Beaconing Detection via Frequency Domain Analysis
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Solution Overview
Problem
Existing solutions for detecting beaconing behavior in cyber security are limited by high memory footprint, short analysis time windows, and inability to handle non-periodic or dynamic beaconing patterns, as well as multiple periodicities employed by malware.
Innovation Solution
A system that uses frequency domain analysis and rescaling/aggregation to detect beaconing behavior in network data streams, ranking potential threats based on relevance and allowing for scalable detection over long time windows, capable of handling imperfect observations and shifted signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pattern-matching-based approaches or finite state machines are used to detect beaconing behavior, then detection accuracy for regular periodic patterns is improved, but memory footprint increases significantly
Solution Approach 1:
The patent replaces the mechanical state-machine approach with a signal processing-based detection system. Instead of maintaining state machines for every source/destination pair, the system uses frequency domain analysis (Fast Fourier Transform) to detect periodic patterns in network traffic intervals. This substitution dramatically reduces memory requirements while maintaining detection accuracy for periodic beaconing behavior.
2Measurement precision
If finite state machines are deployed for every source/destination pair to detect periodic events, then detection capability for strict periodic patterns is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex finite state machines with a simpler frequency analysis system. The detection mechanism uses Fast Fourier Transform to identify periodic patterns in traffic intervals, eliminating the need for complex state transition logic. This reduces device complexity while maintaining the ability to detect periodic beaconing behavior.
Solution Approach 2:
The patent changes the detection parameter from discrete state transitions to continuous frequency spectrum analysis. By analyzing the frequency components of traffic interval sequences, the system can detect periodic patterns without requiring complex state machine logic, thereby reducing device complexity.
3Speed
If existing solutions use fixed short analysis time windows to detect beaconing, then processing speed is improved, but detection capability for long-term periodic patterns deteriorates
Solution Approach 1:
The patent performs preliminary aggregation of network traffic data into time windows of various durations before frequency analysis. This allows the system to efficiently process long time spans by pre-organizing data, enabling both fast processing and long-term pattern detection without requiring analysis of entire historical datasets at once.
4Ease of operation
If existing solutions assume strictly regular periodic beaconing behavior, then detection simplicity is improved, but adaptability to real-world dynamic beaconing patterns deteriorates
Solution Approach 1:
The patent introduces dynamic adaptability by allowing the analysis system to handle variable periodicities and irregular intervals. The frequency domain analysis naturally accommodates changes in beaconing patterns over time, detecting multiple periodicities and adapting to dynamic malware behavior without requiring complex rule updates or reconfiguration.
Data Source
AI summary
A method for detecting beaconing behavior includes preprocessing network records to identify candidate source and destination pairs for detecting beaconing behavior, where each source and destination pair is associated with a specific time interval in a plurality of time intervals forming a time range, the time interval and time range having been predefined. The activity time interval information is converted from the time domain into the frequency domain. Candidate frequencies are determined from the source and destination pairs, as likely candidate frequencies/periodicities of beaconing activities.