토픽 모델링을 이용한 자동 인터넷 응용 프로그램 시그니쳐 추출

학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2013. 2. 김종권.Classifying network traffic according to the application that generated it has attracted significant interests among Internet researchers and operators, as it is an essential task for understanding, operating, optimizing, planning, and financing the Internet. Although content-analysis based Deep Packet (Payload) Inspection technique has been found very accurate once given a set of known payload signature strings for corresponding applications, it is very time consuming and challenging to manually derive and construct the signatures. In this paper, we propose a new, automatic payload content-analysis based traffic classification method called TASTE(Topic-model based Automatic Signature Extraction). TASTE adopts the Latent Dirichlet Allocation (LDA) topic model, which is one of the most popular probabilistic text modeling techniques for extracting latent semantic information from text corpa. Our evaluation with a broad range of data sets demonstrates that TASTE can automatically detect and identify signatures for a range of applications without any prior knowledge, with 96-98% of overall accuracy.Chapter 1 Introduction 1 1.1 Background 1 1.2 Main Idea and Contributions 2 1.3 Thesis Organization 3 Chapter 2 Related Work 4 Chapter 3 Topic Model Based Classification 8 3.1 LDA 8 3.2 Applying LDA to Traffic Classification 10 3.2.1 Overview 10 3.2.2 Inputs: Data pre-processing and Parameters 11 3.2.3 Outputs: Topics and their Distributions 12 Chapter 4 METHODOLOGIES 14 4.1. Performance metrics 14 4.2. Data set and reference benchmark 15 4.3. Parameter Setting 17 Chapter 5 RESULTS 23 5.1. Performance Comparison 23 5.2. Classification Quality 24 Chapter 6 DISCUSSION 26 Chapter 7 Conclusion 27Maste

A Hybrid Approach for Accurate Application Traffic Identification

Maste

A Hybrid Approach for Accurate Application Traffic Identification

1