Finite-State Machine Profiles for VoIP SPIT Detection
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Solution Overview
Problem
Existing Voice over Internet Protocol (VoIP) systems face challenges in detecting unwanted or malicious calls, known as SPIT, as they rely on coarse-grained profiles that do not effectively characterize call behavior, leading to inefficiencies in identifying improper call behavior.
Innovation Solution
The use of finite-state machines to represent the legal states and state transitions of VoIP communications protocols, with execution profiles and rules generated from observed behaviors to monitor and alert on potentially improper call behavior, providing a more nuanced characterization of call behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If coarse-grained profiles are used to characterize calls, then the system is simpler to implement, but the detection precision of improper call behavior deteriorates
Solution Approach 1:
The patent segments the call behavior analysis into multiple discrete state variables (e.g., call duration state, inter-call interval state, time of day state, day of week state, caller ID suppression state) rather than using a single coarse-grained profile. Each state variable is independently monitored and combined to form a comprehensive execution profile, enabling fine-grained characterization of call behavior while maintaining manageable system complexity through modular state tracking.
2Measurement precision
If fine-grained execution profiles are used to characterize call behavior, then the detection precision of improper call behavior improves, but the device complexity increases
Solution Approach 1:
The patent implements dynamic state transitions where the execution profile adapts based on observed call behavior. The finite state machine transitions between states based on incoming calls, allowing the system to dynamically adjust its monitoring focus. This dynamic approach enables the system to maintain high detection precision while managing complexity by only actively tracking relevant state combinations rather than pre-defining all possible profiles.
Solution Approach 2:
The system performs self-service by automatically learning and updating execution profiles from observed call patterns without requiring manual configuration. The finite state machine automatically transitions through states based on actual call data, and the system self-adjusts its detection thresholds based on learned behavior patterns, reducing the operational complexity of profile management while maintaining high detection precision.
Data Source
AI summary
An apparatus and method for detecting potentially-improper call behavior (e.g., SPIT, etc.) are disclosed. The illustrative embodiment of the present invention is based on finite-state machines (FSMs) that represent the legal states and state transitions of a communications protocol at a node during a Voice over Internet Protocol (VoIP) call. In accordance with the illustrative embodiment, a library of FSM execution profiles associated with improper call behavior is maintained. When there is a match between the behavior of a finite-state machine during a call and an execution profile in the library, an alert is generated.