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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Mangalam, Kumar Subba, Basant Indian Institute of Technology Ropar Department of Computer Science Engineering India

ISBN: (纸本)9798350383119

As per the Anti-Phishing Working Group's (APWG) report, there were approximately 4.7 million phishing attacks in the year 2022. A significant portion of these phishing attacks were carried out by alluring unsuspecting users into visiting counterfeit web-pages, which are hosted using malicious uniform resource locators (URLs). This paper proposes a lightweight Bidirectional Long Short Term Memory (BiLSTM) based phishing URL detection framework for mitigating the threats posed by phishing attacks. The proposed framework initially splits the URLs into four distinct components namely, Protocol type, domain, sub-domain and top level domain(TLD) using a set of special characters as delimiters. Different feature values corresponding to these four components are then used to build a vocabulary database of the URL corpus. Thereafter, a customized FastText word embedding technique is used to learn numeric feature vector representations of the tokens(URL features) present in the vocabulary database. These learned feature vectors, along with the pre-processed instances of the URL corpus are then provided as input to train a BiLSTM based classifier model for detection of malicious phishing URLs. Experimental results on a proprietary URL corpus comprising 200,000 normal and phishing URL instances show that the proposed framework achieves high accuracy in detecting malicious phishing URLs with minimal computational overhead. © 2024 IEEE.

关键词： - Phishing attacks BiLSTM FastText word embedding PhishTank UNB dataset

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Fabric Defects Detection Using YOLOv8 4th

Fabric Defects Detection Using YOLOv8

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4th International Conference on Innovations in Computational Intelligence and computer Vision, ICICV 2024

作者： Kapil, Bhavesh Dutta, Kamlesh Computer Science and Engineering Department National Institute of Technology Hamirpur India

ISBN: (纸本)9789819769919

Fabric defect detection poses a significant challenge in the textile industry due to the intricate shapes and diverse range of fabric defects. Numerous approaches have been suggested to address this issue;however, their efficacy in terms of both detection speed and accuracy has been notably limited. This paper introduces an approach for detecting fabric defects using the YOLOv8 model. The methodology involves using the Tinachi dataset, labeling the dataset with LabelImg, applying data augmentation to the dataset, and iteratively training the YOLOv8 network model. The model achieves a mean Average Precision (mAP) of 78.5%, box_loss of 1.6209, a cls_loss of 1.0495, and a dfl_loss of 1.2898. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Textile industry

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FMeAR: FACS Driven Ensemble Model for Micro-Expression Action Unit Recognition

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SN computer Science 2024年第5期5卷 1-9页

作者： Chauhan, Anjaly Jain, Shikha Department of Computer Science & amp Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Micro-expressions (MEs) are fleeting involuntary facial movements, which occur frequently when people attempt to conceal their emotions. Since human eyesight cannot detect fleeting and slight changes in facial expression, it is difficult to determine when spontaneous and low-intensity micro-expression occurs. The paper presents FMeAR which is a Facial action coding scheme (FACS) driven ensemble model for the micro-expression action unit recognition. Considering the simultaneous triggering of multiple facial AUs (action units), the problem is designed as a multiple-binary-class framework. First, each frame of the video is extracted and pre-processed thoroughly to eliminate any noise in the input. Then, features are extracted using an LBP descriptor from the pre-processed frames. To reduce the processing time, the size of the feature vector is reduced using PCA. Finally, reduced features are processed using a multiple ensemble model (comprising of Support Vector Machine, Linear Discriminant Analysis, and Decision Tree classifiers) to identify the micro-expressions action units. The presented model, FMeAR, outperforms several baseline frameworks and shows an accuracy of 95.3% for the CASME dataset, 95.9% for CASME-II, and 92.6% for MEVIEW. Intensive experiments demonstrated the effectiveness and generalization of FMeAR emphasizing its potential for various applications such as deception detection, depression detection, medical diagnosis, and many more. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Action units Ensemble-model FACS FMeAR Micro-expression

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A Systematic Study for Understanding the Security Risks in 5G Core Network 16

