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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Vyshnavi, Chellu Raj, Chukka Mishal Gopakumar, Gokul Prasanna, Guddeti Bindu Belwal, Meena Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

In software development, system integrity is a measure of the impact code changes have on them. It is determined by the team's comprehension. However, rapid evolution of change commits and interaction in complex code bases create problems. By evaluating the effect of reflection changes, this study contributes to the improvement of the comprehension of code, a critical success factor for quality and efficiency. This addresses issues by using semantic analysis, change detection, refactoring detection, and semantic change detection and diffing. Our approach documents the semantic differences between the original and changed source code and gives pragmatic conclusions on how to enhance code quality and maintainability. Specifically, we divide the code into AST files using simple python scripts for semantic analysis and compare them for differences to find implications. It executes the method through the focus on development workflows and major projects as it includes the past information while still simplifying argument parsing and file processing. Based on our study findings, a better understanding of code can help developers to improve their decision-making capacity and protect the performance during the enhancement activity of the program. © 2024 IEEE.

关键词： Abstract Syntax Trees (ASTs) change detection refactoring detection semantic analysis semantic changes

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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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Empowering data structure visualization through modification in Python 15

Empowering data structure visualization through modification...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Puvvadi, Harsha Vardhan Angala, Hari Prasad Swaroop, Bandi Vishnu Reddy, Busa Thanuj Sathwik Belwal, Meena Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

This article introduces a novel approach to data structure visualization through the development of a new programming language, utilizing Python's Lex-YACC library for lexical analysis and parsing, and the Turtle module for graphics in the backend. The proposed language enables users to visualize various data structures such as arrays, stacks, and queues, along with their operations, facilitating a deeper understanding of these fundamental concepts in computer science. By combining the power of a custom scripting language with Python's versatile libraries, this research aims to empower users to interactively explore and comprehend complex data structures through intuitive visualizations. © 2024 IEEE.

关键词： Python

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Effortless learning: leveraging summarization and tabulation for automated note making

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Multimedia Tools and Applications 2024年 1-26页

作者： Arivarasi, A. Srinivasan, R. Rajeswari, D. Govindasamy, Alagiri School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603203 India Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603203 India Future Connect Technologies Pvt. Ltd Tamil Nadu Kanchipuram600034 India

In this fast-paced Digital Age, Natural Language Processing (NLP) can prove beneficial in consuming quality information efficiently. With the ever-growing number of learning resources, it is becoming onerous for students to choose the best-suited learning resource. Furthermore, many students find it difficult to understand and retain highly factual information. To overcome these issues, this paper proposes a novel Automated TExt Summarization of scholastic text in Table format (Auto-TEST) to provide information at a glance by mimicking the student’s style of note-making. The Auto-TEST framework is achieved through two steps which is Summarization and Tabulation. Initially, the Extractive Single Document Text Summarization (ESDTS) method is adopted to identify the salient information from the non-fictional (scholastic) text which is extracted and grouped together to form a concise summary. A Named Entity Recognition (NER) method is implemented to extract noun chunks and then arrange them in a tabulation format that aids in memorizing essential facts and figures by enabling effortless retrieval. The performance of the Auto-TEST framework is gauged by using scholastic domain-specific dataset using ROUGE as an evaluation metric. The extracted entities gives an average Recall of 71.40%, an average Precision score of 75.80% and an average F-score of 73.54% for the dataset respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Students

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Depth Map Prediction of Occluded Objects Using Structure Tensor with Gain Regularization

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Intelligent Automation & Soft computing 2023年第7期37卷 1145-1161页

作者： H.Shalma P.Selvaraj Department of Computing Technologies Faculty of Engineering and TechnologySchool of ComputingSRM Institute of Science and TechnologyKattankulathurChennai603203Tamil NaduIndia

