Exploring Efficiency of Character-level Convolution Neuron Network and Long Short Term Memory on Malicious URL Detection

Thuy Pham, Van-Nam Hoang, Thanh Ngoc Ha
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引用次数: 13

Abstract

Machine learning techniques, especially deep learning neuron networks have been increasingly applied to solve the problems relating to information security and cybersecurity. Malicious URL (Uniform Resource Locator) detection is one of these. It is considered as a binary classification in machine learning, in which a URL or website address is classed as malign or benign. In this work, we implement the experiments on two different datasets to explore the efficiency of three proposed character-level deep neuron networks: (1) CNN (Convolution Neuron Network) based on VGG-16 architecture (Visual Geometry Group), (2) LSTM (Long Short Term Memory), and a fusion of CNN and LSTM for malicious URL detection. The experimental results are promising, especially for the fusion scheme of LSTM and CNN, with above 96% for precision and 98% for recall.
字符级卷积神经元网络和长短期记忆在恶意URL检测中的效率探索
机器学习技术,特别是深度学习神经元网络已经越来越多地应用于解决与信息安全和网络安全有关的问题。恶意URL(统一资源定位器)检测就是其中之一。它被认为是机器学习中的二元分类,其中URL或网站地址被分类为恶性或良性。在这项工作中,我们在两个不同的数据集上进行了实验,以探索三种提出的字符级深度神经元网络的效率:(1)基于VGG-16架构(视觉几何组)的卷积神经元网络(CNN),(2)长短期记忆(LSTM),以及CNN和LSTM的融合用于恶意URL检测。实验结果令人满意,特别是LSTM与CNN的融合方案,准确率达到96%以上,召回率达到98%以上。
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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