Authors
Introduction
Our colleagues from the AI expertise center recently developed a machine-learning model that detects DLL-hijacking attacks. We then integrated this model into the Kaspersky Unified Monitoring and Analysis Platform SIEM system. In a separate article, our colleagues shared how the model had been created and what success they had achieved in lab environments. Here, we focus on how it operates within Kaspersky SIEM, the preparation steps taken before its release, and some real-world incidents it has already helped us uncover.
How the model works in Kaspersky SIEM
The model’s operation generally boils down to a step-by-step check of all DLL libraries loaded by processes in the system, followed by validation in the Kaspersky Security Network (KSN) cloud. This approach allows local attributes (path, process name, and file hashes) to be combined with a global knowledge base and behavioral indicators, which significantly improves detection quality and reduces the probability of false positives.
The model can run in one of two modes: on a correlator or on a collector. A correlator is a SIEM component that performs event analysis and correlation based on predefined rules or algorithms. If detection is configured on a correlator, the model checks events that have already triggered a rule. This reduces the volume of KSN queries and the model’s response time.
This is how it looks:
