Diagnosing client faults using SVM-based intelligent inference from TCP packet traces

TL;DR

The paper introduces the IACD system that uses SVM classifiers to diagnose client device faults from TCP packet traces, achieving high accuracy and independence from TCP implementation variations.

Contribution

The novel IACD system leverages SVM-based inference to accurately diagnose client faults solely from TCP traces, independent of TCP implementation.

Findings

Achieved 98% accuracy in diagnosing client faults.

Effectively distinguishes between link and client problems.

Operates independently of specific TCP implementations.

Abstract

We present the Intelligent Automated Client Diagnostic (IACD) system, which only relies on inference from Transmission Control Protocol (TCP) packet traces for rapid diagnosis of client device problems that cause network performance issues. Using soft-margin Support Vector Machine (SVM) classifiers, the system (i) distinguishes link problems from client problems, and (ii) identifies characteristics unique to client faults to report the root cause of the client device problem. Experimental evaluation demonstrated the capability of the IACD system to distinguish between faulty and healthy links and to diagnose the client faults with 98% accuracy in healthy links. The system can perform fault diagnosis independent of the client's specific TCP implementation, enabling diagnosis capability on diverse range of client computers.