A Systematic Study for Understanding the Security Risks in 5...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Dixit, Unnati Vittal, Shwetha Antony Franklin, A. Indian Institute of Technology Hyderabad Department of Computer Science and Engineering India

ISBN: (纸本)9798350383119

The widespread adoption of 5G networks has revolutionized communication, providing great connectivity and services across various verticals. Specifically, on the 5G Core (5GC) this has been made possible by leveraging network slicing and Network Function Virtualization (NFV) where multiple Network Functions (NFs) are involved to serve different user requests on the control plane and a variety of service traffic on the data plane. However, as 5G networks embrace zero trust and multi-tenant architectures, this rapid technological advancement has also exposed the 5GC to various security risks such as Distributed Denial of Service (DDoS), Man in The Middle (MITM), cross-slice disruptions, and side-channel attacks. In this regard, this paper presents a comprehensive approach to enhance the security of 5GC through a systematic study of dependency graphs, Betweenness Centrality (BC), and attack graphs. Potential attack scenarios are carefully analyzed by modeling the 3GPP-defined 5GC, performing vulnerability assessments, building attack graphs, analyzing them, and presenting the key inferences from the study. © 2024 IEEE.

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A Transfer Learning-Based DenseNet with Multi kernel aggregated Channel Attention Block for COVID-19 Classification using CT scan images

A Transfer Learning-Based DenseNet with Multi kernel aggrega...

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Girepunje, Shashank Singh, Pradeep National Institute of Technology Department of Computer Science & Engineering Raipur India

ISBN: (纸本)9798350373783

Accurate diagnosis of COVID-19 using chest X-ray imaging is pivotal for large-scale screening and epidemic containment efforts. However, discerning COVID-19 from CT scan images poses formidable challenges due to the heterogeneous and intricate radiographic features, such as widespread ground-glass opacities and diffuse reticular-nodular opacities. Secondly, the limited number of samples also poses a big challenge for deep learning models that require large number of samples to perform efficiently. In this study, we propose a novel approach for COVID-19 classification using a pre-trained DenseNet architecture combined with a MKACAB (multi-kernel aggregated Channel attention Block). Leveraging the expressive power of DenseNet and the attention mechanism's ability to highlight informative features, our model aims to effectively discriminate between COVID-19 and non-COVID-19 cases. Comprehensive experimental evaluations conducted on publicly available dataset attest to the superior performance of our architecture in the domain of COVID-19 classification. © 2024 IEEE.

关键词： COVID-19

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Review On Text Classification Using Improved Deep Learning Models 2

Review On Text Classification Using Improved Deep Learning M...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Gaikwad, Rajni Mulchandani, Mona Thakur, Reena Jhulelal Institute of Technology Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350387933

Modern deep learning models have exhibited better performance than conventional machine learning methods across a range of text classification tasks, encompassing natural language inference, sentiment analysis, news categorization, and question answering. The application of text classification in natural language processing extends to tasks like spam detection, news categorization, information retrieval, sentiment analysis, and intent assessment. Traditional text classifiers utilizing machine learning techniques encounter challenges such as sparse data, dimensionality issues, and limited generalization capabilities. In contrast, classifiers based on deep learning effectively tackle these limitations, eliminating the need for intricate feature extraction processes, and providing improved learning capabilities along with enhanced prediction accuracy. One notable instance of Convolutional neural networks are used in this way (CNNs). This paper delineates the text classification process, emphasizing the application of deep learning methodologies for text classification. © 2024 IEEE.

关键词： Sentiment analysis

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Concierge: Towards Accuracy-Driven Bandwidth Allocation for Video Analytics Applications in Edge Network 8

Concierge: Towards Accuracy-Driven Bandwidth Allocation for ...

