Network Stream Analyzer for Binary File Malicious Data Detection
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Solution Overview
Problem
Conventional security software is ineffective in detecting malicious data streams, such as ROP chains, within binary files before they reach the processing system, as it primarily focuses on post-execution analysis, leaving the system vulnerable to exploitation.
Innovation Solution
Implementing a network device with a screening controller and stream analyzer that pre-screens incoming files for suspicious patterns, including ROP chains, by analyzing metadata and binary data streams, and terminating malicious files before they are processed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional security software performs post-execution analysis, then the system can detect malicious behavior after it occurs, but the system remains vulnerable to exploitation before execution
Solution Approach 1:
The patent applies preliminary action by performing security analysis on binary files before they are executed. The system extracts and analyzes metadata, cross-sections, and data streams from the binary file in advance, detecting malicious ROP chains and exploit patterns prior to execution. This prevents the harmful effect of exploitation by identifying and blocking malicious content before it can cause damage, rather than relying on post-execution detection alone.
2Measurement precision
If the system analyzes all binary data streams for security threats, then detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent applies segmentation by dividing the binary file analysis into distinct segments: metadata extraction, cross-section analysis, and data stream examination. The system focuses analysis on specific suspicious patterns and structures rather than treating the entire file uniformly. This targeted segmentation approach maintains high detection accuracy for malicious ROP chains while reducing unnecessary processing time on benign files.
Solution Approach 2:
The patent applies local quality by directing analysis attention to specific local characteristics of the binary file that are most indicative of malicious content. The system examines local patterns such as return-oriented programming chains, exploit offsets, and specific data stream signatures rather than analyzing every byte uniformly. This localized examination strategy improves detection precision for critical security indicators while minimizing overall processing time.
3Reliability
If the system blocks all suspicious binary files, then security coverage improves, but legitimate functionality may be prevented
Solution Approach 1:
The patent applies feedback by implementing a nuanced decision-making process that continuously refines its blocking criteria based on analysis results. The system provides feedback about detected patterns, cross-sections, and data streams to adjust its detection thresholds and blocking decisions. This feedback mechanism allows the system to distinguish between legitimate files with benign patterns and actual malicious content, maintaining high security coverage while preserving the ability to process legitimate files that may trigger preliminary warnings.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed to detect an attack in an input file. An example apparatus includes a detection controller to identify a section of a number of bytes of data in a buffer including a first or second byte of data indicative of a value within a preconfigured range, the preconfigured range corresponding to a range of values indicative of memory addresses, update a merged list with a chunk of data that includes the section having the first or second byte of data indicative of the value within the preconfigured range, and a reoccurrence detector to concatenate the chunk of data in the merged list into a string to identify a number of occurrences the string matches remaining data in the buffer, and in response to a detection of the number of occurrences exceeding an occurrence threshold, determine that the data includes a malicious data stream.