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E-grāmata: Applications and Techniques in Information Security: 14th International Conference, ATIS 2024, Tamil Nadu, India, November 22-24, 2024, Proceedings

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This book constitutes the refereed proceedings of the 14th International Conference, on Applications and Techniques in Information Security, ATIS 2024, held in Tamil Nadu, India, November 22-24, 2024.





The 24 full papers presented were carefully reviewed and selected from 149 submissions. The conference focuses on Advancing Quantum Computing and Cryptography; AI-Driven Cybersecurity: The Role of Machine Learning; Advancing Cybersecurity with Deep Learning Techniques; and Securing Connected Systems: IoT, Cloud, and Web Security Strategies.
.- Security of Emerging Technologies in Computer Networks.



.- Advancing Quantum Computing and Cryptography.



.- Optical Neural Networks A Strategy for Secure Quantum Computing.



.- Guarding Against Quantum Threats: A Survey of Post-Quantum Cryptography
Standardization, Techniques, and Current Implementations.



.- Cryptographic Distinguishers through Deep Learning for Lightweight Block
Ciphers.



.- Detection and Mitigation of Email Phishing.



.- Securing Digital Forensic Data Using Neural Networks, Elephant Herd
Optimization and Complex Sequence Techniques.



.- Design of Image Encryption Technique Using MSE Approach.



.- Low Latency Binary Edward Curve Crypto processor for FPGA platforms.



.- Augmenting Security in Edge Devices: FPGA-Based Enhanced LEA Algorithm
with S-Box and Chaotic Functions.



.- AI-Driven Cybersecurity: The Role of Machine Learning.



.- Machine Learning Approach for Malware Detection Using Malware Memory
Analysis Data.



.- DDOS Attack Detection in Virtual Machine Using Machine Learning
Algorithms.



.- An Unsupervised Method for Intrusion Detection using Novel Percentage
Split Clustering.



.- HATT-MLPNN: A Hybrid Approach for Cyber-Attack Detection in Industrial
Control Systems Using MLPNN and Attention Mechanisms.



.- Silent Threats: Monitoring Insider Risks in Healthcare Sector.



.- Advancing Cybersecurity with Deep Learning Techniques.



.- Enhanced Deep Learning for IIoT Threat Intelligence: Revealing Advanced
Persistent Threat Attack Patterns.



.- Adaptive Data-Driven LSTM Model for Sensor Drift Detection in Water
Utilities.



.- Enhancing FGSM Attacks with Genetic Algorithms for Robust Adversarial
Examples in Remote Sensing Image Classification Systems.



.- GAN-Enhanced Multiclass Malware Classification with Deep Convolutional
Networks.



.- Securing Connected Systems: IoT, Cloud, and Web Security Strategies.



.- IOT Based Locker Access System with MFA Remote Authentication.



.- A Secure Authentication Scheme between Edge Devices using HyperGraph
Hashing Technique in IoT Environment.



.- Enhancing Access Control and Information Sharing in Cloud IoT with an
Effective Blockchain-Based Authority System.



.- Securing Data in MongoDB: A Framework Using Encryption.



.- Handling Sensitive Medical Data A Differential Privacy enabled Federated
Learning Approach.



.- Securing your Web Applications: The Power of Bugbite Vulnerability Scanner.
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