HTTP Header Risk Detection via Scoring
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Solution Overview
Problem
The increasing volume of malware network communications, particularly those with malicious intent, poses a risk to servers and network equipment, interfering with valid communications and causing damage, as existing technologies are inadequate in effectively detecting and controlling such threats.
Innovation Solution
The method involves analyzing HTTP header information to identify potentially malicious communications by assigning scores based on characteristics such as user agent field length, number of header fields, and protocol version compliance, with a combined score compared to a threshold to determine the risk level and trigger actions like blocking or alerting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing detection technologies are used, then network communications can be monitored, but malware communications cannot be effectively detected and controlled
Solution Approach 1:
The HTTP header is segmented into multiple fields (e.g., User-Agent, Host, Content-Type, etc.), and each field is analyzed independently for malware indicators. This segmentation allows comprehensive detection of various malware communication patterns without overwhelming complexity, improving detection accuracy while managing the difficulty of detecting diverse malware variants.
Solution Approach 2:
The system changes detection parameters by analyzing multiple header field characteristics (length, presence, value patterns) rather than relying on a single parameter. By transforming the detection approach to examine multiple parameters simultaneously, the system achieves more reliable malware detection while addressing the inherent difficulty in detecting evolving malware techniques.
2Reliability
If HTTP header analysis is performed to detect malware, then detection capability is improved, but system complexity increases
Solution Approach 1:
The detection system segments the HTTP header analysis into discrete, manageable components (individual field analysis, value pattern matching, length verification). This segmentation reduces overall system complexity by breaking down the complex task of malware detection into simpler, independent analysis steps that can be processed sequentially.
Solution Approach 2:
The system employs self-service mechanisms where the analysis engine automatically evaluates header fields against predefined malware indicators and risk thresholds without requiring complex external processing. The automated scoring and risk assessment mechanisms simplify the detection process while maintaining high reliability, reducing the need for complex manual intervention or additional system resources.
3Measurement precision
If comprehensive header field analysis is conducted, then detection precision is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-defining malware indicators, risk thresholds, and analysis rules before actual communication processing occurs. This preliminary preparation allows the system to rapidly assess incoming HTTP headers against ready-made criteria, achieving high measurement precision for communication risk assessment while minimizing processing time during actual threat detection.
Solution Approach 2:
The system dynamically adjusts analysis parameters based on the specific header fields present in each communication. By changing the scope and depth of analysis parameters adaptively, the system maintains high precision for risk assessment while avoiding unnecessary processing time on fields that are not relevant to the current communication context, thus optimizing the balance between precision and processing speed.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture to detect risks using application protocol headers are disclosed. An example method includes extracting characteristics from a header of a received hypertext transport protocol (HTTP) request, determining a first score corresponding to a first characteristic of the characteristics, determining a second score corresponding to a second characteristic of the characteristics, adding the first score and the second score to determine a combined score, and indicating that the received HTTP request is malware when the combined score meets a threshold.