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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Farooq, Refath Chishti, Mohammad Ahsan National Institute of Technology Department of Computer Science Engineering Srinagar India

ISBN: (纸本)9798350372229

The quantification of dependency stands as a crucial element in programming quality. The Software Reliability Growth Model (SRGM) is a commonly utilized tool for evaluating reliability across diverse and challenging scenarios. However, the accuracy of traditional time-sensitive SRGMs may be compromised when test execution extends over a prolonged period. In response to this limitation, the SRGM approach is adapted to center around testing practices rather than relying exclusively on the progression of time. Earlier methods proposed restricted testing capabilities, but the notion of unlimited test time introduces an infinite testing scenario, rendering it impractical. To overcome this, the method introduces Neural Heterogeneous Poisson Process (NHPP) models, which operate without being bound by time limitations. Furthermore, the approach incorporates Artificial Neural Networks (ANNs) to model program failures based on available information. The application of an AI model selection method ensures precise representation of both historical and future load fitting location information Artificial Neural Networks (ANNs) showcase an adept capacity for effective generalization from the input provided to a comparable model, proficiently capturing patterns from prior instances of failure data. Through the integration of common-sense information into programming failure indicators, the suggested Testing Effort Function (TEF) and Software Reliability Growth Model (SRGM) proficiently depict extensive failure data. The incorporation of Machine Learning (ML) and Artificial Neural Networks (ANN) notably enhances the precision of traditional boundary estimation when compared to release time, thereby ensuring robust software reliability. © 2024 IEEE.

关键词： ANN Machine Learning NHPP SRGM Testing Effort Function (TEF)

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Security and Service Vulnerabilities with HTTP/3 16

Security and Service Vulnerabilities with HTTP/3

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

作者： Hari Hara Sudhan, S. Kulkarni, Sameer G. Indian Institute of Technology Gandhinagar Department of Computer Science and Engineering India

ISBN: (纸本)9798350383119

The adoption of Hypertext Transfer Protocol v3 (HTTP/3 or H3) is on the rise. In this context, we analyze the security vulnerabilities of H3, specifically with the QUIC protocol, and the associated challenges they pose to the commonly used network middleboxes. First, we demonstrate how the connection migration feature of QUIC can be used by malicious clients to launch denial of service (DoS) attacks through resource exhaustion of the connection state tables in the simple network address and port translation (NAPT) devices. Further, we show that the connection migration feature disrupts the services of various critical network middleboxes like Layer3/4 load-balancers, rate-limiters, and intrusion detection/prevention systems that rely on the connection state table for their faithful operation. We also present a feasible solution to mitigate the DoS attacks in connection tracking middleboxes. Second, we show how the spin bit in QUIC short header packets can act as a highly reliable covert channel to exchange information stealthily across two end-points. Nonetheless, this spin-bit can also be used to shield from spoofing attacks. Although H3, by virtue of QUIC, aims to be a secure and privacy-preserving protocol, the existence of such vulnerabilities calls for the community to explore and adopt additional measures to make H3 a truly secure protocol. © 2024 IEEE.

关键词： Denial of Service HTTP/3 Middlebox Network functions QUIC Security

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Deep transfer learning technique to detect white blood cell classification in regular clinical practice using histopathological images

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Multimedia Tools and Applications 2025年第9期84卷 5699-5723页

作者： Davamani, K. Anita Jawahar, Malathy Anbarasi, L. Jani Ravi, Vinayakumar Al Mazroa, Alanoud Robin, C. R. Rene Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Leather Process Technology Division CSIR-Central Leather Research Institute Chennai India School of Computing Science and Engineering Vellore Institute of Technology Vellore India Computer Science and Engineering Sri Sairam Engineering College Sirukalathur India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia

