Intrusion Detection System using Semi-Supervised Learning with Adversarial Auto-encoder
Intrusion Detection System using Semi-Supervised Learning with Adversarial Auto-encoder
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DOI：
10.1109/noms47738.2020.9110343
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发表时间：
2019-02
期刊：
影响因子：
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通讯作者：
Kazuki Hara;K. Shiomoto
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文献类型：
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作者：
Kazuki Hara;K. Shiomoto
随着计算机网络越来越容易受到入侵者的攻击，入侵检测系统(入侵检测系统)是监控计算机网络活动并将其分类为正常或异常的关键组件。机器学习的显著进步促使我们考虑使用有监督的机器学习来构建入侵检测系统。有监督的机器学习需要大量的训练数据，导致昂贵的人力操作；它需要人类操作员检查数据，对它们进行分类，并用标签对它们进行标注。为了解决这个问题，我们提出了一种采用半监督学习的入侵检测系统。半监督学习利用训练数据集中少量的标记数据来减少昂贵的人工任务，并利用训练数据集中未标记数据的支持来提高性能。该方法采用对抗性自动编码器(AAE)，这是一种将生成性对抗性网络(GAN)结合到自动编码器(AE)中的半监督学习算法。我们使用NSL-KDD数据集对该方法的有效性进行了评估。我们证实，所提出的方法只使用0.1%的标签数据，获得了与现有使用机器学习方法的入侵检测系统相当的性能。
As computer networks become vulnerable to attacks from intruders, Intrusion Detection Systems (IDS) is a critical component that monitors activities of computer networks and classifies them as either normal or anomalous. Remarkable advancement of machine learning makes us consider to use supervised machine learning to build IDS. Supervised machine learning requires a large amount of training data, leading to costly human-labor operation; it requires the human operator to examine data, classify them, and annotate them with a label. To address this issue, we propose an IDS that employs semi-supervised learning. Semi-supervised learning uses a small number of labeled data in training dataset to reduce costly human-labor tasks and improves the performance with support of unlabeled data in training dataset. The proposed method employs Adversarial Auto-encoder (AAE), a semi-supervised learning algorithm that incorporates the Generative Adversarial Nets (GAN) into the Auto-encoder (AE). We evaluate the effectiveness of the proposed method using NSL-KDD dataset. We confirm that the proposed method that uses only 0.1 percent of labeled data achieves comparable performance with existing IDSs that use machine learning methods.