Host-Based Intrusion Detection Model Using Siamese Network

Daekyeong Park

doi:10.1109/ACCESS.2021.3082160

2021-01-01

DOI：10.1109/ACCESS.2021.3082160

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摘要

En 中文

As cyberattacks become more intelligent, the difficulty increases for traditional intrusion detection systems to detect advanced attacks that deviate from previously stored patterns. To solve this problem, a deep learning-based intrusion detection system model has emerged that analyzes intelligent attack patterns through data learning. However, deep learning models have the disadvantage of having to re-learn each time a new cyberattack method emerges. The time required to learn a large amount of data is not efficient. In this paper, an experiment was conducted using the Leipzig Intrusion Detection Data Set (LID-DS), which is a host-based intrusion detection data set released in 2018. In addition, in order to evaluate and improve the performance of the system, a host-based intrusion detection model consisting of pre-processing, vector-to-image processing, training and testing steps is proposed. In the training and testing steps, a Siamese Convolutional Neural Network (Siamese-CNN) is constructed using the few-shot learning method, which shows excellent performance by learning a small amount of data. Siamese-CNN determines whether the attack type is the same based on the similarity score of each cyberattack sample converted to an image. The accuracy was calculated using the few-shot learning technique. The performance of the Vanilla Convolutional Neural Network (Vanilla-CNN) and Siamese-CNN are compared to confirm the performance of Siamese-CNN. As a result of measuring the accuracy, precision, recall, and F1-score indicators, it was confirmed that the recall of the Siamese-CNN model proposed in this study increased by about 6% compared to the Vanilla-CNN model.

Keyword:

Intrusion detection

Deep learning

Data models

Computer crime

Malware

Machine learning algorithms

Convolutional neural networks

Machine learning

LID-DS

few-shot learning

siamese network