User Behavior Profiling using Ensemble Approach for Insider Threat Detection

Malvika Singh, B. Mehtre, S. Sangeetha
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引用次数: 13

Abstract

The greatest threat towards securing the organization and its assets are no longer the attackers attacking beyond the network walls of the organization but the insiders present within the organization with malicious intent. Existing approaches helps to monitor, detect and prevent any malicious activities within an organization’s network while ignoring the human behavior impact on security. In this paper we have focused on user behavior profiling approach to monitor and analyze user behavior action sequence to detect insider threats. We present an ensemble hybrid machine learning approach using Multi State Long Short Term Memory (MSLSTM) and Convolution Neural Networks (CNN) based time series anomaly detection to detect the additive outliers in the behavior patterns based on their spatial-temporal behavior features. We find that using Multistate LSTM is better than basic single state LSTM. The proposed method with Multistate LSTM can successfully detect the insider threats providing the AUC of 0.9042 on train data and AUC of 0.9047 on test data when trained with publically available dataset for insider threats.
基于集成方法的内部威胁检测用户行为分析
确保组织及其资产安全的最大威胁不再是攻击者在组织的网络墙之外进行攻击,而是组织内部存在恶意意图的内部人员。现有的方法有助于监视、检测和防止组织网络中的任何恶意活动,而忽略了人类行为对安全性的影响。本文主要研究用户行为分析方法,对用户行为动作序列进行监控和分析,以检测内部威胁。本文提出了一种基于多状态长短期记忆(MSLSTM)和卷积神经网络(CNN)的时间序列异常检测的集成混合机器学习方法,根据其时空行为特征检测行为模式中的附加异常值。我们发现使用多状态LSTM比使用基本的单状态LSTM效果更好。使用公开的内部威胁数据集进行训练时,训练数据的AUC为0.9042,测试数据的AUC为0.9047,采用Multistate LSTM方法可以成功检测内部威胁。
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