Information Security: 27th International Conference, ISC 2024, Arlington, VA, USA, October 23–25, 2024,...

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Nicky Mouha Nick Nikiforakis

Information Security

27th International Conference, ISC 2024, Arlington, VA, USA, October 23–25, 2024, Proceedings,...

Nicky Mouha, Nick Nikiforakis

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English

Springer International Publishing AG
24 October 2024

Coding theory & cryptology; Computer hardware; Computer security; Network security

Series: Lecture Notes in Computer Science

The two volume set LNCS 15257 + 15258 constitutes the proceedings of the 27th International Conference on Information Security, ISC 2024, held in Arlington, VA, USA, during October 23–25, 2024.

The 33 full papers presented in these proceedings were carefully reviewed and selected from 111 submissions. The papers are organized in the following topical sections:

Part I - Blockchain; Symmetric-Key Cryptography; Machine Learning; Software Security; Multi-Party Computation; Post-Quantum Cryptography; System Security.

Part II - Web Security; Intrusion Detection.

Edited by: Nicky Mouha, Nick Nikiforakis
Imprint: Springer International Publishing AG
Country of Publication: Switzerland
Edition: 2024 ed.
Volume: 15258
Dimensions: Height: 235mm, Width: 155mm,
ISBN: 9783031757631
ISBN 10: 3031757637
Series: Lecture Notes in Computer Science
Pages: 342
Publication Date: 24 October 2024
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

.- Web Security. .- JSHint: Revealing API Usage to Improve Detection of Malicious JavaScript. .- Automated Generation of Behavioral Signatures for Malicious Web Campaigns. .- We have Phishing at Home: Quantitative Study on Email Phishing Susceptibility in Private Contexts. .- Intrusion Detection. .- TIDL-IDS: A Time-series Imaging and Deep Learning-based IDS for Connected Autonomous Vehicles. .- FedMADE: Robust Federated Learning for Intrusion Detection in IoT Networks Using a Dynamic Aggregation Method. .- MVDetector: Malicious vehicles detection under Sybil attacks in VANETs. .- Insider Threat Detection Based on User and Entity Behavior Analysis with a Hybrid Model.