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Web Extension for Phishing URL Identification 3

Web Extension for Phishing URL Identification

引用

3rd international conference on intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Kiruthi Whasan, W.R. Khiran Khumhaar, W.R. Reddy, Kalluri Raviteja Dhanalakshmi, R. Kapilan Radhakrishnan, P. Kcg College of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Applied Computing University of Wales Trinity Saint David United Kingdom

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Phishing attacks have been the most consistent and emerging cyber-attack among other social engineering attacks. In spite of several strict mechanisms and policies to identify and avoid phishing websites, new techniques and tools are used by the attackers to exploit the security measures. This paper proposes a system that uses machine learning algorithm to identify phishing URL based on web parameters. Among several machine learning algorithms, Random Forest algorithm is chosen as it provides more accuracy and performance for the identification process. This system is implemented as a web extension in order to increase the performance of the identification process and to decrease the overhead of the system caused when using a standalone software. © 2022 IEEE.

关键词： Decision trees

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Health Insurance Cost Prediction using Machine learning 3

Health Insurance Cost Prediction using Machine Learning

引用

3rd international conference for Emerging Technology, INCET 2022

作者： Bhatia, Kashish Gill, Shabeg Singh Kamboj, Navneet Kumar, Manish Bhatia, Rajesh Kumar Punjabi University Department of Computer Science and Engineering Patiala India Indraprastha Institute of Information Technology Department of Computer Science and Engineering Delhi India Amazon Big Data Engineer Punjab Engineering College Department of Computer Science and Engineering Chandigarh India

ISBN: (纸本)9781665494991

This paper represents a machine learning-based health insurance prediction system. Recently, many attempts have been made to solve this problem, as after Covid-19 pandemic, health insurance has become one of the most prominent areas of research. We have used the USA's medical cost personal dataset from kaggle, having 1338 entries. Features in the dataset that are used for the prediction of insurance cost include: Age, Gender, BMI, Smoking Habit, number of children etc. We used linear regression and also determined the relation between price and these features. We trained the system using a 70-30 split and achieved an accuracy of 81.3%. © 2022 IEEE.

关键词： Health insurance

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A Study of data Augmentation for Imbalance Problem in Network Traffic 3

A Study of Data Augmentation for Imbalance Problem in Networ...

引用

3rd international conference on Blockchain Technology and Information Security, ICBCTIS 2023

作者： Wang, Xu Sun, Shimin Han, Li School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Autonomous Intelligent Technology and System School of Software Tiangong University Tianjin China School of Computer Science and Engineering Tianjin University of Technology National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin China

ISBN: (纸本)9798350340402

The complexity and diversity of smart grid systems increase the likelihood of anomalies in communications between devices in the system, and how these anomalies are detected is critical to the security of the grid system. However, the distribution of traffic types in the grid is often highly unbalanced, with the total amount of normal traffic data often far outweighing the anomalous data. This imbalance compromises the effectiveness of the detection model and poses a significant threat to the overall security of the grid This paper improves on traditional conditional generation adversarial neural networks and designs a new approach to overcome this obstacle and enhance the security of smart grid systems. In the generator part, the spatial features of the data are captured by convolutional neural networks, and the temporal features of the data are captured by gated recurrent units. By integrating these two components into the generative network we can generate synthetic data with both temporal and spatial features, thus enhancing the minority class samples. By incorporating the augmented minority data into the training process, the proposed approach enables the detection model to understand the distribution of features of the minority anomaly samples more accurately. This improved learning accuracy translates into improved model performance, resulting in higher detection rates for Previously under-represented anomalies. In addition, our approach effectively addresses the inherent bias towards traffic types with higher flows typically observed in asymmetric datasets, thus providing a more equitable and comprehensive security solution for smart grid systems. © 2023 IEEE.

关键词： Anomaly detection

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Research on Privacy Protection of Smart Home 3

Research on Privacy Protection of Smart Home

引用

3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Zhang, Wenjun Chen, Yuan Wei, Xia Yan, Yongrui Shaanxi Branch of National Computer Network Center China Emergency Response Technical Team/Coordination Xi'an China School of Information Engineering Xi'An Mingde Institute of Technology Xi'an China China Mobile Communications Group Shanxi Co. Ltd. Taiyuan China

ISBN: (纸本)9798350317022

With the development of the Internet of Things technology, the smart home industry is rapidly developing, and the collection and use of sensitive user data are also increasing. The issue of user privacy protection is becoming increasingly prominent and has received widespread attention. However, related research is still in its infancy. Firstly, the technical architecture of the smart home system was introduced, and an in-depth analysis was conducted on the privacy issues and the reasons for their increasing severity in the system. Secondly, based on the degree of use of privacy data such as user personal information and behavior, existing smart home systems were classified. Once again, the research status of different technologies in smart home privacy protection, such as access control, policy control, data minimization, network traffic obfuscation, federated learning, homomorphic encryption, and zero knowledge proof, was summarized and organized, and a comparative analysis was conducted on various technologies. Finally, corresponding suggestions were proposed for privacy protection in smart home systems. © 2023 IEEE.

