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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Singh, Yuvraj Goel, Paranjay Aggarwal, Shubhani Chaudhary, Rajat Budhiraja, Ishan Amity University Amity School of Engineering & Technology Department of Computer Science & Engineering Punjab India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350391725

Cross-Site Request Forgery (CSRF) remains a pervasive vulnerability in web applications, appearing regularly in the OWASP Top 10 lists, posing significant threats to user data and system integrity despite the existence of numerous rectification methodologies. This study undertakes a thorough, comprehensive analysis of the present environment of CSRF prevention systems, investigating their operational mechanisms, effectiveness, and associated limitations. Hence, this paper explores the role of CSRF tokens in the prevention of cyberattacks, including the detection of token absence and the implementation of token-based countermeasures to mitigate CSRF risks. The system model utilizing simple JavaScript and Flask Applications that aim to utilize the token-based countermeasure to identify the existence of tokens and generate and append them to requests, which ensures security of user data and system integrity. © 2024 IEEE.

关键词： CSRF attacks CSRF tokens Mitigation Prevention

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Faster AMEDA-A Hybrid Mesoscale Eddy Detection Algorithm

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computer Modeling in engineering & sciences 2024年第11期141卷 1827-1846页

作者： Xinchang Zhang Xiaokang Pan Rongjie Zhu Runda Guan Zhongfeng Qiu Biao Song Organization Department Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China School of Teacher Education Nanjing University of Information Science and TechnologyNanjing210044China School of Electronics&Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China School of Software Nanjing University of Information Science and TechnologyNanjing210044China

Identification of ocean eddies from a large amount of ocean data provided by satellite measurements and numerical simulations is crucial,while the academia has invented many traditional physical methods with accurate detection capability,but their detection computational efficiency is *** recent years,with the increasing application of deep learning in ocean feature detection,many deep learning-based eddy detection models have been developed for more effective eddy detection from ocean *** it is difficult for them to precisely fit some physical features implicit in traditional methods,leading to inaccurate identification of ocean *** this study,to address the low efficiency of traditional physical methods and the low detection accuracy of deep learning models,we propose a solution that combines the target detection model Faster Region with CNN feature(Faster R-CNN)with the traditional dynamic algorithm Angular Momentum Eddy Detection and Tracking Algorithm(AMEDA).We use Faster R-CNN to detect and generate bounding boxes for eddies,allowing AMEDA to detect the eddy center within these bounding boxes,thus reducing the complexity of center *** demonstrate the detection efficiency and accuracy of this model,this paper compares the experimental results with AMEDA and the deep learning-based eddy detection method *** results show that the eddy detection results of this paper are more accurate than eddyNet and have higher execution efficiency than AMEDA.

关键词： Mesoscale eddy detection object detection contour detection remote sensing

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A Graph-Based Approach to Automatic Car Reliability System Prediction with Probabilistic Modeling and Machine Learning 3

A Graph-Based Approach to Automatic Car Reliability System P...

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

作者： Krishna, Kaivaram Sivarama Kumar, Bommala Deepak Reddy, Morumpalli Deekshith Varun, Bheema Sai Murali, K. Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350369083

Reliability prediction in automotive systems undoubted represents a substantial part of safety and customer satisfaction. a new graph-based probabilistic method and machine learning algorithm for the automotive system reliability prognosis. By breaking complex dependency among vehicles' components into a hierarchy and using past failure data, the method improves on prediction error. Real-world automotive datasets show excellent results with better resolution and higher accuracy for the enhanced components compared to existing techniques while at the same time providing interpretability by visualizing the dependency of the components. © 2024 IEEE.

关键词： Prediction models

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Multi-verse Optimizer with Rosenbrock and Diffusion Mechanisms for Multilevel Threshold Image Segmentation from COVID-19 Chest X-Ray Images

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Journal of Bionic engineering 2023年第3期20卷 1198-1262页

作者： Yan Han Weibin Chen Ali Asghar Heidari Huiling Chen Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China School of Surveying and Geospatial Engineering College of EngineeringUniversity of TehranTehranIran

Coronavirus Disease 2019(COVID-19)is the most severe epidemic that is prevalent all over the *** quickly and accurately identifying COVID-19 is of great significance to controlling the spread speed of the ***,it is essential to accurately and rapidly identify COVID-19 lesions by analyzing Chest X-ray *** we all know,image segmentation is a critical stage in image processing and *** achieve better image segmentation results,this paper proposes to improve the multi-verse optimizer algorithm using the Rosenbrock method and diffusion mechanism named *** utilizes RDMVO to calculate the maximum Kapur’s entropy for multilevel threshold image *** image segmentation scheme is called *** ran two sets of experiments to test the performance of RDMVO and ***,RDMVO was compared with other excellent peers on IEEE CEC2017 to test the performance of RDMVO on benchmark ***,the image segmentation experiment was carried out using RDMVO-MIS,and some meta-heuristic algorithms were selected as *** test image dataset includes Berkeley images and COVID-19 Chest X-ray *** experimental results verify that RDMVO is highly competitive in benchmark functions and image segmentation experiments compared with other meta-heuristic algorithms.