Leukemia, a malignant disease characterized by the rapid proliferation of specific types of white blood cells (WBC), has prompted increased interest in leveraging automatic WBC classification system. This study presents the design of a robust WBC classification system, integrating deep learning with microscopic image analysis. The proposed classification model is designed to categorize blood cells into four distinct groups: eosinophil, lymphocyte, monocyte, and neutrophil. The study employs a Kaggle image dataset for comprehensive learning. The dataset comprises 12,500 microscopic images used to categorize the four blood cell groups. To enhance performance and reduce the likelihood of overfitting, transfer learning techniques are employed. Six deep transfer learning models namely VGG16, ResNet50, AlexNet, MobileNet, GoogLeNet, and EfficientNetBO are used for feature extraction and classification. The results of the experiments show that VGG16 and MobileNet demonstrated enhancements compared to other pre-trained models when evaluated on both augmented and original datasets. A diverse set of evaluation criteria was employed to thoroughly assess the effectiveness of medical image classification. Notably, the VGG16 and MobileNet models accurately predicted Lymphocytes and Monocytes with a 100% accuracy rate, and Eosinophil and Neutrophil with rates of 99.35% and 99.81%, respectively. This research work empower medical practitioners, particularly in the field of pathology and hematology by providing advanced tools for accurate blood cell classification. The proposed VGG16 and MobileNet models hold promise for simplifying WBC classification, improving decision-making processes, and ultimately advancing patient care in the medical domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Malicious Activities Prediction Over Online Social Networking Using Ensemble Model

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Intelligent Automation & Soft Computing 2023年第4期36卷 461-479页

作者： S.Sadhasivam P.Valarmathie K.Dinakaran Department of Computer Science and Engineering K.S.R College of EngineeringThiruchengodeIndia Centre for Artificial Intelligence Chennai Institute of TechnologyChennaiIndia Department of Computer Science and Engineering S.A.Engineering CollegeChennaiIndia

With the vast advancements in Information technology,the emergence of Online Social Networking(OSN)has also hit its peak and captured the atten-tion of the young generation *** clone intends to replicate the users and inject massive malicious activities that pose a crucial security threat to the original ***,the attackers also target this height of OSN utilization,explicitly creating the clones of the user’s *** clone detection mechanisms are designed based on social-network *** instance,monitoring the occur-rence of clone edges is done to restrict the generation of clone ***,this assumption is unsuitable for a real-time environment and works optimally during the simulation *** research concentrates on modeling and efﬁ-cient clone prediction and avoidance methods to help the social network activists and the victims enhance the clone prediction *** model does not rely on ***,an ensemble Adaptive Random Subspace is used for clas-sifying the clone victims with k-Nearest Neighbour(k-NN)as a base classiﬁ*** weighted clone nodes are analysed using the weighted graph theory concept based on the classiﬁed *** the weighted node’s threshold value is high-er,the trust establishment is terminated,and the clones are ranked and sorted in the higher place for ***,the victims are alert to the clone propaga-tion over the online social networking end,and the validation is done using the MATLAB 2020a simulation *** model shows a better trade-off than existing approaches like Random Forest(RF),Naïve Bayes(NB),and the standard graph *** performance metrics like True Positive Rate(TPR),False Alarm Rate(FAR),Recall,Precision,F-measure,and ROC and run time analysis are evaluated to show the signiﬁcance of the model.

关键词： Online social network decision tree weighted measure clone attack predictive measures

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PCLive: Pipelined Restoration of Application Containers for Reduced Service Downtime 15

PCLive: Pipelined Restoration of Application Containers for ...

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15th Annual ACM Symposium on Cloud Computing, SoCC 2024

作者： Tripathi, Shiv Bhushan Mishra, Debadatta Department of Computer Science and Engineering Indian Institute of Technology Kanpur India

ISBN: (纸本)9798400712869

Application containers are widely used in contemporary cloud computing environments. Migration of containers across hosts provides cost-effective cloud management by enabling improved server consolidation, load balancing and enhanced fault tolerance. One of the primary objectives of container migration is to reduce the service downtime of applications hosted in containers. The service downtime depends on performing the migration activities efficiently, specifically from the time the container is stopped on the source host till it is restored and fully functional at the destination host. In this paper, we show that, the state-of-the-art pre-copy migration strategy for containers using checkpoint and restore techniques (e.g., CRIU) inflates the downtime due to its inherent limitations in the restoration procedures, particularly for containers with large memory working set size. We propose PCLive to address this bottleneck using a pipelined restore mechanism. Compared to the baseline CRIU pre-copy migration, PCLive results in up to ∼38.8x reduction in restoration time which leads to a reduction of service downtime by up to ∼2.7x for migration of a container hosting the Redis key-value store over an one Gbps network. We also present comprehensive comparative analysis of the resource cost for the proposed solution along with additional optimizations to demonstrate that PCLive can reduce the application downtime in a resource efficient manner leveraging its flexible and efficient design choices. © 2024 Owner/Author.

