论文标题
部分可观测时空混沌系统的无模型预测
Detecting Anomalous LAN Activities under Differential Privacy
论文作者
论文摘要
异常检测已成为一种流行技术,用于检测当地网络(LANS)中的恶意活动。 LAN异常检测的各个方面已经广泛研究。尽管如此,在先前的工作中尚未对个人用户或其在LAN中的关系的隐私问题进行探讨。在某些现实情况下,需要由位于LAN外部的外部方进行异常检测分析。因此,LAN管理员以私人方式向该方发布LAN数据以保护LAN用户的隐私很重要。同时,发布的数据还必须保留能够检测异常的实用性。本文研究了私下释放ARP数据的可能性,这些数据后来可用于识别LAN中的异常情况。我们提出了四种方法,并表明它们满足了不同级别的差异隐私 - 这是量化系统中隐私损失的严格概念。我们的现实实验结果证实了我们方法的实际可行性。有了适当的隐私预算,我们所有的方法都可以保留75%以上的效用,以检测发布的数据中的异常情况。
Anomaly detection has emerged as a popular technique for detecting malicious activities in local area networks (LANs). Various aspects of LAN anomaly detection have been widely studied. Nonetheless, the privacy concern about individual users or their relationship in LAN has not been thoroughly explored in the prior work. In some realistic cases, the anomaly detection analysis needs to be carried out by an external party, located outside the LAN. Thus, it is important for the LAN admin to release LAN data to this party in a private way in order to protect privacy of LAN users; at the same time, the released data must also preserve the utility of being able to detect anomalies. This paper investigates the possibility of privately releasing ARP data that can later be used to identify anomalies in LAN. We present four approaches and show that they satisfy different levels of differential privacy - a rigorous and provable notion for quantifying privacy loss in a system. Our real-world experimental results confirm practical feasibility of our approaches. With a proper privacy budget, all of our approaches preserve more than 75% utility of detecting anomalies in the released data.