Probabilistic Security Policy Reposturing for Network Threat Detection
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Solution Overview
Problem
Conventional computer security technologies rely on periodic updates of virus definitions, which makes them ineffective against newly created malicious programs that have not been identified yet, allowing these programs to infect computer systems despite the presence of security software.
Innovation Solution
A probabilistic security policy re-posturing process that uses security agents to monitor and learn from processing operations within computer systems, creating a graphical model to infer the likelihood of attacks and adjust security policies dynamically, allowing for real-time prevention of malicious activities without relying on external updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional security software uses periodic virus definition updates, then the security software can identify known malicious programs, but it becomes ineffective against newly created malicious programs that have not been identified yet
Solution Approach 1:
The system performs preliminary actions by establishing baseline security policies and monitoring processing operations before new threats emerge. Security agents continuously observe system behavior and create probabilistic models in advance, enabling the system to detect and respond to new malicious programs immediately when they appear, without waiting for periodic definition updates.
Solution Approach 2:
The security system serves itself by automatically learning from observed processing operations and dynamically adjusting security policies without external intervention. The probabilistic model continuously updates based on system behavior patterns, enabling the security software to autonomously identify and respond to new threats without requiring manual updates from security vendors.
2Reliability
If security agents monitor all processing operations to detect attacks, then the system can identify new threats in real-time, but the complexity of the security system increases
Solution Approach 1:
The system changes parameters by using probabilistic thresholds and confidence levels to filter and prioritize monitored events. Instead of treating all processing operations equally, the probabilistic model assigns different weights and significance levels to various events, allowing the system to focus computational resources on high-risk patterns while maintaining comprehensive monitoring capability.
Solution Approach 2:
The probabilistic security model acts as an intermediary layer between raw processing operations and security policy enforcement. This intermediary translates complex monitored events into probabilistic assessments, which then inform policy adjustments. This mediation simplifies the overall system architecture by providing a structured framework for analyzing and responding to diverse security events.
3Adaptability or versatility
If the security policy is adjusted dynamically based on observed attacks, then the system can adapt to new threats rapidly, but the security policy may become unstable or overly permissive
Solution Approach 1:
The system implements feedback mechanisms where security policy adjustments are continuously evaluated based on their effectiveness. The probabilistic model monitors whether policy changes result in improved threat detection or false positives, and automatically adjusts subsequent policy modifications accordingly. This feedback loop ensures that dynamic policy adjustment maintains stability while adapting to new threats.
Solution Approach 2:
The security policy transitions from static to dynamic, allowing flexible adaptation to new threats while maintaining structural integrity. The probabilistic model enables gradual, controlled policy evolution based on confidence levels and threat assessments, preventing abrupt or destabilizing changes while maintaining the ability to respond rapidly to emerging threats.
Data Source
AI summary
A system defines at least one key event to be monitored by at least one agent, and creates a graphical model for the at least one key event. The system observes the at least one key event. The system infers a degree of attack on the computer system based on an observation of the at least one key event in conjunction with a result of an effect the at least one key event has on the graphical model. The system then adjusts a security policy based on an output of the graphical model.