Pattern-Based Zero-Day Vulnerability Detection System
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Solution Overview
Problem
Existing technologies struggle to accurately detect and mitigate software attacks exploiting zero-day vulnerabilities, such as Spring4Shell, without relying on corrective software patches, and often result in high false positive and false negative rates.
Innovation Solution
A method and system that detect an initial stage of an attack pattern by identifying specific initial identifiers in a computer server's activity log, followed by a search window to detect subsequent identifiers indicating remote code execution attempts, allowing for timely mitigation of the attack without requiring a patch.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional detection methods are used to identify software attacks, then the system can detect known vulnerabilities, but it fails to detect zero-day vulnerabilities like Spring4Shell without patches
Solution Approach 1:
The system performs preliminary actions by establishing attack patterns and identifier sequences before actual zero-day attacks occur. By pre-defining what constitutes suspicious activity sequences (e.g., specific log entry patterns, temporal relationships between events), the system can detect novel attacks without needing prior knowledge of their exact implementation details
Solution Approach 2:
The detection approach segments the attack detection problem into identifying individual identifiers and their sequences within log data. Rather than attempting to detect complete attack patterns as single units, the system breaks down attacks into detectable identifier components that can be identified and correlated independently
2Reliability
If the system increases detection sensitivity to catch more attacks, then more attacks are detected, but the number of false positives increases significantly
Solution Approach 1:
The system pre-establishes sequences of identifiers that must occur in specific temporal relationships to constitute a detected attack. By requiring multiple identifiers to appear in a defined sequence within a search window, the system raises the threshold for detection in a controlled manner that reduces false positives while maintaining sensitivity to actual attacks
Solution Approach 2:
The system uses feedback mechanisms where detected identifier sequences are evaluated against established patterns, and detection thresholds are adjusted based on the correlation strength between observed sequences and known attack patterns. This feedback loop allows the system to maintain high detection rates while filtering out random coincidences that would otherwise generate false positives
3Measurement precision
If the system requires multiple identifiers to be detected in sequence, then false positives are reduced, but detection response time increases
Solution Approach 1:
The system dynamically adjusts the search window parameters and identifier sequence requirements based on the specific attack pattern being detected. For time-critical attacks, the system can reduce the number of required identifiers or shorten the search window duration, while for less critical threats, it can enforce stricter sequence requirements. This dynamic configuration allows the system to balance false positive reduction with response time requirements
4Reliability
If the system implements comprehensive attack pattern detection, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The system implements a universal detection framework that uses the same identifier sequence analysis mechanism for detecting multiple different types of attacks, including zero-day vulnerabilities. Rather than implementing separate detection systems for each attack type, a single multi-functional framework analyzes log data for pattern sequences that are characteristic of various attack methodologies, reducing overall system complexity while maintaining comprehensive detection capability
Data Source
AI summary
A method implemented on an electronic device detects and mitigates software attacks exploiting a zero-day vulnerability, such as Spring4Shell, against a computer server. At least one initial identifier, which indicates an attempt to extract a predetermined type of information on the computer server, is detected within an activity log of the computer server to thereby indicate occurrence of an initial stage. Then, prior to elapsing of a search window having a predetermined time duration, at least one subsequent identifier, which indicates an attempt to perform remote code execution on the computer server, is detected within the activity log of the computer server. Then, a source of network activity associated with the detected identifiers is detected, and a security response associated with the identified source of network activity is performed to mitigate the exploitation of the vulnerability.