RNN-VED for Reducing False Positive Alerts in Host-based Anomaly Detection Systems

Lydia Bouzar-Benlabiod, S. Rubin, Kahina Belaidi, Nour ElHouda Haddar
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引用次数: 6

Abstract

Host-based Intrusion Detection Systems HIDS are often based on anomaly detection. Several studies deal with anomaly detection by analyzing the system-call traces and get good detection rates but also a high rate off alse positives. In this paper, we propose a new anomaly detection approach applied on the system-call traces. The normal behavior learning is done using a Sequence to sequence model based on a Variational Encoder-Decoder (VED) architecture that integrates Recurrent Neural Networks (RNN) cells. We exploit the semantics behind the invoking order of system-calls that are then seen as sentences. A preprocessing phase is added to structure and optimize the model input-data representation. After the learning step, a one-class classification is run to categorize the sequences as normal or abnormal. The architecture may be used for predicting abnormal behaviors. The tests are achieved on the ADFA-LD dataset.
在基于主机的异常检测系统中减少误报的RNN-VED
基于主机的入侵检测系统通常是基于异常检测。一些研究通过分析系统调用轨迹来处理异常检测,得到了很好的检测率,但也有很高的误报率。在本文中,我们提出了一种新的应用于系统调用轨迹的异常检测方法。正常行为学习使用基于变分编码器-解码器(VED)架构的序列到序列模型,该模型集成了循环神经网络(RNN)细胞。我们利用系统调用调用顺序背后的语义,然后将其视为句子。在模型输入数据表示的结构和优化中增加了预处理阶段。在学习步骤之后,运行单类分类,将序列分类为正常或异常。该体系结构可用于预测异常行为。测试是在ADFA-LD数据集上实现的。
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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