Malicious Message Indicator Scoring for Endpoint-Specific Threat Detection
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Solution Overview
Problem
Existing endpoint security systems struggle to detect and intercept new, evolving cybersecurity threats, such as zero-day attacks, which can evade detection and pose a security risk to organizations by delivering malicious messages to employees, potentially leading to breaches of sensitive information.
Innovation Solution
A system and method for determining indicators of malicious elements within email messages, involving servers that receive reports of malicious messages, identify indicators, determine a dangerousness score, and store this information in a threat database to generate an endpoint-specific threat data set, which can be used by endpoint security systems to enhance protection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If endpoint security systems process a large number of cybersecurity attacks daily, then the volume of processed threats increases, but the detection accuracy decreases due to small percentage of missed detections
Solution Approach 1:
The system segments the threat detection process into multiple specialized components: initial filtering by endpoint security systems, collection of missed detections by threat collection servers, analysis by threat analysis servers, and distribution of updated indicators. This segmentation allows each component to focus on specific aspects, maintaining high detection accuracy even when processing large volumes of threats.
Solution Approach 2:
Threat collection servers and threat analysis servers act as intermediaries between endpoint security systems and the broader security infrastructure. These intermediaries collect, aggregate, and analyze missed detections from multiple endpoints, enabling centralized intelligence gathering that improves overall detection accuracy without burdening individual endpoint systems.
2Reliability
If endpoint security systems use known cybersecurity attack signatures, then detection of known threats is effective, but detection of new zero-day attacks fails
Solution Approach 1:
The system performs preliminary action by collecting and analyzing threat indicators before they become widespread. Threat collection servers proactively gather missed detections and potential zero-day attacks, and threat analysis servers analyze these indicators in advance, preparing updated threat intelligence that can be quickly distributed to endpoint systems when new threats emerge.
Solution Approach 2:
The system implements feedback loops where threat analysis servers continuously analyze new threat indicators and update threat databases, which are then distributed back to endpoint security systems. This feedback mechanism ensures that endpoint systems receive updated detection capabilities for new threats while maintaining their reliable detection of known threats through established signature databases.
3Adaptability or versatility
If organizations invest in multiple cybersecurity tools, then the variety of security coverage increases, but the complexity of the security apparatus increases
Solution Approach 1:
The threat intelligence platform provides multi-functionality by serving multiple purposes: collecting threat data from various sources, analyzing threat indicators, storing threat intelligence in centralized databases, and distributing updated indicators to endpoint systems. This universal platform reduces the need for separate specialized tools for each function, simplifying the overall security apparatus while maintaining comprehensive coverage.
Data Source
AI summary
Systems and methods are described for determination of indicators of malicious elements within messages. A report of a malicious message is received from a user of an organization, the malicious message having traversed an endpoint security system of the organization. After receiving the report of the malicious message, one or more indicators of one or more malicious elements of the malicious message are identified. Further, an identification of the endpoint security system and a dangerousness score of the malicious message are determined. The one or more indicators, the identification of the endpoint security system, and the dangerousness score are stored into a threat database that is able to be queried to generate an endpoint-specific threat data set.