Heap Spray Attack Detection via Script Loop Analysis
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Solution Overview
Problem
Current solutions for preventing heap spray attacks are ineffective in detecting unknown malicious activities, as they rely on pattern-based signatures and hash tables that can be evaded by attackers using different code or programming techniques, limiting their ability to detect unknown attacks.
Innovation Solution
A method and system that detect heap spray attacks by analyzing scripts for loop operations that write data to memory, determining the size of the data written, and preventing the write if it exceeds a threshold, thereby identifying and blocking both known and unknown attacks without relying on specific vulnerability signatures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pattern-based signatures and hash tables are used to detect malware, then known attacks can be detected, but unknown attacks cannot be detected and can be easily evaded
Solution Approach 1:
The patent changes the detection parameter from static pattern matching to dynamic behavior analysis. Instead of checking for specific known malware patterns, the system monitors runtime parameters such as memory allocation patterns, loop iteration counts, and data write volumes to detect anomalous heap spray behaviors regardless of the specific malware variant.
Solution Approach 2:
The system performs preliminary detection of heap spray patterns before the actual attack executes. By analyzing the script structure and detecting loop operations that write large volumes of data to memory in advance, the system can block the attack before it compromises the application, rather than reacting after detection.
2Object-affected harmful factors
If heap spray attacks are prevented by blocking known patterns, then known attacks are stopped, but the system cannot adapt to new evasion techniques
Solution Approach 1:
The system implements feedback mechanisms that continuously monitor memory allocation patterns and adjust detection thresholds dynamically. When new heap spray patterns are detected, the system learns from these patterns and adjusts its detection parameters, creating a self-improving defense mechanism that adapts to new evasion techniques without requiring manual updates.
Solution Approach 2:
The patent creates a universal detection mechanism that identifies heap spray attacks based on fundamental behavioral characteristics rather than specific malware signatures. By detecting the underlying pattern of rapid memory allocation and large-volume data writes, the system can identify diverse attack variants using a single detection approach that works across different malware families and evasion techniques.
Data Source
AI summary
A method and system for protecting against unknown malicious activities by detecting a heap spray attack on a electronic device are disclosed. A script is received at an electronic device from a remote device via a network and a loop operation is detected in the script that contains a write operation operable to write data to a memory of the electronic device. The amount of the data operable to be written to the memory by the write operation is determined and the data is prevented from being written to the memory if the amount of the data is greater than or equal to a threshold.