology for digital photography and high resolution displays rapidly evolving and gaining popularity, there is a growing demand for blind image quality assessment (BIQA) models for high resolution images. Unfortunately, the publicly available large scale image quality databases used for training BIQA models contain mostly low or general resolution images. Since image resizing affects image quality, we assume that the accuracy of BIQA models trained on low resolution images would not be optimal for high resolution images. Therefore, we created a new high resolution image quality database (HRIQ), consisting of 1120 images with resolution of 2880 × 2160 pixels. We conducted a subjective study to collect the subjective quality ratings for HRIQ in a controlled laboratory setting, resulting in accurate MOS at high resolution. To demonstrate the importance of a high resolution image quality database for training BIQA models to predict mean opinion scores (MOS) of high resolution images accurately, we trained and tested several traditional and deep learning based BIQA methods on different resolution versions of our database. The database is publicly available in https://***/jarikorhonen/hriq.

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Federated Transfer Learning For Diabetic Retinopathy Detection Using CNN Architectures

Federated Transfer Learning For Diabetic Retinopathy Detecti...

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IEEE Southeastcon

作者： Mohammad Nasajpour Mahmut Karakaya Seyedamin Pouriyeh Reza M. Parizi College of Computing and Software Engineering Kennesaw State University Marietta GA USA

Diabetic Retinopathy is a complication of diabetes that could cause vision loss or even blindness if not detected in the early stages. As a result, having a regular eye exam is critical for maintaining a healthy retina and preventing damage to the eyes. Due to the lack of ophthalmologists in developing countries, there should be a faster approach to analyze the condition of these fundus images collected by different opticians. In this case, developing deep learning approaches comes in handy to enhance the effectiveness of Diabetic Retinopathy (DR) diagnosis accurately. Our main motivation is to develop a system that could manage various medical institutions. The critical part is to preserve privacy while training such deep learning models, which Federated Learning enables a decentralized training method by only sharing the parameters not the actual data. This study investigates three main models using standard transfer learning, Federated Averaging, and Federated Proximal frameworks. We demonstrate that our three models, including standard, FedAVG, and FedProx, were able to detect DR or non-DR images with the accuracy of 92.19%, 90.07%, and 85.81% respectively.

关键词： Training Deep learning Retinopathy Transfer learning Retina Collaborative work Data models

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