A Deep Learning Methodology to Detect Trojaned AI-based DDoS Defend Model-Reference-Cited by-同舟云学术

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A Deep Learning Methodology to Detect Trojaned AI-based DDoS Defend Model

Published:2022-02-18 Issue: Volume: Page:
ISSN:
Container-title:2022 8th International Conference on Automation, Robotics and Applications (ICARA)
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Author:

Chen Yen-Hung¹,Lai Yuan-Cheng²,Lu Cho-Hsun³,Huang Yu-Ching²,Chang Shun-Chieh²,Jan Pi-Tzong⁴

Affiliation:

1. National Taipei University of Nursing and Health Sciences,Department of Information Management,Taipei,Taiwan

2. National Taiwan University of Science and Technology,Department of Information Management,Taipei,Taiwan

3. Fo Guang University,Department of Applied Informatics,Yilan,Taiwan

4. Shih Hsin University,Department of Business Administration,Taipei,Taiwan

Funder

Ministry of Science and Technology

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9738547/9738518/09738571.pdf?arnumber=9738571

Reference10 articles.

1. Fast target link flooding attack detection scheme by analyzing traceroute packets flow

2. Towards mitigating Link Flooding Attack via incremental SDN deployment;lei;2016 IEEE Symposium on Computers and Communication (ISCC),2016

3. An AI Driven Risk Assess Framework to Evaluate DDoS Cyberattack;chen;Journal of Intelligent and Fuzzy Systems,2021

4. Detecting and Mitigating Target Link-Flooding Attacks Using SDN;wang;IEEE Transactions on Dependable and Secure Computing,2018

5. Traceroute-based target link flooding attack detection scheme by analyzing hop count to the destination

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