MFT:新型内存流变换器高效入侵检测方法

IF 4.8 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Computers & Security Pub Date : 2024-10-22 DOI:10.1016/j.cose.2024.104174
Xuefeng Jiang , Liuquan Xu , Li Yu , Xianjin Fang
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引用次数: 0

摘要

入侵检测是网络安全研究的一个重要领域,致力于检测网络上的恶意流量或攻击。即使当今互联网环境不断进步,许多入侵检测技术仍然没有考虑到网络数据的长期特性,从而导致误报率很高。一些研究人员尝试使用传统的变压器模型来解决这一问题,但在处理大量连续数据的复杂关系和微妙分类要求时,这种方法并不十分有效。针对传统变压器模型的局限性,本研究提出了一种名为内存流变压器(MFT)的新颖解决方案。通过利用精心设计的内存流结构,MFT 超越了传统限制,使从网络流量中获取复杂的长期特征成为可能。这一创新使该模型能够在更精细的层次上识别各种网络流量数据之间的深层联系。为了验证 MFT 模型的有效性,我们在复杂的 CICIDS 2017 和 NSL-KDD 数据集上进行了广泛的实验。实验结果非常出色,证明了 MFT 强大的检测能力。在准确率、F1 分数、误报率和训练时间等性能指标方面,MFT 都优于目前最先进的方法。MFT 大大加强了网络安全,为入侵检测领域的从业人员提供了新颖有效的技术。
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MFT: A novel memory flow transformer efficient intrusion detection method
Intrusion detection is a critical field in network security research that is devoted to detecting malicious traffic or attacks on networks. Even with the advances in today's Internet environment, a lot of intrusion detection techniques still fail to take into account the long-term characteristics present in network data, which results in a high false alarm rate. Some researchers have tried to address this problem by using the traditional transformer model; however, it is not very effective when dealing with complex relationships and the subtle classification requirements of large amounts of sequential data. This work presents a novel solution called the memory flow transformer (MFT) in response to the limitations of the conventional transformer model. By utilizing a carefully designed memory flow structure, MFT transcends traditional limitations and makes it possible to obtain complex long-term features from network traffic. This innovation enables the model to identify deep connections at a finer level between a wide variety of network traffic data. Extensive experiments were carried out on the complex CICIDS 2017 and NSL-KDD datasets to validate the effectiveness of the MFT model. The results were outstanding, demonstrating MFT's powerful detection abilities. With regard to performance metrics like accuracy, F1 score, false alarm rate, and training time, MFT is superior to current state-of-the-art approaches. Network security is greatly strengthened by MFT, which provides practitioners in the intrusion detection field with novel and effective techniques.
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来源期刊
Computers & Security
Computers & Security 工程技术-计算机:信息系统
CiteScore
12.40
自引率
7.10%
发文量
365
审稿时长
10.7 months
期刊介绍: Computers & Security is the most respected technical journal in the IT security field. With its high-profile editorial board and informative regular features and columns, the journal is essential reading for IT security professionals around the world. Computers & Security provides you with a unique blend of leading edge research and sound practical management advice. It is aimed at the professional involved with computer security, audit, control and data integrity in all sectors - industry, commerce and academia. Recognized worldwide as THE primary source of reference for applied research and technical expertise it is your first step to fully secure systems.
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