In-App Behavior Detection via Attack Pattern Induction
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Solution Overview
Problem
Traditional data security strategies are generally reactive, failing to detect novel attack vectors in a timely manner, leaving electronic resources exposed.
Innovation Solution
A logic-based attack pattern induction method using sub-exhaustive satisfiability tests to identify Boolean functions that detect unauthorized behavior by analyzing binary feature vectors and timing relationships, enabling proactive detection within applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional reactive detection strategies are used, then implementation simplicity is maintained, but detection speed and responsiveness to novel attacks deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-defining multiple attack patterns representing different attack vectors before actual attacks occur. These patterns are stored in a database and used for real-time comparison with incoming data, enabling rapid detection without complex real-time analysis of novel attack vectors.
Solution Approach 2:
The detection system segments attack detection into multiple independent attack patterns, each representing a specific attack vector. This segmentation allows the system to handle different attack types through dedicated patterns, improving detection speed while maintaining manageable system complexity through modular organization.
2Adaptability or versatility
If traditional detection strategies are used, then system simplicity is maintained, but adaptability to novel attack vectors deteriorates
Solution Approach 1:
The system achieves universality by creating a multi-functional detection framework where a single detection mechanism can identify multiple types of attacks through different pre-defined patterns. Each pattern represents a different attack vector, allowing the system to adapt to various attack types without requiring separate specialized systems for each threat.
3Reliability
If reactive detection is used, then resource consumption is minimized, but security reliability deteriorates due to exposure to novel attacks
Solution Approach 1:
The system performs preliminary analysis by pre-defining attack patterns and storing them in a database before actual attacks occur. This preliminary action enables the detection mechanism to quickly compare incoming data against known patterns, improving security reliability while minimizing real-time computational resource consumption.
Data Source
AI summary
Architectures and techniques for in-app behavior detection. A behavior detection agent within an application running on a hardware computing device captures events within the application. The events are inputs received from one or more sources external to the application. The behavior detection agent generates an event stream from the captured events. The behavior detection agent analyzes the event stream for significant feature frequencies and associations corresponding to one or more attack profiles. The behavior detection agent initiates an attack response in response to finding one or more significant feature frequencies and associations. The attack response comprises at least changing an operational configuration of the application.