ML-Based Threat Detection for Information Systems
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Solution Overview
Problem
Existing information security systems are unable to detect network attacks in a timely manner, allowing malicious activities such as data exfiltration and malware uploads to occur, as they lack proactive measures to identify potential bad actors and vulnerabilities.
Innovation Solution
The system employs machine learning models to analyze user activity information, identifying potential bad actors and network security threats, and executes corrective actions to prevent attacks by filtering user interactions and implementing protective measures before an attack occurs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing security systems are used, then network operations can proceed without proactive threat identification, but attacks are only detected after they occur allowing data exfiltration and malware uploads
Solution Approach 1:
The system performs preliminary actions by training machine learning models on historical user activity data before attacks occur. These models establish baseline behavior patterns and are ready to proactively identify deviations that indicate potential threats, enabling detection before data exfiltration or malware uploads happen
Solution Approach 2:
The system implements continuous feedback loops where user activity is constantly monitored, analyzed by machine learning models, and compared against established baselines. When anomalies are detected, the system provides feedback by generating alerts and can automatically respond by blocking suspicious activities, creating a closed-loop security system that learns and adapts over time
2Reliability
If machine learning models analyze all user activity information, then bad actors can be identified proactively, but system complexity and computational resources increase
Solution Approach 1:
The system segments the analysis process into distinct machine learning models with specific functions: one model analyzes user activity patterns to identify bad actors, another model assesses network security threats, and additional models evaluate specific risk factors. This segmentation allows each model to specialize in particular aspects of threat detection, improving accuracy while managing complexity through modular architecture
Solution Approach 2:
The system introduces machine learning models as intermediary components between raw user activity data and security decisions. These models act as mediators that process, interpret, and translate complex user behavior patterns into actionable security insights, reducing the complexity burden on the overall system while maintaining high detection accuracy
3Reliability
If corrective network security actions are executed to prevent attacks, then network security is improved, but network operations may be impacted by false positives
Solution Approach 1:
The system applies partial action by executing corrective security measures selectively rather than universally. Machine learning models evaluate each detected threat and determine the appropriate level of response, applying security actions only to confirmed threats while allowing normal operations to continue uninterrupted, thus maintaining network productivity while preventing attacks
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 input the user activity information into a first machine learning model that is configured to receive user activity information and to output a set of bad actor candidates based on the user activity information. 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 second machine learning model that 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.