Service Processor Web Attack Pattern Learning
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Solution Overview
Problem
Current web security systems are inadequate in dynamically learning and adapting to new attack patterns, leading to vulnerabilities that can result in significant financial losses and data breaches, as they rely on manual detection methods and are prone to being bypassed by hackers.
Innovation Solution
A dynamic security apparatus and algorithm that utilizes a service processor with a security monitoring module to learn attack vectors on web artifacts, perform selective scanning, and correlate attack patterns with access logs, enabling proactive detection and remediation of threats without exposing proprietary code to third parties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If offline scanning services are used to protect against attacks, then detection capability is improved, but code security deteriorates due to requiring code exposure to third parties
Solution Approach 1:
The patent introduces an intermediary scanning service that operates without requiring direct code exposure. The service uses heuristic analysis and pattern matching on compiled artifacts (bytecode, JavaScript) rather than requiring access to source code, acting as a mediator between security scanning and code protection needs.
Solution Approach 2:
The patent replaces traditional mechanical code scanning methods with intelligent analysis techniques. Instead of simple pattern matching on source code, the system uses heuristic evaluation, behavioral analysis, and machine learning models to detect vulnerabilities in compiled artifacts, eliminating the need for third-party code access.
2Measurement precision
If traditional security scanning is used, then known attack patterns can be detected, but adaptability to new attack patterns deteriorates
Solution Approach 1:
The patent implements a dynamic scanning system that continuously adapts to new attack patterns. The service uses machine learning models that are retrained on new vulnerability data, and employs heuristic analysis that evolves based on emerging threats, allowing the system to remain effective against both known and novel attacks.
Solution Approach 2:
The system incorporates feedback mechanisms where detection results and new vulnerability information are fed back into the analysis models. This continuous learning loop allows the scanning service to improve its detection capabilities over time and adapt to emerging attack vectors without requiring complete system reconfiguration.
3Reliability
If comprehensive code scanning is performed, then security coverage is improved, but processing time deteriorates
Solution Approach 1:
The patent applies partial scanning by focusing analysis on critical sections of code and high-risk artifacts. Instead of exhaustively scanning entire codebases, the service prioritizes scanning of authentication modules, data handling functions, and other high-value targets, achieving effective security coverage with reduced processing time.
Solution Approach 2:
The system applies different scanning intensities to different parts of the codebase based on risk assessment. Critical sections receive thorough analysis while less sensitive areas receive lighter scanning, optimizing the balance between security coverage and processing efficiency through localized quality adjustment.
Data Source
AI summary
A server includes a service processor hosted by a baseboard management controller (BMC) and independent of a CPU of the server. The server hosts web files. The service processor performs a set of monitoring tasks including receiving packets forming access requests made to the web files. A learning block is updated with information about suspicious packets. A counter is updated indicating a number of times a packet with a signature of a suspicious packet was received. When the counter reaches a threshold, a suspicious packet is analyzed in conjunction with other previously received suspicious packets. The analysis includes rearranging an arrival order of the suspicious packets into a new arrival sequence. The new arrival sequence of suspicious packets is matched to attack patterns in an attack pattern database. When the new arrival sequence matches an attack pattern, source IP addresses associated with the suspicious packets are added to a blacklist.