ML Threat Detection Using User Activity Filtering
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Solution Overview
Problem
Existing network security systems are unable to detect network attacks in a timely manner, allowing malicious activities such as data exfiltration and malware uploads to occur before they can be prevented, due to their reactive nature.
Innovation Solution
A machine learning-based system that proactively identifies potential bad actors and network security threats by analyzing user activity information, using multiple machine learning models to filter and classify user interactions, and executing corrective actions to mitigate threats before an attack occurs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing network security systems are used, then they can monitor network activity, but they cannot detect attacks in a timely manner allowing malicious activities to occur
Solution Approach 1:
The system performs preliminary analysis of user behavior patterns and establishes baseline profiles before attacks occur. By continuously monitoring and learning normal user activities in advance, the system can quickly identify deviations that indicate potential threats, eliminating the need for reactive detection after an attack has already happened.
Solution Approach 2:
The security system transitions from static rule-based detection to dynamic adaptive monitoring that continuously adjusts to changing user behaviors and threat patterns. The system dynamically updates user profiles and detection thresholds based on real-time data, enabling it to adapt to new attack vectors while maintaining accurate threat detection.
2Reliability
If multiple machine learning models are used to analyze user activity, then threat detection capability is improved, but system complexity increases
Solution Approach 1:
The complex security analysis task is segmented into multiple specialized machine learning models, each responsible for specific aspects such as user behavior analysis, anomaly detection, and threat classification. This segmentation allows each model to focus on particular patterns, improving overall detection accuracy while making the system more manageable through modular architecture.
Solution Approach 2:
The machine learning models are designed with universal capabilities to handle multiple types of threats and user behaviors through a common framework. By creating multi-functional models that can adapt to different threat scenarios, the system reduces overall complexity compared to having separate specialized models for each threat type.
3Reliability
If proactive threat identification is implemented, then network security is improved, but computational resources and processing time are increased
Solution Approach 1:
The system applies partial monitoring and analysis to user activities, focusing computational resources on high-risk behaviors and users with elevated threat profiles. Rather than analyzing every single user action in depth, the system selectively intensifies monitoring based on risk indicators, reducing overall computational load while maintaining security effectiveness.
Solution Approach 2:
The security analysis applies different levels of scrutiny to different users and activities based on their risk profiles. High-value targets and suspicious behaviors receive intensive analysis with multiple machine learning models, while normal low-risk activities receive minimal processing. This local differentiation of analysis quality optimizes resource allocation.
Data Source
AI summary
A device that is configured to receive user activity information that includes information about user interactions with a network device for a plurality of users. The device is further configured to receive a set of bad actor candidates that identifies one or more users from among the plurality of users. The device is further configured to filter the user activity information based on the set of bad actor candidates. The device is further configured to input the filtered user activity information into a machine learning model. The machine learning model is configured to receive the filtered user activity information and to output system exposure information that identifies network security threats. The device is further configured to identify network security actions based on the network security threats and to execute the network security actions.