The creation of the 3D rendering model involves the prediction of an accurate depth map for the input images.A proposed approach of a modified semi-global block matching algorithm with variable window size and the gradient assessment of objects predicts the depth map.3D modeling and view synthesis algorithms could effectively handle the obtained disparity *** work uses the consistency check method to find an accurate depth map for identifying occluded *** prediction of the disparity map by semi-global block matching has used the benchmark dataset of Middlebury stereo for *** improved depth map quality within a reasonable process-ing time outperforms the other existing depth map prediction *** experimental results have shown that the proposed depth map predictioncould identify the inter-object boundaryeven with the presence ofocclusion with less detection error and *** observed that the Middlebury stereo dataset has very few images with occluded objects,which made the attainment of gain *** this gain,we have created our dataset with occlu-sion using the structured lighting *** proposed regularization term as an optimization process in the graph cut algorithm handles occlusion for different smoothing *** experimented results demonstrated that our dataset had outperformed the Tsukuba dataset regarding the percentage of occluded pixels.

关键词： Depth maps occlusion detection reconstruction refined disparity map

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Blockchain-based Decentralized Digital Forensics Case Management System using IPFS 3

Blockchain-based Decentralized Digital Forensics Case Manage...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Viswanathan, Jayaraj Kumar, S. Udhaya Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350354348

Blockchain technology has gained significant attention for its ability to provide a decentralized and immutable platform for various applications. In this paper, we propose a Blockchain based Decentralized Case Management System using the InterPlanetary File System (IPFS) protocol. The decentralized nature of blockchain ensures data integrity and transparency, while IPFS facilitates efficient and decentralized storage of case files. Our system allows for secure and fine-grained access control to case data, ensuring that only authorized users can view and manage cases. We present the architecture of the system, which includes smart contracts deployed on the blockchain to manage case data and access control rules. Additionally, we have discussed the implementation details and use case scenarios to demonstrate the functionality of the proposed system. Performance testing revealed that our system significantly enhances data integrity and transparency. Comparative analysis demonstrated that IPFS, despite having slightly higher latencies than traditional FTP, provides superior security and redundancy. Through our research, we aim to provide a secure, transparent, and efficient solution for managing cases in various domains, including legal, healthcare, and finance. © 2024 IEEE.

关键词： Data integrity

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Contactless interaction recognition and interactor detection in multi-person scenes

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Frontiers of Computer science 2024年第5期18卷 83-95页

作者： Jiacheng LI Ruize HAN Wei FENG Haomin YAN Song WANG College of Intelligence and Computing Tianjin UniversityTianjin 300350China Department of Computer Science and Engineering University of South CarolinaColumbia SC 29208USA

Human interaction recognition is an essential task in video *** current works on human interaction recognition mainly focus on the scenarios only containing the close-contact interactive subjects without other *** this paper,we handle more practical but more challenging scenarios where interactive subjects are contactless and other subjects not involved in the interactions of interest are also present in the *** address this problem,we propose an Interactive Relation Embedding Network(IRE-Net)to simultaneously identify the subjects involved in the interaction and recognize their interaction *** a new problem,we also build a new dataset with annotations and metrics for performance *** results on this datasesthow significant improvements of the proposed method when compared with current methodsdeveloped for human interaction recognition and group activity recognition.

关键词： human-human interaction recognition multiper-son scene contactless interaction human relation modeling

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Face Recognition CCTV for Automated Attendance Tracking

Face Recognition CCTV for Automated Attendance Tracking

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2025 IEEE International Students' Conference on Electrical, Electronics and Computer science, SCEECS 2025

作者： Kola, Sai Sindhu Kenchi, Harsha Rama, P. Angayarkanni, V. Kalaivani, J. Sivakumar, S. Srm Institute of Science and Technology Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India Srm Institute of Science and Technology College of Engineering and Technology Faculty of Engineering and Technology Department of Computing Technologies TN Kattankulathur603203 India Srm Institute of Science and Technology Computing TechnologiesCollege of Engineering and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Computing Technologies Kattankulathur Chennai603203 India Faculty of Engineering and Technology Srmist Department of Computing Technologies Kattankulathur India

ISBN: (纸本)9798331529833

This study investigates the design and execution of an automated attendance tracking system using facial recognition CCTV based. Facial recognition technology and CCTV cameras are integrated in this system to provide a seamless, frictionless way to track and record attendance in real time. In addition to boosting attendance record accuracy, the approach reduces the administrative burden associated with using traditional methods. The suggested system is designed to recognise people when they enter or leave a monitored area by leveraging developments in computer vision. It accomplishes this automatically and requires no interaction from humans. Furthermore, the system's use of the Internet of Things makes it flexible and scalable to a variety of business situations, including offices, schools, and large-scale events. This is made feasible via real-time data synchronising and reporting. © 2025 IEEE.

