Kernel Network Syscall Interception for Pre-Ransomware Data Leakage
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Solution Overview
Problem
Advanced ransomware variants that leak data before encrypting it pose challenges in detection and mitigation, as existing methods struggle to identify and halt such activities effectively.
Innovation Solution
Implementing a multi-tiered security system in the kernel space that intercepts and analyzes system calls from user applications to detect data leakage, preventing further data exposure and encryption by identifying and terminating malicious network calls originating from external IP addresses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional ransomware detection methods are used, then existing systems can identify basic encryption activities, but they fail to detect and halt data leakage before encryption occurs
Solution Approach 1:
The system performs preliminary analysis of system calls before encryption occurs. By intercepting and analyzing network syscalls in advance, the system can identify data leakage attempts prior to the encryption step, enabling preventive action rather than reactive detection after damage has occurred.
Solution Approach 2:
The detection system is divided into multiple analytical tiers that process different aspects of system calls sequentially. This segmentation allows comprehensive analysis of network calls, file access, and other indicators separately, making the detection of sophisticated ransomware behaviors more effective.
2Measurement precision
If a multi-tiered security system intercepts and analyzes all system calls, then data leakage detection accuracy improves, but system performance and processing speed deteriorate
Solution Approach 1:
The system divides the analysis into multiple tiers that handle different types of system calls at different depths. Not all calls require full analysis - the system can quickly dismiss benign calls and only perform deep analysis on suspicious ones, maintaining accuracy while managing processing load.
Solution Approach 2:
The system applies partial analysis to most system calls and reserved analysis only when necessary. By not over-analyzing every single call uniformly, the system maintains reasonable processing speeds while still achieving high detection accuracy for malicious activities.
3Reliability
If the system terminates network syscalls based on IP address analysis, then ransomware activity is halted effectively, but false positives may occur affecting legitimate operations
Solution Approach 1:
The system incorporates feedback mechanisms that allow it to learn from analyzed patterns and adjust its termination decisions. By continuously refining its analysis based on observed behavior patterns, the system can distinguish between legitimate external communications and malicious ransomware activities more accurately.
Solution Approach 2:
The system dynamically adjusts its analysis parameters and termination thresholds based on the context and behavior patterns observed. By changing its decision criteria adaptively rather than using fixed rules, the system reduces false positives while maintaining effective ransomware detection.
Data Source
AI summary
One example method includes intercepting a call issued by an application, at a first tier of an analytical structure, determining if the call is a networking call, when the call is determined to be a networking call, determining, at a second tier of the analytical structure, whether the networking call is requesting data, when the networking call is determined to be requesting data, determining at a third tier of the analytical structure, a classification of the data that is being requested, when the classification indicates that the data is not classified as public, or has not classified, determining, at a fourth tier of the analytical structure whether a destination IP address for the requested data is an internal IP address, and when the destination IP address is determined to be an external IP address, terminating the call.