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| Titolo: |
Applications and Techniques in Information Security : 14th International Conference, ATIS 2024, Tamil Nadu, India, November 22-24, 2024, Proceedings / / edited by V.S. Shankar Sriram, Anila Glory H., Gang Li, Shiva Raj Pokhrel
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| Pubblicazione: | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025 |
| Edizione: | 1st ed. 2025. |
| Descrizione fisica: | 1 online resource (XIII, 342 p. 192 illus., 108 illus. in color.) |
| Disciplina: | 005.8 |
| Soggetto topico: | Data protection |
| Artificial intelligence | |
| Computer networks | |
| Education - Data processing | |
| Computer engineering | |
| Data and Information Security | |
| Artificial Intelligence | |
| Computer Communication Networks | |
| Computers and Education | |
| Computer Engineering and Networks | |
| Persona (resp. second.): | Shankar SriramV.S |
| HAnila Glory | |
| LiGang | |
| PokhrelShiva Raj | |
| Nota di contenuto: | -- 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. |
| Sommario/riassunto: | 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. |
| Titolo autorizzato: | Applications and Techniques in Information Security ![]() |
| ISBN: | 981-9797-43-8 |
| Formato: | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 9910983301303321 |
| Lo trovi qui: | Univ. Federico II |
| Opac: | Controlla la disponibilità qui |