ML-Based Security Policy Generation for Network Isolation
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Solution Overview
Problem
Identifying and addressing malicious behavior in networks is time-consuming and resource-intensive, often resulting in false positives and negatives, and existing techniques fail to detect targeted network attacks effectively.
Innovation Solution
A security platform that uses a machine learning model to process traffic, endpoint device information, and network device information to generate a security policy, isolating malicious behavior by implementing security rules that prevent compromised devices from accessing the network and other devices, thereby reducing false indications and conserving resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods are used to identify malicious behavior, then detection capability is provided, but the process is time-consuming and resource-intensive
Solution Approach 1:
The system performs preliminary actions by proactively monitoring network traffic and endpoint behaviors before malicious activities fully manifest. The machine learning model continuously analyzes patterns and triggers automated responses at early stages, reducing the time required to identify and respond to malicious behavior compared to traditional reactive methods.
Solution Approach 2:
The system implements self-service through automated machine learning-based detection and response mechanisms that operate without continuous human intervention. The automated response system independently analyzes traffic patterns, identifies anomalies, and executes remediation actions, significantly reducing the time and resources required compared to manual security operations.
2Reliability
If traditional security measures are implemented, then basic protection is provided, but false positives and negatives occur and targeted attacks are not detected
Solution Approach 1:
The system changes the parameters of detection by using machine learning models that continuously adapt to evolving threat patterns. The model analyzes multiple parameters including traffic volume, packet characteristics, endpoint behaviors, and temporal patterns to accurately distinguish between legitimate activity and targeted attacks, reducing false positives and negatives compared to static security rules.
Solution Approach 2:
The system implements feedback mechanisms where the machine learning model continuously learns from detected behaviors and updates its detection criteria. This feedback loop enables the system to improve its measurement precision over time by adjusting detection parameters based on actual attack patterns and reducing both false positives and missed detections.
3Measurement precision
If manual analysis of network traffic is performed, then detailed inspection is possible, but resources are consumed and productivity is reduced
Solution Approach 1:
The system replaces manual mechanical analysis with automated machine learning-based inspection. The machine learning model processes network traffic data automatically, analyzing patterns and anomalies without human intervention. This substitution maintains detailed inspection capability while significantly improving productivity by eliminating time-consuming manual analysis.
Solution Approach 2:
The automated response system performs self-service by independently analyzing network traffic, identifying security issues, and executing remediation actions without requiring security professionals. This self-service capability maintains thorough inspection of network behaviors while dramatically improving operational efficiency and productivity.
Data Source
AI summary
A device receives information identifying malicious behavior by a compromised endpoint device associated with a network and traffic associated with the compromised endpoint device after the malicious behavior is identified. The device receives endpoint device information identifying other endpoint devices associated with the network, wherein the compromised endpoint device is not one of the other endpoint devices. The device receives network device information identifying network devices associated with the network, and processes the traffic, the endpoint device information, and the network device information, with a machine learning model, to generate a security policy to isolate the malicious behavior. The device performs one or more actions based on the security policy to isolate the malicious behavior.