Collaborative Email Security System for Phishing Defense
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Solution Overview
Problem
Existing email security systems primarily rely on centralized perimeter defenses, which are ineffective in identifying and mitigating phishing and ransomware attacks once malicious emails reach the user's inbox, as they do not leverage end-user knowledge or behavior to classify threats effectively.
Innovation Solution
A collaborative intelligence system that allows end-users to classify potentially malicious emails in a protected environment, with the classification results being propagated across the network, enabling all users to benefit from the knowledge of one member and updating security rules in real-time to enhance vigilance against such threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized perimeter defense systems are used to filter emails, then the system structure is simple and centralized control is maintained, but the ability to identify phishing and ransomware attacks reaching user inboxes is insufficient
Solution Approach 1:
The email security system is segmented into multiple independent components: centralized perimeter gateway for initial filtering, local endpoint agents on user devices for secondary analysis, and distributed threat intelligence network. This segmentation allows the system to maintain centralized control while enabling localized detection capabilities that can identify attacks that bypass the perimeter gateway.
Solution Approach 2:
The patent implements a nested defense architecture where multiple security layers are embedded within each other: the centralized gateway provides the outer layer of perimeter defense, while local endpoint agents provide inner layers of protection at the user device level. Each layer operates independently but contributes to the overall security effectiveness, allowing detection of threats that penetrate outer layers.
2Measurement precision
If end-users are empowered to classify malicious emails, then threat detection accuracy improves through user intelligence, but the system complexity increases due to distributed decision-making
Solution Approach 1:
The system implements feedback loops where user classifications of emails as malicious or legitimate are captured, analyzed, and used to update threat intelligence databases. This feedback mechanism allows the system to learn from user decisions and improve future classifications, maintaining high accuracy while managing complexity through automated learning algorithms.
Solution Approach 2:
The patent enables self-service capabilities where the system automatically processes and learns from user classifications without requiring manual intervention for each decision. The distributed threat intelligence network automatically aggregates user feedback, updates security rules, and propagates learnings across the network, reducing the operational complexity of managing distributed user input.
3Productivity
If threat information is shared across the network in real-time, then collective vigilance against attacks is amplified, but the data processing and communication overhead increases
Solution Approach 1:
The system extracts and shares only the most critical threat intelligence data across the network, such as identified phishing patterns, ransomware signatures, and malicious sender information, rather than transmitting all email data. This selective extraction minimizes network bandwidth consumption while maintaining rapid threat response capabilities through targeted information sharing.
Solution Approach 2:
The patent implements partial information sharing where not all users receive all threat intelligence updates, but rather only those relevant to their specific context and risk profile. This approach reduces overall network traffic while ensuring that each user receives sufficient information to maintain high productivity in threat detection and response.
4Adaptability or versatility
If security rules are updated continuously based on user input, then the system adapts to new threats rapidly, but the processing load on the system increases
Solution Approach 1:
The system performs preliminary processing and filtering of user input data before initiating full rule update sequences. Critical updates are pre-processed and validated to minimize the computational load during actual rule application. This preliminary action allows rapid adaptation to new threats while managing processing resources efficiently through staged updates.
Solution Approach 2:
The patent implements periodic batch processing of security rule updates rather than continuous real-time updates for all users. Threat intelligence is aggregated over defined time periods and processed in batches, allowing the system to adapt rapidly to new threats while reducing overall computational load through time-based scheduling of intensive processing operations.
Data Source
AI summary
A system of averting phishing and ransomware attacks is disclosed wherein system comprises of a network of computers that exchange information to collaborate in sharing knowledge about the attack so that the other computers on the network are alerted and can successfully thwart similar attacks. This collaborative system relies on the end-point intelligence. That is, the computer that is the recipient entity of a malicious electronic mail message allows a review of the content of the message in a safe and sand-boxed environment, referred to as Safe View, and makes a determination regarding the maliciousness of the message. If indeed this message is determined to be malicious, the message is forwarded to an enterprise system on network that analyzes the message and gleans key metadata from it and distributes this information over the network to all the computer nodes on the network. Thus, the enterprise system saves the extracted metadata and key parameters in its database and instantaneously shares with all the systems on the network that utilize it for future analysis of electronic mail messages received. In this manner the individual nodes become better prepared to preemptively analyze and discard any malicious electronic mail messages that are in fact phishing or ransomware attacks. In addition to sharing the end-point intelligence of any one of the nodes with the rest of the network, the enterprise node may itself generate and share knowledge with all local nodes encapsulating information gathered from third parties regarding any prevalent trends, as well as information about malicious phishing or ransomware exploits as they become known in information security community.