Zero Day Attack Detection via Sequence Emulation
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Solution Overview
Problem
Current cybersecurity systems are ineffective in detecting zero-day attacks due to the lack of known vulnerability signatures, leading to incomplete protection against sophisticated attacks.
Innovation Solution
A system that monitors transaction requests and uses a sequence manager to identify anomalous requests, which are then verified by a sequence emulator before processing, with the option to deny or allow the requests based on verification, and updates a database for potential vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vulnerability signature-based detection is used, then detection accuracy for known vulnerabilities is improved, but detection capability for zero-day attacks deteriorates
Solution Approach 1:
The system performs preliminary actions by creating a baseline model of normal system behavior before attacks occur. This baseline captures typical transaction sequences, system states, and operational patterns. When anomalies are detected, the system can compare against this pre-established baseline to identify potential zero-day attacks without relying on known vulnerability signatures.
Solution Approach 2:
The patent replaces the traditional mechanical signature-matching system with a behavioral analysis system. Instead of mechanically comparing attack patterns against known vulnerability databases, the system uses machine learning models and anomaly detection algorithms to analyze system behavior dynamics, enabling detection of previously unseen attack patterns.
2Reliability
If whitelisting approach is used, then protection against some attacks is improved, but protection against sophisticated zero-day attacks deteriorates
Solution Approach 1:
The system inverts the traditional whitelisting approach by implementing a blacklist-based anomaly detection system. Instead of explicitly permitting only known-good actions (whitelisting), the system establishes a baseline of normal behavior and automatically identifies and blocks deviations from this baseline, thereby protecting against both known and unknown (zero-day) attacks without requiring explicit rules for each threat.
Solution Approach 2:
The system performs self-service by automatically adapting its detection rules based on observed system behavior. The machine learning models continuously learn from new data patterns, automatically updating the baseline behavior model without requiring manual rule updates or expert intervention for each new threat type.
3Measurement precision
If sequence emulation verification is implemented, then detection of anomalous transaction sequences is improved, but system processing speed deteriorates
Solution Approach 1:
The system applies partial action by implementing selective verification. Not all transaction sequences undergo full emulation verification - only those flagged as potentially anomalous by preliminary monitoring are subjected to detailed sequence emulation. This partial application of verification maintains processing speed for normal transactions while achieving high detection accuracy for suspicious sequences.
Solution Approach 2:
The system performs preliminary monitoring and anomaly detection before full sequence emulation verification. This preliminary action filters out normal transactions that don't require detailed verification, allowing the computationally intensive sequence emulation to be applied only when necessary, thereby maintaining overall system productivity.
Data Source
AI summary
The concepts and technologies disclosed herein are directed to zero day attack detection. A system can monitor, by a sequence manager, a sequence of transaction requests. The sequence manager can determine whether a transaction request in the sequence is anomalous. In response to determining that the transaction request is anomalous, and before the allowing the system to process the transaction request, the sequence manager can provide the sequence of transaction requests to a sequence emulator. The sequence emulator can attempt to verify an output of the sequence of transaction requests. The sequence manager can receive a notification from the sequence emulator. The notification can indicate whether the output of the sequence of transaction requests can be verified. In response, the sequence manager can instruct the system to deny (if the output cannot be verified) or allow (if the output can be verified) processing of the sequence of transaction requests.