关键词： Face recognition

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A Survey on Deep Learning Techniques for Secure and Accurate Mobile Sink Position Prediction in Vehicular Pattern WSN 8

A Survey on Deep Learning Techniques for Secure and Accurate...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Shareef, D.K. Jyothsna, V. School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India School of Computing Department of Data Science Andhra Pradesh Tirupati India

ISBN: (纸本)9798350386578

This research study presents a comprehensive survey of deep learning methods applied in order to improve the security along with accuracy of the mobile sink position prediction in Vehicular Pattern Wireless Sensor Networks (V-WSNs). V-WSNs are pivotal in modern wireless communication, especially in dynamic environments like urban traffic systems. The survey begins with an overview of V-WSNs, highlighting their unique characteristics, applications, and challenges, with a particular focus on mobile sink position prediction. It then delves into the nuances of routing protocols in V-WSNs, discussing the advantages and limitations of existing methods, and underscores the necessity for deep learning-based solutions. The paper further explores the integration of deep learning in WSNs, examining its applications, benefits, and inherent challenges. The significance of accurate and secure routing is analyzed, followed by an in-depth discussion on the integration of deep learning for enhancing these aspects in V-WSN routing. The paper also addresses the current challenges and open issues in mobile sink prediction and secure, accurate routing, paving the way for future research directions. A comparative analysis of various deep learning approaches and their practical applications in real-world scenarios is presented, offering insights into the effectiveness of these techniques. The survey concludes by summarizing the key findings and reaffirming the importance of integrating deep learning for secure and accurate mobile sink position prediction in V-WSNs. © 2024 IEEE.

关键词： Wireless sensor networks

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NetProbe: Deep Learning-Driven DDoS Detection with a Two-Tiered Mitigation Strategy 25

NetProbe: Deep Learning-Driven DDoS Detection with a Two-Tie...

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26th International Conference on Distributed computing and Networking, ICDCN 2025

作者： Jha, Prianshu Singh, Gurpreet Kumar, Amit Agrawal, Dewesh Patel, Yashwant Singh Forough, Javad Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Computing Science Umea University Umeå Sweden

ISBN: (纸本)9798400710629

Web servers are the backbone of modern Internet infrastructure, serving as the primary medium for online information distribution. Despite their critical role, web servers are susceptible to cyber-attacks. While current firewall mechanisms provide some level of protection against cyber threats, the evolving nature of these attacks and emerging vulnerabilities continue to pose significant risks. One of the most prevalent yet lethal attacks known today is DDoS (Distributed Denial of Service) attacks. These growing risks emphasize the urgent need for dynamic and robust threat detection and mitigation systems. This paper presents a comparative analysis of ensemble learning models (e.g., Random Forest, XGBoost, and LightGBM) and neural network-based models (e.g., Graph Neural Networks (GNN), Long Short-Term Memory networks (LSTM) with attention layers, and Gated Recurrent Units (GRU)) for DDoS attack detection and classification. Based on this analysis, we propose a real-time DDoS attack detection system integrated with a mitigation mechanism. The proposed system utilizes a two-tiered mitigation strategy assisted by UFW (Uncomplicated Firewall) and Apache server configuration files to block the incoming and outgoing traffic associated with suspicious IP addresses. The system's overall complexity, integrating both detection and response processes, ensures its efficiency in real-time environments while handling large volumes of traffic. Furthermore, the proposed approach achieves 15% improvement in detection accuracy and 20% reduction in false positives compared to traditional techniques, making it an effective and scalable solution for modern web server security. © 2025 Copyright held by the owner/author(s).

关键词： Decision trees

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