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8th IEEE International Conference on Edge Computing and Communications, EDGE 2024

作者： Huang, Yuyang Zharfan, Faishal Hendrawan, Hendrawan Gunawi, Haryadi S. Jiang, Junchen University of Chicago Department of Computer Science Chicago United States Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350368499

When performing inference on sensor data, edge video analytics applications may not always need high-fidelity data, since important information may not appear all the time. Consequently, each edge AI application's bandwidth demand is highly dynamic. Thus, a shared edge system should dynamically allocate more bandwidth to the applications in need to reach high accuracy at each moment. However, previous bandwidth allocators are ill-suited because they are agnostic to the timevarying impact of bandwidth on each application's *** short paper explores a new accuracy-driven approach to bandwidth allocation, which periodically re-allocates bandwidth across edge AI applications based on the sensitivity of each application's accuracy to its bandwidth share. To examine its practical benefit and technical challenges, we present a concrete accuracy-driven bandwidth allocator called ConciERGE, which exposes a simple yet efficient interface to estimate each application's sensitivity to a small change in its bandwidth *** run CONCIERGE on state-of-the-art video-analytics applications with real video streams and show its early promise in greatly improving the inference accuracy of video analytics. © 2024 IEEE.

关键词： Bandwidth

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Smart Lock Security System 2

Smart Lock Security System

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Ravikumar, Pushpa Arpitha, C.N. Hemanth, N.G. Department of Computer Science and Engineering Adichunchanagiri Institute of Technology Chikkamagaluru India

ISBN: (纸本)9798350383867

With the percentage of single-parent households rising quickly, criminal vulnerability is becoming more of a social issue The main issue is often worry over stranger visiting, especially in the case of single female families. This project focuses on the design strategy for an embedded real-time surveillance system based on a Raspberry Pi for the purpose of detecting intruders. Surveillance technology is reinforced to provide vital security for our daily lives, along with related control and alert functions. Our creative integration of cameras and motion detectors into a web application is the foundation of the suggested security system. Remote sensing cameras and motion detectors are managed and operated by Raspberry Pi. The goal of this research is to create a system that can quickly identify outsiders and respond by taking pictures and sending them to a wireless module that is based on the owner. If owner wish to add the other members to home, then he need to save the person photo in database. © 2024 IEEE.

关键词： Remote sensing

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Deep Learning Approach for Detecting Fake Images Using Texture Variation Network

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Inteligencia Artificial 2023年第72期26卷 1-14页

作者： Haseena, S. Saroja, S. Shri Dharshini, D. Nivetha, A. Department of Information Technology Mepco Schlenk Engineering College India Department of Computer Applications National Institute of Technology Trichy India

Face manipulation technology is rapidly evolving, making it impossible for human eyes to recognize fake faces in photos. Convolutional Neural Network (CNN) discriminators, on the other hand, can quickly achieve high accuracy in distinguishing fake from real face photos. In this paper, we investigate how CNN models distinguish between fake and real faces. According to our findings, face forgery detection heavily relies on the variation in the texture of the images. As a result of the aforementioned discovery, we propose a deep texture variation network, a new model for robust face fraud detection based on convolution and pyramid pooling. Convolution combines pixel intensity and pixel gradient information to create a stationary representation of composition difference information. Simultaneously, multi-scale information fusion based on the pyramid pooling can prevent the texture features from being destroyed. The proposed deep texture variation network outperforms previous techniques on a variety of datasets, including Faceforensics++, DeeperForensics-1.0, Celeb-DF, and DFDC. The proposed model is less susceptible to image distortion, such as JPEG compression and blur, which is important in this field. © IBERAMIA and the authors.

关键词： Convolution

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Efficient Parallel Algorithms For Exact SimRank Computations 31

Efficient Parallel Algorithms For Exact SimRank Computations

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31st IEEE International Conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2024

作者： Mundhara, Aditya Nijhara, Prajjwal Banerjee, Dip Sankar Department of Computer Science and Engineering Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798331509118

SimRank is a popular measure for evaluating node similarities in graphs, but its high computational cost limits scalability for large graphs. The ExactSim [1] algorithm achieves precise single-source SimRank similarities but suffers from significant computation time. In this work, we parallelize key components of ExactSim - Personalized PageRank (PPR) computations and SimRank calculations on a CPU-based shared memory multi-core system. By optimizing these modules, we significantly reduce runtime, making ExactSim more practical for large-scale applications. Our results (on graph sizes ranging from 0.1 million edges to 100 million edges) demonstrate the effectiveness of parallelization in improving both efficiency and scalability. © 2024 IEEE

关键词： Graph algorithms

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