Memory Scan for Heap Spray Attack Detection
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Solution Overview
Problem
Current computer systems are ineffective in detecting and mitigating code injection attacks, particularly heap spray attacks, which exploit heap memory to execute malicious code or cause denial-of-service, posing a significant threat to applications like web browsers and operating systems.
Innovation Solution
A computer system and method that scans memory sections for non-operational instructions, determines the presence of a heap spray attack by analyzing the number and spatial locality of no-ops, and takes defensive actions such as terminating the program or isolating affected sections, utilizing techniques like Hidden Markov Models to detect and mitigate such attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If heap memory is sprayed with no-ops to enable code execution redirection, then code injection capability is improved, but system security deteriorates
Solution Approach 1:
The system performs preliminary scanning of heap memory sections to detect no-op instructions before they can be executed as part of a code injection attack. By proactively identifying and isolating malicious no-op sequences, the system prevents the attack from succeeding while maintaining legitimate heap functionality.
Solution Approach 2:
The patent introduces an intermediary detection layer between the heap memory and the CPU execution pipeline. This intermediary scans memory sections for suspicious no-op patterns and can intercept malicious code redirection attempts, allowing legitimate heap operations to proceed while blocking security threats.
2Object-affected harmful factors
If heap memory is sprayed with large volume of no-ops, then denial-of-service effect is achieved, but system availability deteriorates
Solution Approach 1:
The system scans heap memory sections in advance to detect excessive no-op sequences that could indicate a denial-of-service attack. By identifying these patterns before they consume excessive CPU resources, the system can terminate the malicious process early, preventing the denial-of-service condition from materializing.
Solution Approach 2:
The patent implements a feedback mechanism that monitors heap memory usage and CPU execution patterns. When the system detects abnormal no-op sequences exceeding threshold values, it triggers corrective actions such as process termination or memory isolation, thereby preventing denial-of-service attacks while maintaining system availability.
3Measurement precision
If scanning is performed on all memory sections, then detection precision is improved, but processing time increases
Solution Approach 1:
The patent applies local quality by focusing scanning resources on specific heap memory sections that are most susceptible to code injection attacks, rather than uniformly scanning all memory. This targeted approach maintains high detection precision for critical areas while reducing overall processing time.
Solution Approach 2:
The system performs partial scanning of memory sections by analyzing only the portions most likely to contain malicious no-op sequences. By applying threshold-based filtering and scanning only suspicious regions, the system achieves adequate detection precision without the time cost of exhaustive full-memory scanning.
Data Source
AI summary
The present disclosure generally relates to computer security and malware protection. In particular, the present disclosure is generally directed towards systems and methods for detecting and mitigating a code injection attack. In one embodiment the systems and methods may detect a code injection attack by scanning identified sections of memory for non-operational machine instructions (“no-ops”), detecting a code injection attack based on the scan(s) and mitigating the code injection attack by taking one or more defensive actions.