关键词： Restoration

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Phishion: Phishing Detection Application 2

Phishion: Phishing Detection Application

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Rangasamy, Gokul Primya, T. Kumar, Gurupriya Sivakumar, Harinath Kanagaraj, G. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Kumaraguru College of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350328202

The threat posed by phishing websites is significant in the digital era due to the widespread use of online activities, which has prompted the need for a comprehensive remedy. The project involves building a website that acts as a database for information about phishing URLs, along with retrieving domain details for in-depth examination. Users will be able to contribute and retrieve phishing data, encouraging teamwork and alertness. In addition, a specialized Chrome extension will provide real-time detection of phishing websites with the support of machine learning techniques and pattern matching. In response to the growing demand for a secure online environment, this effort works as a cohesive ecosystem to protect online users by gathering important data and actively alerting them, when possible, dangers are detected. © 2024 IEEE.

关键词： Websites

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Enhanced U-Net with Transformer-Driven Encoder for Medical Image Segmentation

Enhanced U-Net with Transformer-Driven Encoder for Medical I...

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2024 International Conference on Brain computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： Palaniappan, Ramya Surendran, Suseendran Balakrishnan, Saranya Madanapalle Institute of Technology & Science Department of Computer Science & Engineering Madanapalle India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India KGiSL Institute of Technology Department of Artificial Intelligence and Data science Coimbatore India

ISBN: (纸本)9798331540067

The automatic segmentation of tumours or lesions from magnetic resonance imaging (MRI) pictures is a critical but difficult task in clinical settings, sometimes necessitating laborious and time-consuming techniques. Deep learning has made a significant advancement in the medical imaging field in recent years. U-Net has emerged as the predominant model used for segmenting medical images. Although the standard U-Net framework has shown excellent performance in segmenting multimodal medical pictures, it has been noticed that it lags in certain aspects when dealing with tough datasets. We propose specific improvements to the state-of-the-art U-Net model. We have introduced attention block in the skipped connections to highlight relevant features. Additionally, we have incorporated transformers in the encoding path to capture temporal dependencies in medical images. In this research work, we have introduced an innovative architecture that has the potential to replace the U-Net model for segmenting regions of interest (ROI) in medical images. This study utilizes a total of 1760 samples obtained from the ISBI 2015 and PH2 medical repositories. The experimental findings have demonstrated that our proposed model has obtained highly encouraging results of 78.02 Jaccard coefficient and IoU of 65.14. In addition, we conducted tests and compared the performance of the proposed model with the classical U-Net model using the same dataset. As a result, we observed a relative improvement in the performance of both 2.36 and 1.85, respectively. The proposed technology will function as a sophisticated toolkit for radiologists to precisely detect and diagnose anomalous tumour cells. © 2024 IEEE.

关键词： Semantic Segmentation

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A New Approach for Prevention of Malicious Attack in Post Disaster Using Delay Tolerant Network

A New Approach for Prevention of Malicious Attack in Post Di...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Chakrabarti, Chandrima Bal, Saptarshi Jaiswal, Rishabh Ansari, Arbaz Akhtar Narula Institute of Technology Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350375237

