基于改进CNN和LSTM的特征驱动入侵检测方法

Q3 Engineering

International Journal of Information and Computer Security Pub Date : 2023-01-01 DOI:10.1504/ijics.2023.10059327

Lin Guo, Jiawei Zhang, Yufei Zhao, Jing Zhang, Xiaoqin Zhang

引用次数: 0

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Feature-driven intrusion detection method based on improved CNN and LSTM

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来源期刊

International Journal of Information and Computer Security Engineering-Safety, Risk, Reliability and Quality

CiteScore

1.40

自引率

0.00%

发文量

期刊介绍： - Assurance and integrity of service. - Computer crime prevention/detection, computer forensics and security. - Confidentiality protection, cryptography and data protection. - Database and data security, denial of service protection. - E-commerce security, e-surveillance. - Fraud/hacker/terrorism detection/prevention, information warfare, national security. - Information ethics. - Information privacy issues, information systems/information security, sharing. - Internet abuse, network intruder prevention, internet/network security. - Malicious code/unauthorised access protection, transaction security, virus/worm controls. - Risk management, safety-critical systems. - Secure communications technology and computer systems. - Security control measures, policy models and mechanisms. - Software and hardware architectures. - Wireless/mobile network security.