Network Intrusion Detection Through File Hash Monitoring
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Solution Overview
Problem
Current network intrusion detection systems are inadequate in detecting and mitigating advanced and persistent cyber threats, leading to unnoticed intrusions that cause financial damage and undermine public trust, as they rely on incomplete security measures like Two Factor Authentication and Zero Trust.
Innovation Solution
A system and method involving file hash analysis on client nodes, comparing initial and subsequent hash analyses to detect intrusions, and generating notifications with potential mitigation actions, including autonomous responses via intrusion detection servers and mobile devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security measures like Two Factor Authentication and Zero Trust are used, then authentication security is improved, but intrusion detection capability deteriorates
Solution Approach 1:
The patent introduces file hash analysis as an intermediary mechanism between authentication and intrusion detection. By continuously monitoring file hashes and comparing them against baseline values, the system detects intrusions without compromising the authentication security provided by 2FA and Zero Trust. This intermediary layer enables detection of file modifications and unauthorized changes that authentication mechanisms alone cannot detect.
Solution Approach 2:
The intrusion detection system is segmented into multiple independent components: file monitoring modules, hash comparison engines, notification systems, and mitigation mechanisms. This segmentation allows each component to perform specific detection functions independently, improving overall detection capability without interfering with authentication processes. The segmented architecture enables specialized detection for different types of intrusions while maintaining the integrity of authentication security.
2Measurement precision
If continuous monitoring and analysis of network traffic is performed, then intrusion detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent extracts the intrusion detection function from complex network traffic analysis and focuses it on a specific, manageable task: monitoring file hash changes. By extracting the detection focus to file integrity monitoring, the system achieves high detection accuracy for intrusions while significantly reducing overall system complexity. This extraction allows the system to monitor only critical file changes rather than analyzing all network traffic, thereby improving precision without proportionally increasing complexity.
Solution Approach 2:
The system applies local quality by focusing monitoring resources on specific critical files and directories rather than uniformly monitoring all system components. The file hash analysis is concentrated on essential system files, configuration files, and application files that are most likely to be targeted by intrusions. This localized monitoring approach improves detection accuracy for critical intrusions while reducing the overall complexity compared to comprehensive system-wide monitoring.
3Speed
If rapid response and mitigation actions are implemented, then threat mitigation effectiveness is improved, but false positive rate increases
Solution Approach 1:
The patent implements preliminary action by establishing baseline file hashes and monitoring configurations in advance before intrusions occur. The system continuously compares current file hashes against these pre-established baselines, enabling rapid detection and response when changes are detected. This preliminary setup allows the system to respond quickly to actual intrusions while reducing false positives by having pre-defined baseline values for comparison, thereby improving both speed and reliability.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor file hash changes and provide real-time information about potential intrusions. When file modifications are detected, the system provides feedback through notifications and can automatically trigger mitigation actions. This feedback loop enables rapid response to actual threats while allowing the system to learn from patterns and reduce false positives over time through adaptive monitoring and analysis of detected changes.
Data Source
AI summary
Systems and methods for autonomous distributed cyber-secure networking and communication are provided. Detection and mitigation of anomalies is accomplished by detection at a granular level.