Probabilistic Signature Generation for Network Security
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Solution Overview
Problem
Conventional computer security technologies are limited by their reliance on periodic updates for virus definitions, making them ineffective against newly created or polymorphic malicious programs that can spread quickly across networks before updates can be distributed.
Innovation Solution
A probabilistic signature generation process that uses security interceptors to monitor and analyze processing operations within computer systems, generating signatures to identify and prevent malicious attacks without relying on continuous updates, allowing for real-time detection and dissemination of attack patterns across the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional virus detection software relies on periodic updates for virus definitions, then the security system can identify known viruses, but it becomes ineffective against newly created or polymorphic malicious programs that spread quickly across networks before updates can be distributed
Solution Approach 1:
The system performs preliminary analysis of program behavior and characteristics before malicious programs can spread widely. By establishing baseline profiles of legitimate system operations and comparing incoming programs against these profiles, the system proactively identifies potential threats without waiting for periodic virus definition updates.
Solution Approach 2:
The security system autonomously analyzes and classifies new malicious programs by examining their behavior patterns, code characteristics, and execution profiles. Instead of relying on external updates, the system self-generates threat intelligence by detecting anomalies and learning from observed malicious behavior, enabling real-time protection against novel threats.
2Reliability
If security systems implement comprehensive monitoring and analysis of all processing operations, then they can detect malicious attacks in real-time, but the system complexity and computational overhead increase significantly
Solution Approach 1:
The system applies different levels of monitoring intensity to different programs and processing operations based on their risk profiles. High-risk programs that exhibit suspicious behavior patterns undergo detailed behavioral analysis, while legitimate programs with established profiles receive minimal monitoring. This selective approach maintains high detection capability while reducing overall system complexity.
Solution Approach 2:
The system dynamically adjusts monitoring parameters such as sampling rates, analysis depth, and detection thresholds based on the current threat level and system conditions. When potential threats are detected, the system increases monitoring intensity for relevant programs; during normal operation, monitoring is reduced to essential functions, optimizing the balance between detection capability and system complexity.
Data Source
AI summary
A system receives information from at least one security interceptor associated with at least one computer system. The information identifies details associated with a traffic flow in a computer system of the computer networking environment. The system determines a probability that an attack on the computer system is in progress based on a probabilistic link provided by the information. The probabilistic link is determined by attack information associated with previous attacks. Based on the information provided by the at least one security interceptor, the system generates a signature utilized to prevent a similar attack on the computer system.