Expert System Automating Vulnerability Risk Management
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Solution Overview
Problem
Conventional IT security practices face challenges in accurately and efficiently testing and auditing systems for emerging IT security vulnerabilities, often resulting in laborious processes, duplication of efforts, and high costs due to false positives and disagreements among security experts.
Innovation Solution
A cloud-based system employing an expert system that automates the life cycle of network auditing and vulnerability risk management using a Vulnerability Management Expert Decision System (VMEDS) module, which imports scan results, converts them into facts, builds a rule set, and applies the Rete algorithm to verify vulnerabilities, reducing false positives and refining the list of vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional vulnerability scanners are used to identify security vulnerabilities, then a list of potential vulnerabilities is generated, but the results contain a large number of false positives and require laborious human review
Solution Approach 1:
An expert system acts as an intermediary between the vulnerability scanner and human security experts. The scanner first identifies potential vulnerabilities, then the expert system automatically analyzes and verifies them using predefined rules and algorithms, filtering out false positives before presenting results to human experts. This intermediary processing layer resolves the contradiction by maintaining high productivity while improving accuracy through automated verification.
Solution Approach 2:
The system enables self-service vulnerability verification through automated analysis engines that independently evaluate scanner results. The expert system automatically cross-references vulnerability data with security databases, system configurations, and contextual information to verify authenticity without requiring immediate human intervention. This self-service capability maintains high productivity while reducing false positives through automated validation.
2Measurement precision
If human security experts are employed to review and verify vulnerabilities, then accuracy improves, but labor costs and time consumption increase significantly
Solution Approach 1:
The vulnerability verification process is segmented into multiple stages: initial scanner detection, automated expert system analysis, and selective human expert review. Only vulnerabilities that pass through the automated analysis stage are presented to human experts, dividing the workload and reducing overall review time while maintaining accuracy through multi-layered verification.
Solution Approach 2:
The expert system performs preliminary verification actions automatically before human experts review vulnerabilities. It pre-analyzes scanner results, cross-references with security databases, and filters out obvious false positives in advance. This preliminary action reduces the time human experts need to spend on verification while maintaining high accuracy through pre-filtering.
3Reliability
If multiple security experts review vulnerabilities independently, then comprehensive analysis is achieved, but disagreements and duplication of efforts increase
Solution Approach 1:
Multiple expert analyses are merged into a unified expert system that applies consistent rules and algorithms to all vulnerability assessments. The system combines the analytical capabilities of multiple experts into a single automated framework, eliminating disagreements caused by individual interpretations while reducing duplication through centralized analysis. This merging maintains comprehensive analysis through rule-based thoroughness while simplifying the process.
4Adaptability or versatility
If conventional manual vulnerability management processes are used, then flexibility in handling complex cases is maintained, but total cost of ownership increases due to labor requirements
Solution Approach 1:
The system changes the operational parameters of vulnerability management by transitioning from manual human analysis to automated expert system processing. This parameter change reduces costs associated with human labor while maintaining adaptability through configurable rules and algorithms that can be adjusted to handle complex cases. The automated system processes vulnerabilities at lower cost while retaining flexibility through programmable logic.
Data Source
AI summary
An instance of a vulnerability risk management (VRM) module and a vulnerability management expert decision system (VMEDS) module are instantiated in a cloud. The VMEDS module imports scan results from a VRM vulnerability database and saves them as vulnerabilities to be reviewed in a VMEDS database. The VMEDS module converts vulnerabilities into facts. The VMEDS module builds a rule set in the knowledge base to verify whether certain vulnerabilities are false positives. Rules related to a vulnerability are received in plain English from a web-based front-end application. The VMEDS module tests each rule against all of the facts using the Rete algorithm. The VMEDS module executes the action associated with the rule derived from the Rete algorithm. The VMEDS module stores the results associated with the executing of the action in the VMEDS database and forwards the results to the VRM module.