关键词： data security federal learning internet of things privacy protection smart home

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Lightweight convolution-based Chinese Speech Synthesis Method 3

Lightweight convolution-based Chinese Speech Synthesis Metho...

引用

3rd international conference on Computer Vision, Image and Deep learning and international conference on Computer engineering and Applications, CVIDL and ICCEA 2022

作者： Yang, Ruotong Shen, Yunfei Yi, Nian Fan, Zhixing Liu, Jiajun Aishan, Wumaier Xinjiang University School of Information Science and Engineering Urumqi China Xinjiang University School of Software Urumqi China

ISBN: (纸本)9781665459112

Speech synthesis technology is one of the key technologies for human-computer speech interaction and is widely used in various fields such as audiobooks and information broadcasting. In this paper, we propose a speech synthesis model based on lightweight convolution (Lightweight Convolution-Tacotron2, LConv-T) to address the problems of severe long-distance information loss and slow inference in Tacotron2 speech synthesis model using recurrent neural networks. The encoder uses multiple lightweight convolutional modules connected in a densely connected manner to obtain the contextual information of the input text over long distances. To address the shortcomings of the proposed LConv-T model for speech synthesis, this paper further proposes a Tacotron2-based feature fusion speech synthesis model (Dynamic Lightweight Convolution-Tacotron2, DLConv-T), which can improve the stability of speech synthesis by using the Bi-LSTM and dynamic lightweight convolution modules respectively. The text feature extraction effectively improves the speech synthesis effect. The experimental results show that compared with the Tacotron2 model, the LConv-T and DLConv-T models reduce the objective evaluation MCD values by 0.15db and 0.42db, and improve the subjective evaluation MOS by 0.15 and 0.47, respectively. © 2022 IEEE.

关键词： Convolution

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Capturing the Essence: Exploring Cutting-Edge Feature Extraction Methods in Digital Image Processing 3

Capturing the Essence: Exploring Cutting-Edge Feature Extrac...

引用

3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Prabu, S. Natarajan, B. George, Roshan Sujitha, S. Bharathi, P Divya Rajeswari, S. School of Computer Science and Engineering VIT University Chennai India Amrita Vishwa Vidyapeetham Chennai India Roever Engineering College Department of Computer Science and Engineering Tamilnadu India Panimalar Engineering College Department of Artificial Intelligence & Data Science Chennai India K. Ramakrishnan College of Engineering Department of Computer Science and Engineering Tamilnadu Tiruchirappalli India

ISBN: (纸本)9798350317022

Feature Extraction (FE) plays a vital role in the fields of image processing, machine learning and pattern recognition. Binarization, resizing, thresholding and normalization are the initial steps applied before extracting features from an image. FE methods are applied to images to extract the most essential features that help in detection, recognition and classification tasks. FE techniques are used in numerous applications, such as object recognition, character recognition and pattern recognition. This research study presents a comprehensive research study of different image FE techniques, including SURF, BRISK, PCA and SIFT, which have been employed for various applications in recent times. This research study explains each method's fundamental concepts and pros and cons and demonstrates in what scenario which feature extraction technique will be better. This research work also presents a summary table of FE techniques used in various applications with their accuracy percentage. © 2023 IEEE.

关键词： Edge Detection Feature Extraction Feature Selection Image Processing Image Representation

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Federated learning Based User Scheduling for Real-Time Multimedia Tasks in Edge Devices 1

引用

3rd EAI international conference on Edge Computing and IoT, EAI ICECI 2022

作者： Wen, Wenkan Liu, Yiwen Gao, Yanxia Zhu, Zhirong Shi, Yuanquan Peng, Xiaoning School of Computer and Artificial Intelligence Huaihua University Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

Edge networks are highly volatile and the quality of device communication and computational resources change not only over time but also according to the movement of users. Current federation learning suffers from poor device network state and failure of devices to upload models in a timely manner. To address these problems, an intelligent scheduling mechanism that uses the predicted device state based on device information to select the appropriate device for federated learning is proposed in this paper. By focusing on information such as communication quality, computational resources, and location information, the information of edge devices is collected to analyze and predict the device network and computing resources to further analyze the state of devices in depth. Experiments are conducted on real datasets, and the experimental results show that the proposed scheduling method can make the global model fit faster than without the algorithm, which significantly improves the training efficiency of federated learning. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Machine learning

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Deep learning Innovations for IoT Network Security and data Privacy 3

Deep Learning Innovations for IoT Network Security and Data ...

