Ali Ahmadian Ramaki; Abbas Ghaemi-Bafghi; Abbas Rasoolzadegan
Abstract
Nowadays, targeted attacks like Advanced Persistent Threats (APTs) has become one of the major concern of many enterprise networks. As a common approach to counter these attacks, security staff deploy a variety of heterogeneous security and non-security sensors at different lines of defense (Network, ...
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Nowadays, targeted attacks like Advanced Persistent Threats (APTs) has become one of the major concern of many enterprise networks. As a common approach to counter these attacks, security staff deploy a variety of heterogeneous security and non-security sensors at different lines of defense (Network, Host, and Application) to track the attacker's behaviors during their kill chain. However, one of the drawbacks of this approach is the huge amount of events raised by heterogeneous sensors which makes it difficult to analyze logged events for later processing i.e. event correlation for timely detection of APT attacks. The main focus of the existing works is only on the degree to which the event volume is reduced, while the amount of security information lost during the event aggregation process is also very important. In this paper, we propose a three-phase event aggregation method to reduce the volume of heterogeneous events during APT attacks considering the lowest rate of loss of security information. To this aim, at first, low-level events of the sensors are clustered into some similar event groups and then, after filtering noisy event clusters, the remained clusters are summarized based on an Attribute-Oriented Induction (AOI) method in a controllable manner to reduce the unimportant or duplicated events. The method has been evaluated on the three publicly available datasets: SotM34, Bryant, and LANL. The experimental results show that the method is efficient enough in event aggregation and can reduce events volume up to 99.7 with an acceptable level of information loss ratio (ILR).