关键词： COVID-19 Multilevel threshold image segmentation Kapur’s entropy Multi-verse optimizer Meta-heuristic algorithm Bionic algorithm

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Supply Chain Management Using Internet of Things and Blockchain for Smart Meat Packaging 5

Supply Chain Management Using Internet of Things and Blockch...

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5th International Conference on Data Intelligence and cognitive Informatics, ICDICI 2024

作者： Kour, Kanwal Preet Singh, S. Kanwal Deep Bali, Malvinder Singh Ghantasala, G. S. Pradeep Rajesh Sharma, R. Ananthanagu, U. Manipal University School of Computer Science and Engineering Jaipur India Federation University Department of Computer Science Australia Manipal University Department of Data Science and Engineering Rajasthan Jaipur India Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350389609

In recent times, blockchain has evolved the security of traditional supply chain systems. Different issues of supply chain management like flexibility and reliability can be easily addressed using blockchain. Meat Packing Industry involves the complete process of processing, packaging and supplying the meat and its products throughout the world for human consumption. The animals used for the meat supply chain include pigs, sheep, cattle etc. Due to the enormous increase in the number of meat-eaters worldwide, its packing and timely supply are of utmost importance. The meat packing industry has revolutionized a lot by using the latest supply chain methods based on new technologies like blockchain. These include adulteration, long-term delays leading to spoilage of the product, mixing of other similar products which are lower in price etc. All these issues can be easily handled by using blockchain and IoT for meat supply chain management. Using these technologies, the challenges ranging from timely transportation of good quality meat to the best quality meat delivered to the customer can be easily addressed. In this paper, an effective blockchain and IoT-based model has been proposed which will counter the above challenges and improve the reliability and security factor. © 2024 IEEE.

关键词： Supply chain management

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Mood Melody Matchmaker System Using Deep Learning Model 9

Mood Melody Matchmaker System Using Deep Learning Model

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Mishra, Susmita Pradhan, Chittaranjan Jananee, V. Rajalakshmi Engineering College Department of Computer Science And Engineering Chennai India School of Computer Engineering KIlT Deemed To Be University Bhubaneswar India Rajalakshmi Engineering College Department of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350365092

Accessing a wide variety of music is made simple by modern music services. Many users rely on recommendation algorithms to select the ideal song for any given circumstance. Due to its frequent use for relaxation, mood regulation, stress, and illness management, as well as support for mental and physical activities, music has a profound influence on individuals. Music therapy employs a range of therapeutic environments and methods to enhance overall well-being. The article suggests a mood melody matching related to the listener's feelings. Machine learning and deep learning methods are employed to create a recommendation system designed to assist individuals in choosing music suitable for various life circumstances. Efficiently recognizing the ideal track for the user's current mood, the system delivers songs tailored to their emotional state, eliminating the need for users to spend time searching or seeking out music themselves. © 2024 IEEE.

关键词： Python

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Cyber Chronicles: Tracking Behavior Patterns for Detecting Threats in Large Networks 3

Cyber Chronicles: Tracking Behavior Patterns for Detecting T...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Ramasubbu, Senthilnathan Thangavel, Senthil Kumar Jeyakumar, G. Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350372120

One of the primary challenges in cybersecurity is that even one un-detected, appropriately unanalyzed malicious security event can hide the attack vectors of a potential hacker. It is essential to detect the data breach at the earliest stage to reduce the impact on the business. The malicious actor's activities will have higher visibility during the initial attacks to compromises or exploitation at the beginning of the attack cycle. After the initial compromise, the attacker studies the environment and establishes the persistence and covert channel communication. At this successive phase of the attack cycle, the attacker camouflages with the production data flow, access, and noises in the network and application logs. It is essential to detect the attacks during the reconnaissance and initial attacks and should not miss any malicious activities. Most of our security tools focus on reducing false positives;however, reducing false negatives is critical to cyber security. Hence, this paper is focused on suitable deep learning to reduce the False Negatives (FNs) with minimal Type II error to detect the attacks well in advance. © 2024 IEEE.

