  Traditional network diagnosis methods of Client-Terminal Device (CTD)
problems tend to be laborintensive, time consuming, and contribute to increased
customer dissatisfaction. In this paper, we propose an automated solution for
rapidly diagnose the root causes of network performance issues in CTD. Based on
a new intelligent inference technique, we create the Intelligent Automated
Client Diagnostic (IACD) system, which only relies on collection of
Transmission Control Protocol (TCP) packet traces. Using soft-margin Support
Vector Machine (SVM) classifiers, the system (i) distinguishes link problems
from client problems and (ii) identifies characteristics unique to the specific
fault to report the root cause. The modular design of the system enables
support for new access link and fault types. 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. The
system can perform fault diagnosis independent of the user's specific TCP
implementation, enabling diagnosis of diverse range of client devices