Natural disasters still have a crippling effect on our infrastructure notwithstanding society's development. Communication systems, the backbone of our network, sustain the most damage during such crises. When it comes to setting up working communication systems, volunteers working on disaster relief missions face enormous obstacles because of the deteriorating communication infrastructure and extended power outages. Smartphones have become essential tools because they may create a peer-to-peer network, in opportunistic way also called as a Delay Tolerant Network (DTN), through Bluetooth or Wi-Fi connections in a latency mode, which makes communication easier in post-disaster situations. Nevertheless, nodes in this network can act strangely, failing to create routes or send data to other nodes, which would hinder the functioning of the network as a whole. Malicious nodes are those that behave in this way. They have the ability to either deny or destroy messages, which can cause serious disruptions. The performance of the network may be further complicated by some nodes displaying partial malicious activity, which causes interference on a selective basis. In response to these difficulties, this work suggests a novel technique for identifying and mitigating the effects of malicious nodes coordinating assaults inside DTNs. Through simulations with the Opportunistic Network Environment (ONE) simulator, the efficacy of the scheme was assessed, demonstrating its ability to reduce the disruptive impact of malicious nodes on network operations. This suggested method shows promise in strengthening DTNs against malevolent attacks and promoting more dependable communication infrastructures in places affected by disasters. © 2024 IEEE.

关键词： Delay tolerant networks

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Pedestrian Trajectory Prediction with Missing Data: Datasets, Imputation, and Benchmarking 38

Pedestrian Trajectory Prediction with Missing Data: Datasets...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chib, Pranav Singh Singh, Pravendra Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

Pedestrian trajectory prediction is crucial for several applications such as robotics and self-driving vehicles. Significant progress has been made in the past decade thanks to the availability of pedestrian trajectory datasets, which enable trajectory prediction methods to learn from pedestrians' past movements and predict future trajectories. However, these datasets and methods typically assume that the observed trajectory sequence is complete, ignoring real-world issues such as sensor failure, occlusion, and limited fields of view that can result in missing values in observed trajectories. To address this challenge, we present TrajImpute, a pedestrian trajectory prediction dataset that simulates missing coordinates in the observed trajectory, enhancing real-world applicability. TrajImpute maintains a uniform distribution of missing data within the observed trajectories. In this work, we comprehensively examine several imputation methods to reconstruct the missing coordinates and benchmark them for imputing pedestrian trajectories. Furthermore, we provide a thorough analysis of recent trajectory prediction methods and evaluate the performance of these models on the imputed trajectories. Our experimental evaluation of the imputation and trajectory prediction methods offers several valuable insights. Our dataset provides a foundational resource for future research on imputation-aware pedestrian trajectory prediction, potentially accelerating the deployment of these methods in real-world applications. Publicly accessible links to the datasets and code files are available at https://***/Pranav-chib/TrajImpute. © 2024 Neural information processing systems foundation. All rights reserved.

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Blockchain Approaches for Healthcare: A Review and Outlook 5

Blockchain Approaches for Healthcare: A Review and Outlook

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Shaikh, Natasha Kasar, Smita Maharashtra Institute of Technology Department of Computer Science & Engineering Aurangabad India

ISBN: (纸本)9798350379945

Nowadays, the application of information technology worldwide is accelerating the digital transformation of the healthcare sector. Incorporating blockchain technology into healthcare provides better and more innovative solutions for healthcare providers to enhance the patient's care. By establishing a shared ledger that is cooperatively maintained and impenetrable to tampering, blockchain (BC) technology offers a novel approach to record historical data and ensure the reliability and security of data stored in distributed networks. Nevertheless, new issues relating to privacy and security arise with the integration of the blockchain into the healthcare industry. Hence, this research review concentrates on offering a thorough literature assessment of the chosen papers to emphasize the research subjects that are currently being investigated in blockchain-based healthcare systems and highlight their limitations and potential directions for strengthening the digital security of healthcare data. By analyzing current trends and implications, this research aims to provide a concise overview of existing approaches, and how BC is being utilized to enhance data security, interpretability, and transparency in the healthcare system. The research analyzes 25 literature papers on BC technology for the healthcare domain. Further, the evaluation based on the metrics utilized and year-wise analysis of the selected publications is carried out in this research. Finally, the research also provides an overview of blockchain-related healthcare mechanisms that provide solutions for various applications. © 2024 IEEE.

关键词： Electronic health record

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