引用

3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Dwivedi, Akhilesh Patel, Neeraj Pastariya, Rishab Ies College of Technology Department of Electrical & Electronics Engineering Madhya Pradesh Bhopal462044 India Ies University Department of Computer Science & Engineering Madhya Pradesh Bhopal462044 India Ies College of Technology Department of Computer Science & Engineering Madhya Pradesh Bhopal462044 India

ISBN: (纸本)9798350343274

This research introduces a comprehensive methodology for the redesign of Cloud Service Level Agreements (SLAs) with a primary focus on improving reliability. The methodology is structured into key steps, commencing with an in-depth literature review to understand the current state of cloud computing, SLAs, and reliability. Collaboration with stakeholders, including cloud service providers and customers, is emphasized to gauge specific needs, expectations, and pain points. A set of critical performance indicators (KPIs) and metrics is defined to measure the reliability of cloud services, encompassing factors like uptime, response times, security, and support responsiveness. The core of the methodology revolves around the customization of SLAs. Cloud service providers are benchmarked based on their adherence to redesigned SLAs, aiding customers in selecting providers that align with their reliability requirements. The methodology undergoes rigorous testing under diverse scenarios, including load testing, security testing, and disaster recovery simulations. By educating customers and providers about the benefits and implications of these agreements, the adoption of redesigned SLAs is facilitated. A feedback loop is established to continuously improve the SLAs based on customer insights and experiences. © 2023 IEEE.

关键词： Reliability

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Research on intelligent healthcare scenario construction of "one hospital for the whole city" 3

Research on intelligent healthcare scenario construction of ...

引用

3rd international conference on Biomedical and intelligent Systems, IC-BIS 2024

作者： Guo, Juanjuan Sun, Shuang Information Center Center for Disease Control and Prevention Shandong Qingdao266033 China Implementation Department Lianren Healthcare Co. Shandong Qingdao266033 China

ISBN: (纸本)9781510681279

This research work focuses on addressing the persistent challenges faced by citizens of Qingdao, China, in accessing efficient healthcare services, including repetitive medical examinations and long waiting times during medical consultations. To resolve these issues, the study proposed the implementation of an intelligent healthcare scenario termed "One Hospital for the Whole City."This work makes use of advanced digital technologies such as big data, cloud computing, the Internet of Things (IoT), artificial intelligence (AI), and blockchain to construct a regional information platform that integrates core operation systems of different hospitals. The platform facilitates cross-institutional recognition and sharing of inspection results and enables access to electronic health records across hospitals. To evaluate the effectiveness of the proposed system, detailed performance parameters were utilized, including average waiting time, redundant medical examinations, patient satisfaction scores, resource utilization, patient outcomes, and financial performance. Statistical analysis, including hypothesis testing using t-test method, was conducted to compare outcomes between a control group and an experimental group. Experimental results demonstrated significant improvements in healthcare efficiency and patient satisfaction in the experimental group compared to the control group. Specifically, the average waiting time reduced from 45 to 20 minutes, redundant medical examinations decreased from 30% to 10%, and patient satisfaction scores increased from 3.5 to 4.5. © 2024 SPIE.

关键词： Hospitals

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Deep learning based Rice plant Disease Identification with Transfer learning 3

Deep learning based Rice plant Disease Identification with T...

引用

3rd IEEE international conference on Computing, Communication, and intelligent Systems, ICCCIS 2022

作者： Shibi, C. Sherin Lincy, R. Babitha Rubia, J. Jency Sheeba, Paul T. Institute of AIandDS Saveetha School of Engineering Simats Tamil Nadu Thandalam India Sri Eshwar College of Engineering Cce Department Kinathukadavu Coimbatore India K.Ramakrishnan College of Engineering Department Ece Tamil nadu Trichy India Srm Institute of Science and Technology School of Computing Department of Dsbs Tamil Nadu Kattankulathur India

ISBN: (纸本)9781665462006

Agriculture is the chief food division in India. Mainly most of the peoples are using the rice for food, even in world wide. But the diseases of rice plant threatening the food part. The appropriate recognition and initial prevention of rice leaf diseases are important for cultivating production. Novel method of image recognition with deep learning algorithm will prevent this disaster. The paper presents a deep model architecture to notice such diseases through the leaf of the rice. The model that has formed is trained to analyse diseased leaf and thereafter identify the type of disease from the rice leaf. The paper is planned to use InceptionV3 with transfer learning concept. The outcomes of this paper show that the transfer learning concept for rice leaf disease image recognition presents a speedy and reasonable detection. The accuracy outcomes in the identification of diseases exposed that the presented model is capable and can greatly influence the efficient identification of the diseases. © 2022 IEEE.

关键词： Deep learning

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