Semantic Model Threat Detection for Structured Data Storage
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Solution Overview
Problem
Current systems fail to effectively detect and mitigate unauthorized access and SQL injection attacks in structured data storage systems, leading to security breaches and performance issues due to high false-positive threat indications and the difficulty in identifying subtle security gaps.
Innovation Solution
A system and method that capture network traffic, assemble packets into messages, generate a semantic model, and use scoring algorithms to identify potentially threatening operations, allowing for real-time detection and mitigation of breaches by analyzing traffic patterns and behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional security systems are used to detect threats, then security monitoring is provided, but high false-positive rates occur and subtle security gaps cannot be identified
Solution Approach 1:
The system segments network traffic analysis into multiple specialized components: packet capture and reassembly, semantic model generation, behavioral pattern recognition, and scoring algorithms. Each component focuses on specific aspects of traffic analysis, improving overall detection precision while reducing false positives through divided expertise.
Solution Approach 2:
The system introduces an intermediary semantic model that sits between raw network packets and threat detection. This semantic model translates low-level packet data into high-level operational meanings, enabling more accurate threat identification and reducing false alarms by understanding the true intent of traffic patterns.
2Difficulty of detecting and measuring
If comprehensive traffic analysis is performed to identify security breaches, then detection capability is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-assembling packets into complete messages and pre-generating semantic models before actual threat detection occurs. This preparation work simplifies the subsequent detection process, reducing real-time complexity while maintaining comprehensive analysis capabilities.
Solution Approach 2:
The system implements self-service through automated semantic model generation and behavioral pattern recognition. The system autonomously learns normal traffic patterns and identifies deviations without requiring complex manual configuration, reducing operational complexity while enhancing detection capability.
3Loss of time
If real-time threat detection is implemented, then security response time is improved, but processing overhead increases
Solution Approach 1:
The system applies partial action by focusing processing resources on the most critical aspects of traffic analysis: reassembling packets into messages, generating semantic models, and evaluating threats using scoring algorithms. This selective approach provides real-time detection capability while managing processing overhead by not analyzing every packet in exhaustive detail.
Data Source
AI summary
Systems, methods, and computer-readable media for detecting threats on a network. In an embodiment, target network traffic being transmitted between two or more hosts is captured. The target network traffic comprises a plurality of packets, which are assembled into one or more messages. The assembled message(s) may be parsed to generate a semantic model of the target network traffic. The semantic model may comprise representation(s) of operation(s) or event(s) represented by the message(s). Score(s) for the operation(s) or event(s) may be generated using a plurality of scoring algorithms, and potential threats among the operation(s) or event(s) may be identified using the score(s).