Information Security: 27th International Conference, ISC 2024, Arlington, VA, USA, October 23–25, 2024, Proceedings, Part II
Author:   Nicky Mouha ,  Nick Nikiforakis
Publisher:   Springer International Publishing AG
Edition:   2024 ed.
Volume:   15258
ISBN:  

9783031757631


Pages:   342
Publication Date:   24 October 2024
Format:   Paperback
Availability:   Not yet available   Availability explained
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Information Security: 27th International Conference, ISC 2024, Arlington, VA, USA, October 23–25, 2024, Proceedings, Part II


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Overview

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.

Full Product Details

Author:   Nicky Mouha ,  Nick Nikiforakis
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   2024 ed.
Volume:   15258
ISBN:  

9783031757631


ISBN 10:   3031757637
Pages:   342
Publication Date:   24 October 2024
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Not yet available   Availability explained
This item is yet to be released. You can pre-order this item and we will dispatch it to you upon its release.

Table of Contents

.- 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.

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