Automated Signature Generation via Exploit Script Samples
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Solution Overview
Problem
Current network security systems face challenges in accurately distinguishing between innocuous and malicious network traffic, leading to the creation of ambiguous and variant-resistant signatures, which can result in false positives and negatives, and are often circumvented by rogue actors.
Innovation Solution
A system that generates synthetic samples of network traffic by executing exploit scripts on a host device to attempt unauthorized access to a target computing device, collecting and storing these samples, and then constructing regular expressions based on invariant tokens to create resilient and unambiguous signatures without relying on honey pots or external systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If signature detection systems use prior knowledge of malicious signals, then resource consumption is reduced, but accuracy is limited because the system cannot always correctly classify traffic or associate signals with malicious traffic
Solution Approach 1:
The system performs preliminary actions by executing exploit scripts against target devices to proactively generate malicious traffic samples before actual attacks occur. These samples are then used to create detection signatures, allowing the system to prepare detection patterns in advance rather than relying solely on prior knowledge databases, thereby improving classification accuracy while maintaining resource efficiency
Solution Approach 2:
The system serves itself by automatically generating malicious traffic samples through internal exploit script execution and autonomously creating detection signatures from these samples without requiring external security systems or honey pots. This self-service capability enables the system to improve its own detection accuracy while avoiding the resource costs of external infrastructure
2Adaptability or versatility
If conventional security systems combine signature detection with anomaly detection by forwarding malicious traffic to ASG systems, then signature generation capability is improved, but the systems create ambiguous signatures that confuse innocuous traffic with malicious traffic
Solution Approach 1:
The system extracts only the essential malicious characteristics from exploit script executions by identifying and isolating invariant tokens that consistently appear across multiple exploit variants. By extracting these core invariant features and excluding variable, non-essential elements, the system generates precise signatures that accurately distinguish malicious traffic from innocuous traffic, resolving the ambiguity problem
Solution Approach 2:
The system segments traffic analysis into distinct components by separating invariant tokens (consistent malicious indicators) from variable portions (changing exploit parameters). This segmentation allows the system to create signatures based solely on the invariant malicious characteristics, improving classification accuracy by preventing confusion with variable innocuous traffic patterns
3Measurement precision
If honey pots are used to attract and study malicious activity, then traffic qualification accuracy is improved, but rogue actors can identify and circumvent the security system
Solution Approach 1:
Instead of using physical honey pot systems that can be compromised, the system creates virtual copies of malicious traffic patterns by executing exploit scripts and capturing the resulting network traffic. These synthetic traffic copies preserve the malicious characteristics needed for accurate detection while eliminating the security risks associated with physical honey pots, as the copies cannot be circumvented by rogue actors
Solution Approach 2:
The system introduces an intermediary process that executes exploit scripts in a controlled environment to generate malicious traffic samples, which then serve as the basis for signature creation. This intermediary approach allows accurate traffic qualification without exposing the system to direct attacks from rogue actors, as the exploit execution occurs in isolation and the resulting signatures are used for detection rather than attraction
4Extent of automation
If ASG systems identify signatures from network traffic, then automated signature generation is achieved, but the signatures are noisy, prone to false positives or false negatives, and inefficient
Solution Approach 1:
The system performs preliminary filtering and analysis of exploit script outputs before signature generation by identifying and extracting invariant tokens that consistently appear across multiple exploit executions. This preliminary action cleans the raw traffic data of noise and variability, ensuring that only consistent, reliable malicious characteristics are used in signature creation, thereby improving signature accuracy while maintaining automation
Solution Approach 2:
The system changes the parameters of signature generation by focusing exclusively on invariant tokens rather than attempting to capture all traffic characteristics. By adjusting the signature creation parameters to prioritize consistency and invariance over completeness, the system generates more accurate and reliable signatures that reduce false positives and negatives while remaining automated
Data Source
AI summary
Systems and methods are provided for generating samples of network traffic and characterizing the samples to easily identify exploits. A first embodiment of the present disclosure can generate traffic between a sample generator and the target computing device based on a particular exploit. The traffic can be a plurality of samples of the exploit using an exploit script. The method can provide for collecting and storing the plurality of samples. These samples can then be used to characterize the exploit by identifying invariant portions and variable portions of the samples. The method can further provide for removing any artifacts from the samples. Regular expressions can be constructed based on the samples. Each regular expression can be tested and ranked according to metrics of efficiency and accuracy.