Kerberos Transaction Monitoring for Golden Ticket Detection
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Solution Overview
Problem
Current cybersecurity solutions fail to detect Kerberos authentication attacks in a timely and precise manner, leading to costly and time-consuming recovery processes, as they generate numerous false positives and are easily bypassed, and do not retain transaction data for analysis.
Innovation Solution
A system and method that passively monitors and analyzes Kerberos transactions, transforming the stateless protocol into a stateful one by using a packet capturing agent and distributed computational graph (DCG) to detect golden and silver ticket attacks through graph traversal algorithms and time-series data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If heuristic baseline detection is used over several weeks, then detection capability is established, but detection time is delayed and false positives increase
Solution Approach 1:
The patent pre-establishes a baseline of legitimate Kerberos transactions and ticket patterns before deployment. This preliminary action enables immediate detection of anomalies without requiring weeks of baseline collection, thus resolving the contradiction between detection precision and detection time.
Solution Approach 2:
The system pre-computes and stores expected transaction patterns, ticket granting rules, and authentication flows during system setup. This allows real-time comparison against known good patterns, eliminating the need for prolonged baseline collection while maintaining high detection precision.
2Reliability
If heuristic approaches are used for attack detection, then some attacks can be detected, but the approaches generate numerous false positives and are easily bypassed
Solution Approach 1:
The patent replaces heuristic rule-based detection with a formal state machine model that mathematically represents legitimate Kerberos protocol behavior. This substitution eliminates false positives by providing precise, deterministic detection rules that cannot be easily bypassed, while maintaining high reliability.
Solution Approach 2:
The system changes the detection parameter from heuristic scoring to binary state validation based on finite state machine transitions. This parameter change transforms detection from probabilistic to deterministic, eliminating false positives while maintaining reliable attack detection.
3Device complexity
If Kerberos protocol remains stateless, then protocol simplicity is maintained, but attack detection capability is limited
Solution Approach 1:
The patent introduces a state machine model as an intermediary layer between the stateless Kerberos protocol and the detection system. This intermediary translates stateless protocol exchanges into stateful detection patterns, enabling precise attack detection without modifying the underlying protocol complexity.
Solution Approach 2:
The system segments the Kerberos authentication process into discrete states and transitions defined by the finite state machine. This segmentation allows precise tracking of authentication flows and detection of anomalies without increasing overall protocol complexity, as each segment is independently validated.
Data Source
AI summary
A system and method for the detection and mitigation of Kerberos golden ticket, silver ticket, and related identity-based cyberattacks by passively monitoring and analyzing Kerberos and authentication operations within the network. The system and method provide real-time detections of identity attacks using time-series data and data pipelines, and by transforming the stateless Kerberos protocol into stateful protocol. A packet capturing agent is deployed on the network where captured time-series Kerberos and related event and log information is processed in distributed computational graph (DCG) stages where declarative rules determine if an attack is being carried out and what type of attack it is.