Weak Classifier Ensemble for Spear Phishing Detection
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Solution Overview
Problem
Traditional systems for detecting spear phishing emails strain labor and resources due to the need for manual labeling of emails, leading to inefficiencies in security threat detection.
Innovation Solution
The method involves creating multiple weak classifiers based on random permutations of security event facets, applying a similarity algorithm to generate weak classifiers, and combining these classifiers to classify candidate security events, thereby reducing the reliance on manual labeling and improving detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual labeling methods are used to detect spear phishing emails, then detection capability is achieved, but labor resources and time consumption increase significantly
Solution Approach 1:
The system performs self-labeling by automatically generating labels for security events through the ensemble of weak classifiers, eliminating the need for external manual labeling resources. The system uses its own classification outputs to create training labels, creating a self-sustaining labeling mechanism that reduces human labor involvement while maintaining detection accuracy.
2Measurement precision
If more manually-labeled emails are used to improve detection accuracy, then detection precision improves, but labor resources and processing time increase
Solution Approach 1:
The system segments the labeling task into multiple independent weak classifiers, each handling specific facets of security events. These classifiers work in parallel to generate labels, dividing the overall labeling workload into manageable segments that can be processed efficiently without requiring sequential manual intervention for each email.
Solution Approach 2:
The system merges multiple weak classifiers into an ensemble that collectively performs the labeling function. By combining the outputs of multiple classifiers, the system achieves high detection accuracy equivalent to manual labeling while maintaining automated processing efficiency, as the ensemble decision integrates information from various classification perspectives.
3Reliability
If traditional detection systems are implemented, then security event detection is achieved, but resource strain on security software vendors occurs
Solution Approach 1:
The system uses multiple weak classifiers that are computationally inexpensive and can be rapidly generated and discarded. Each weak classifier is a simple, low-resource component that contributes to the overall detection capability without requiring significant computational resources, allowing the system to maintain high reliability through their ensemble while keeping individual component complexity low.
Data Source
AI summary
A computer-implemented method for detecting security events may include (1) identifying facets of candidate security events detected by a network security system, (2) assigning each of the facets of the candidate security events to one of multiple groups of facets to create permutations of the facets, (3) comparing, for each group of facets, the candidate security events according to a similarity algorithm that indicates similarity between the candidate security events, (4) generating, for each group of facets, a weak classifier for detecting security events based on a nearest neighbor graph, and (5) performing, by the network security system, a remedial action in response to classifying a candidate security event as a security threat by applying a combination of the weak classifiers for the groups of facets to the candidate security event. Various other methods, systems, and computer-readable media are also disclosed.