Active Learning Based Adversary Evasion Attacks Defense for Malwares in the Internet of Things-Reference-Cited by-同舟云学术

Active Learning Based Adversary Evasion Attacks Defense for Malwares in the Internet of Things

Published:2023 Issue: Volume: Page:1-11
ISSN:1932-8184
Container-title:IEEE Systems Journal
language:
Short-container-title:IEEE Systems Journal

Author:

Ahmed Usman¹^ORCID,Lin Jerry Chun-Wei¹^ORCID,Srivastava Gautam²^ORCID,Jolfaei Alireza³^ORCID

Affiliation:

1. Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway

2. Department of Mathematics and Computer Science, Brandon University, Brandon, Canada

3. College of Science and Engineering, Flinders University, Tonsley, Australia

Funder

Western Norway University of Applied Sciences, Bergen, Norway

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering,Computer Networks and Communications,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/4267003/4357939/09976924.pdf?arnumber=9976924

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. TDBAMLA: Temporal and dynamic behavior analysis in Android malware using LSTM and attention mechanisms;Computer Standards & Interfaces;2025-03

2. A Novel Deep Reinforcement Learning (DRL) Method for Detecting Evasion Attack in Iot Environment;2024 International Conference on Integrated Circuits and Communication Systems (ICICACS);2024-02-23

3. Anomaly detection using deep convolutional generative adversarial networks in the internet of things;ISA Transactions;2023-12

4. MalHyStack: A hybrid stacked ensemble learning framework with feature engineering schemes for obfuscated malware analysis;Intelligent Systems with Applications;2023-11

5. Tdbamla: Temporal and Dynamic Behavior Analysis in Android Malware Using Lstm and Attention Mechanisms;2023