关键词： Cybersecurity

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Identification and Acknowledgment of Programmed Traffic Sign Utilizing Profound Convolutional Neural Organization

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Intelligent Automation & Soft Computing 2023年第2期35卷 1527-1543页

作者： P.Vigneshwaran N.Prasath M.Islabudeen A.Arunand A.K.Sampath Department of Networking and Communications SRM Institute of Science and TechnologyKattankulathurChennai603203India Department of Computer Science and Engineering School of EngineeringPresidency UniversityBengaluru560064India

Trafﬁc signs are basic security workplaces making the rounds,which expects a huge part in coordinating busy time gridlock direct,ensuring the pros-perity of the road and dealing with the smooth segment of vehicles and indivi-duals by walking,*** a segment of the clever transportation structure,the acknowledgment of trafﬁc signs is basic for the driving assistance system,trafﬁc sign upkeep,self-administering driving,and various *** are different assessments turns out achieved for trafﬁc sign acknowledgment in the ***,most of the works are only for explicit arrangements of trafﬁc signs,for example,beyond what many would consider a possible ***ﬁc sign recognizable proof is generally seen as trying on account of various complexities,for example,extended establishments of trafﬁc sign *** critical issues exist during the time spent identiﬁcation(ID)and afﬁrmation of trafﬁc *** signs are occasionally blocked not entirely by various vehicles and various articles are accessible in busy time gridlock scenes which make the signed acknowledgment hard and walkers,various vehicles,constructions,and loads up may frustrate the ID structure by plans like that of road *** concealing information from trafﬁc scene pictures is affected by moving light achieved by environment conditions,time(day-night),and ***ﬁc sign revelation and afﬁrmation structure has two guideline sorts out:The essential stage incorpo-rates the trafﬁc sign limitation and the resulting stage portrays the perceived trafﬁc signs into a particular class.

关键词： Trafﬁc sign classiﬁer convolution neural network image vehicle

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Accurate and Efficient Code Matching Across Android Application Versions Against Obfuscation 31

Accurate and Efficient Code Matching Across Android Applicat...

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31st IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024

作者： Feng, Runhan Zhang, Zhuohao Zhou, Yetong Yan, Ziyang Zhang, Yuanyuan Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350330663

In an effort to enhance the attractiveness of apps, developers consistently and frequently release updates to introduce new features and address known issues. Although frequent updates are beneficial for improving user experience, they also increase the workload for reverse engineers since existing analysis results may become obsolete after the release of a new version. Matching code across app versions can help reverse engineers quickly migrate existing analysis results to new versions, verifying whether their prior findings still hold in the new version. This allows them to focus more on the modified portions of the code, thus increasing reverse engineering efficiency. Nevertheless, existing techniques cannot effectively match the code of apps protected by obfuscation techniques, which are pervasively adopted in prac-tice. To address the challenges introduced by code obfuscation, this study presents MatchScope, a novel automated approach designed to match code at the method level across versions of Android app binaries. MatchScope effectively leveraging different levels of fine-grained code features, including class structures and method opcodes, etc., for similarity comparison, thus achieving high accuracy. To further enhance the matching efficiency, we design an index-aware matching algorithm, significantly reducing the scope and number of pairwise comparisons required compared with existing work. The critical insight of our algorithm lies in that the obfuscation tools usually rely on an incrementing index to generate obfuscated names for classes in a deterministic way. Our evaluation on 20 open-source and 60 real-world apps demonstrates the effectiveness of MatchScope. The precision and recall of MatchScope on the ground truth achieve 97.49 % and 92.34 %, respectively, which are 19.50 % and 30.74 % higher than the state-of-the-art tool. © 2024 IEEE.

关键词： Reverse engineering

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Agri-Ontology: A Novel Ontology Framework for Agricultural Domain 3

Agri-Ontology: A Novel Ontology Framework for Agricultural D...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Deepa, R. Jayaraman, Venkatesh Srinivasulu, Senduru Umamageswari, B. Surendran, R. Srm Institute of Science & Technology School of Computing Department of Computing Technologies Kattankulathur Tamilnadu Chennai603203 India Chennai Institute of Technology Department of Computer Science and Engineering Chennai600069 India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai600119 India Srm Institute of Science & Technology School of Computing Department of Computing Technologies Chennai600119 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331509675

The agricultural information system deals with massive amounts of data from heterogeneous sources. It helps the farmers gain accurate information by providing better insights. A significant issue in agricultural data is its unstructured form, which can be resolved through ontology - a novel framework known as AgriOntology is proposed to address this issue for the Agrovoc dataset. The data in the unstructured form is initially pre-processed through the Natural Language Processing approach to generate features for ontology construction. The BERT and Jaccard similarity are applied to establish a relationship between the features used to construct the Agri-Ontology. Post ontology construction, the BiGAN framework is used to analyze the performance. The performance of the constructed ontology is observed to have an accuracy of 94.64%, which is higher than the existing models. © 2025 IEEE.

关键词： Data assimilation

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