Application Behavior Modeling for Automated Threat Prevention
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Solution Overview
Problem
Existing security systems face challenges in efficiently detecting and responding to emerging threats and vulnerabilities, particularly in large organizations, where manual vulnerability management is error-prone and time-consuming, and it is impossible to patch all vulnerabilities in time.
Innovation Solution
A method and arrangement that collects data on application behavior, builds a model of normal behavior, and creates a configuration to restrict or prevent operations if deviations are detected, using vulnerability information to manage vulnerabilities automatically and efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual vulnerability management is implemented, then security coverage can be increased, but time consumption and error rate increase significantly
Solution Approach 1:
The system enables automatic vulnerability management where the security system autonomously collects vulnerability information, builds behavior models, generates configurations, and applies restrictions without requiring continuous manual intervention. The system self-manages the entire vulnerability response lifecycle, eliminating the time-consuming and error-prone manual processes while maintaining comprehensive security coverage.
Solution Approach 2:
The system performs preliminary actions by proactively collecting vulnerability information and building behavior models before actual attacks occur. By pre-configuring security policies and establishing baseline behavior patterns in advance, the system is prepared to rapidly respond to vulnerabilities when they are exploited, eliminating the need for reactive manual management.
2Reliability
If all vulnerabilities are patched manually, then security protection is improved, but time and resource requirements become impossible to meet
Solution Approach 1:
The system automatically manages vulnerability responses by autonomously analyzing vulnerability information, comparing it against collected behavior data, and implementing appropriate restrictions. This self-service capability enables the system to handle multiple vulnerabilities simultaneously without the time and resource constraints that plague manual patching processes.
Solution Approach 2:
Instead of requiring complete patching of all vulnerabilities, the system applies partial action by selectively restricting vulnerable applications based on their behavior models. The system implements excessive action by monitoring and responding to vulnerabilities faster than traditional patching cycles, using behavior-based restrictions that take effect immediately rather than waiting for software updates.
3Measurement precision
If behavior monitoring and restriction is implemented, then threat detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system implements a universal behavior model that can be applied across multiple applications and vulnerability types. By creating a standardized framework for collecting behavior data, building models, and generating restrictions, the system achieves high threat detection accuracy without proportionally increasing complexity. The same core mechanisms serve multiple security functions.
Solution Approach 2:
The system creates simplified copies or representations of application behavior through behavior models that capture essential patterns without replicating the full complexity of actual application operations. These behavioral fingerprints enable accurate threat detection by comparing observed actions against modeled patterns, achieving high precision while maintaining manageable system complexity.
Data Source
AI summary
An arrangement (410) and a method, e.g. a computer implemented method, of threat prevention in a computer (101, 205a-205h) or computer network (201), wherein the method comprises collecting data related to the computer (101, 205a-205h) and/or computer network (201), the collected data relating at least to behavior of at least one application, building a model of normal behavior of the at least one application based on the collected data, requesting and/or receiving vulnerability information relating to the at least one application, building a configuration for the application, e.g. application control policy for the application, if the received vulnerability information indicates that the application has a vulnerability, wherein the built configuration restricts and/or prevents the operation of the application if a deviation is observed between the monitored behavior of the application and the built normal model of the application.


