Full Report
We will tell you how we integrated a DLL Hijacking detection model into the Kaspersky SIEM platform and how it helped us uncover several incidents in their early stages.
Analysis Summary
The provided article describes how a machine-learning model within Kaspersky SIEM detects DLL-hijacking incidents. Since the context is focused on the *detection method* rather than a specific piece of malware or adversary tool, the summary will focus on the technique (DLL Hijacking) and the detection framework (Kaspersky ML Model/SIEM).
# Tool/Technique: DLL Hijacking Detection via Machine Learning
## Overview
This summary focuses on the technique of **DLL Hijacking** as observed in security incidents and the utilization of a **Machine Learning (ML) model** integrated within **Kaspersky SIEM** for its detection. DLL Hijacking is an execution technique where an adversary forces a legitimate application to load a malicious Dynamic Link Library (DLL) under its control.
## Technical Details
- Type: Technique (DLL Hijacking) / Detection Tool (Kaspersky ML model in SIEM)
- Platform: Primarily Windows operating systems (where DLL loading mechanisms are active)
- Capabilities: The ML model is designed to identify anomalous patterns indicative of DLL sideloading or path manipulation used in DLL Hijacking attacks.
- First Seen: DLL Hijacking is a well-established technique, but the specific ML model application date is not provided in the context.
## MITRE ATT&CK Mapping
- **TA0005 - Defense Evasion**
- **T1574 - Hijack Execution Flow**
- **T1574.001 - DLL Side-Loading** (The primary technique being detected)
## Functionality
### Core Capabilities
- **Detection of Anomalous Loading:** Identifying legitimate applications loading dynamic libraries from unexpected or suspicious locations (e.g., non-standard search paths).
- **Behavioral Analysis:** Utilizing historical data and established baselines to flag deviations indicative of a DLL search order manipulation or poisoning.
### Advanced Features
- **Machine Learning Application:** The core advanced feature is the use of ML to analyze system events (like process creation, file access, and DLL loading logs) to construct predictive models that differentiate normal behavior from malicious hijacking attempts.
## Indicators of Compromise
Since the context discusses detection methodology rather than a specific malware sample, specific IoCs for an exploit are not detailed.
- File Hashes: N/A (Focus is on process behavior)
- File Names: Focuses on the path and relationship of the loaded DLL relative to the host executable.
- Registry Keys: N/A
- Network Indicators: N/A
- Behavioral Indicators:
- An application loading a DLL from a directory that is not the application's primary location or a standard system directory.
- Anomalous modifications to environment variables or application configuration files that dictate DLL search order.
## Associated Threat Actors
The context does not name specific threat actors using DLL Hijacking for this specific detection analysis, though this technique is commonly employed across various APT groups and common malware families for persistence and evasion.
## Detection Methods
- Signature-based detection: Limited effectiveness against novel DLL payloads or zero-day side-loading patterns.
- **Behavioral detection:** The primary method described, relying on the ML model to score system events against malicious patterns.
- YARA rules: N/A (ML is signature-less)
## Mitigation Strategies
- **Application Hardening:** Ensuring applications use validated paths (e.g., using absolute paths) when loading required modules to prevent path-based manipulation.
- **Principle of Least Privilege:** Restricting write access to directories where legitimate applications reside to prevent malware from dropping malicious DLLs there.
- **System Monitoring:** Implementing robust audit logging and SIEM integration to capture process and DLL load events for analysis.
- **Security Software:** Utilizing endpoint protection that monitors process injection and anomalous module loading.
## Related Tools/Techniques
- DLL Sideloading (Core Technique)
- Process Hollowing (Related execution technique)
- Process Injection (Related initial access/execution technique)