Interpretable Deep Learning for DDoS Defense: A SHAP-based Approach in Cloud Computing-Reference-Cited by-同舟云学术

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Interpretable Deep Learning for DDoS Defense: A SHAP-based Approach in Cloud Computing

Published:2024-06-28 Issue: Volume: Page:1-8
ISSN:
Container-title:2024 International Conference on Circuit, Systems and Communication (ICCSC)
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Author:

Ouhssini Mohamed¹,Afdel Karim¹,Akouhar Mohamed²,Agherrabi Elhafed³,Abarda Abdallah⁴

Affiliation:

1. University IBN Zohr,Lab SIV,Department of Computer Science,Agadir,Morocco

2. University IBN Tofail,Lab Partial Differential Equations, Algebra and Spectral Geometry,Kenitra,Morocco

3. University IBN Zohr,Lab IRF-SIC,Department of Computer Science,Agadir,Morocco

4. Hassan First University,Lab of Mathematical Modeling and Economic Calculations,Settat,Morocco

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10616390/10616394/10616654.pdf?arnumber=10616654

Reference18 articles.

1. Efficient Identification of DDoS Attacks on Cloud Computing Environment using Machine Learning Techniques

2. Designing ensemble deep learning intrusion detection system for DDoS attacks in software defined networks;Mbasuva

3. Explaining network intrusion detection system using explainable AI framework;Mane;arXiv.org,2021

4. An assessment of robustness for adversarial attacks and physical distortions on image classification using explainable AI. AI-Cybersec@SGAI;Mahima

5. Explainable AI in deep reinforcement learning models for power system emergency control;Zhang;IEEE Transactions on Computational Social Systems.,2022

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