Cyber security risk analysis framework : network traffic anomaly detection
Name
1054927618-MIT.pdf
Description
Full printable version
Size
15.02 MB
Format
Adobe PDF
Checksum (MD5)
1a7ec30aba1da566b1ce433b2ae72923
Author(s)
Moe, Lwin P
Advisor(s)
Abel Sanchez.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
Cybersecurity is a growing research area with direct commercial impact to organizations and companies in every industry. With all other technological advancements in the Internet of Things (IoT), mobile devices, cloud computing, 5G network, and artificial intelligence, the need for cybersecurity is more critical than ever before. These technologies drive the need for tighter cybersecurity implementations, while at the same time act as enablers to provide more advanced security solutions. This paper will discuss a framework that can predict cybersecurity risk by identifying normal network behavior and detect network traffic anomalies. Our research focuses on the analysis of the historical network traffic data to identify network usage trends and security vulnerabilities. Specifically, this thesis will focus on multiple components of the data analytics platform. It explores the big data platform architecture, and data ingestion, analysis, and engineering processes. The experiments were conducted utilizing various time series algorithms (Seasonal ETS, Seasonal ARIMA, TBATS, Double-Seasonal Holt-Winters, and Ensemble methods) and Long Short-Term Memory Recurrent Neural Network algorithm. Upon creating the baselines and forecasting network traffic trends, the anomaly detection algorithm was implemented using specific thresholds to detect network traffic trends that show significant variation from the baseline. Lastly, the network traffic data was analyzed and forecasted in various dimensions: total volume, source vs. destination volume, protocol, port, machine, geography, and network structure and pattern. The experiments were conducted with multiple approaches to get more insights into the network patterns and traffic trends to detect anomalies.
Description
Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 84-86).
Subjects
Engineering and Management Program.
Integrated Design and Management Program.
MIT Department
Massachusetts Institute of Technology. Engineering and Management Program
Massachusetts Institute of Technology. Integrated Design and Management Program.
Terms of Use
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