TL-CNN-IDS: transfer learning-based intrusion detection system using convolutional neural network-Reference-Cited by-同舟云学术

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TL-CNN-IDS: transfer learning-based intrusion detection system using convolutional neural network

Published:2023-05-08 Issue:15 Volume:79 Page:17562-17584
ISSN:0920-8542
Container-title:The Journal of Supercomputing
language:en
Short-container-title:J Supercomput

Author:

Yan Fengru,Zhang Guanghua,Zhang Dongwen,Sun Xinghua,Hou Botao,Yu Naiwen

Funder

the technical service project of State Grid Hebei Energy Technology Service Co., Ltd.

open project of Hebei Internet of Things Monitoring Engineering Technology Research Center

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Information Systems,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s11227-023-05347-4.pdf

Reference44 articles.

1. Yang L, Manias D M, Shami A (2021) PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams. In: IEEE Global Communications Conference (GLOBECOM), pp 01–06

2. Hosseini S, Nezhad AE, Seilani H (2022) Botnet detection using negative selection algorithm, convolution neural network and classification methods. Evol Syst 13(1):101–115

3. Alauthman M, Aslam N, Al-Kasassbeh M et al (2020) An efficient reinforcement learning-based Botnet detection approach. J Netw Comput Appl 150:102479

4. Obaidat I, Sridhar M, Pham KM et al (2022) Jadeite: a novel image-behavior-based approach for Java malware detection using deep learning. Comput Secur 113:102547

5. Kim TG, Kang BJ, Rho M et al (2018) A multimodal deep learning method for android malware detection using various features. IEEE Trans Inf Forensics Secur 14(3):773–788

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