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

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.

Table of Contents:
.- 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.