Outbound SPIT Filter with Optimal Performance Guarantees

TL;DR

This paper introduces a mathematically rigorous, optimal outbound SPIT filtering method using decision theory and sequential probability ratio tests, providing concrete performance bounds and eliminating manual parameter tuning.

Contribution

It formalizes the SPIT detection problem in a decision-theoretic framework, deriving an optimal, scalable solution with provable performance guarantees.

Findings

Method is proven to be optimal in minimizing expected loss.

The approach is fully automated, requiring no manual parameter tuning.

The solution is computationally simple and scalable.

Abstract

This paper presents a formal framework for identifying and filtering SPIT calls (SPam in Internet Telephony) in an outbound scenario with provable optimal performance. In so doing, our work is largely different from related previous work: our goal is to rigorously formalize the problem in terms of mathematical decision theory, find the optimal solution to the problem, and derive concrete bounds for its expected loss (number of mistakes the SPIT filter will make in the worst case). This goal is achieved by considering an abstracted scenario amenable to theoretical analysis, namely SPIT detection in an outbound scenario with pure sources. Our methodology is to first define the cost of making an error (false positive and false negative), apply Wald's sequential probability ratio test to the individual sources, and then determine analytically error probabilities such that the resulting expected loss is minimized. The benefits of our approach are: (1) the method is optimal (in a sense defined in the paper); (2) the method does not rely on manual tuning and tweaking of parameters but is completely self-contained and mathematically justified; (3) the method is computationally simple and scalable. These are desirable features that would make our method a component of choice in larger, autonomic frameworks.