IP-MCCLSTM: A Network Intrusion Detection Model Based on IP Filtering
Author
Abstract

The network intrusion detection system capably safeguards our network environment from attacks. Yet, the relentless surge in bandwidth and inherent constraints within these systems often hinder detection, particularly in confrontations with substantial traffic volume. Hence, this paper introduces the IP-filtered multi-channel convolutional neural networks (IP-MCCLSTM), which filters traffic by IP, curtails system loading, and notably enhances detection efficiency. IP-MCCLSTM outperforms comparison methods in tests using the 2017CICIDS data set. The result shows IPMCCLSTM obtains 98.9\% accuracy and 99.7\% Macro-Recall rate, showcasing its potential as an avant-garde solution in intrusion detection.

Year of Publication
2023
Date Published
dec
Publisher
IEEE
Conference Location
Chengdu, China
ISBN Number
9798350318982
URL
https://ieeexplore.ieee.org/document/10387114/
DOI
10.1109/ICCWAMTIP60502.2023.10387114
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