Automated Activity Action System for Service Security
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Solution Overview
Problem
Existing systems lack the ability to automatically detect and respond to frequent activities involving resources, particularly distinguishing between desirable and undesirable activities, which can lead to inefficiencies and security breaches.
Innovation Solution
A device is configured to monitor user activities and identify frequent patterns, applying appropriate actions such as rewarding desirable activities and restricting or banning undesirable ones, using a system that records and evaluates user interactions to automatically enforce rules and defend against malicious patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual monitoring of user activities is performed, then security breaches can be detected, but the system requires significant human resources and time consumption
Solution Approach 1:
The system automatically monitors user activities, detects frequent patterns, and applies activity actions without human intervention. The device self-manages the security monitoring task by evaluating activity logs and automatically responding to detected patterns, eliminating the need for manual security personnel while maintaining continuous surveillance.
Solution Approach 2:
The patent replaces manual mechanical monitoring with an automated electronic system that uses processors to evaluate activity logs and detect patterns. The mechanical process of human review is substituted with automated computational analysis that can process activity data at machine speed without time constraints.
2Speed
If automated activity detection is implemented, then response time to security threats is improved, but the device complexity increases
Solution Approach 1:
The system segments the security monitoring function into distinct modular components: an activity log evaluator that processes logs, a pattern detector that identifies frequent activities, and an activity action applicator that executes responses. This modular architecture reduces overall system complexity by making each component independent and manageable while enabling fast automated response.
Solution Approach 2:
The patent introduces an activity log as an intermediary data structure that captures user activities in a standardized format. This intermediary layer simplifies the complexity by providing a uniform interface between the monitoring system and various activity types, allowing the processor to evaluate diverse activities through a consistent mechanism.
3Object-affected harmful factors
If frequent activities are automatically restricted, then undesirable patterns are mitigated, but legitimate user activities may be incorrectly blocked
Solution Approach 1:
The system implements feedback mechanisms where activity actions are applied based on detected patterns, and the results are continuously monitored. The activity log evaluator receives feedback from applied actions and adjusts pattern detection thresholds accordingly, allowing the system to learn from false positives and refine its discrimination between legitimate and undesirable activities over time.
Solution Approach 2:
The patent employs adjustable parameters in pattern detection, such as frequency thresholds and activity type weights. These parameters can be dynamically changed based on system performance and feedback, allowing the system to adapt its sensitivity and reduce false restrictions on legitimate activities while maintaining protection against undesirable patterns.
Data Source
AI summary
Activities of users of a service often involve one or more resources, such as uploading or downloading files in a file system of an FTP server. The activities of the users may be tracked and recorded in an activity log in order to identify frequently performed activities involving particular resources, and for such frequently performed activities, one or more activity actions may be performed. For example, malicious users may upload or utilize an equivalent set of assets stored in several accounts. The frequency of these undesirable activities may be identified, and an activity action may be automatically applied to the users (e.g., banning accounts), resources (e.g., deleting assets), and/or activities (e.g., blocking access to the resources). Conversely, desirable activities involving particular resources may be similarly detected, and the activity action applied to such desirable activities may involve reporting the desirable activity to an administrator of the service.