Abstract

The cyber security toolkit, CyberSecTK, is a simple Python library for preprocessing and feature extraction of cyber-security-related data. As the digital universe expands, more and more data need to be processed using automated approaches. In recent years, cyber security professionals have seen opportunities to use machine learning approaches to help process and analyze their data. The challenge is that cyber security experts do not have necessary trainings to apply machine learning to their problems. The goal of this library is to help bridge this gap. In particular, we propose the development of a toolkit in Python that can process the most common types of cyber security data. This will help cyber experts to implement a basic machine learning pipeline from beginning to end. This proposed research work is our first attempt to achieve this goal. The proposed toolkit is a suite of program modules, data sets, and tutorials supporting research and teaching in cyber security and defense. An example of use cases is presented and discussed. Survey results of students using some of the modules in the library are also presented.

Highlights

  • The cyber security toolkit (CyberSecTK) is a simple Python library for preprocessing and feature extraction of cyber-security-related data

  • We assessed the tutorial and videos with the survey to help gauge if the code written for the library was understood by students and if there were correlations

  • We presented and discussed a new library for machine learning (ML) and cyber security

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Summary

Introduction

The cyber security toolkit (CyberSecTK) is a simple Python library for preprocessing and feature extraction of cyber-security-related data. As the digital universe expands, more and more data need to be processed using automated approaches. The statistics [1] show a rapid growth in internet of things (IoT) devices. There are over 12 billion devices that can connect to the internet, and it was predicted to reach 14.2 billion in 2019. According to the publication announcement on the Worldwide Semiannual Internet of Things Spending Guide forecast [2], a 13.6% compound annual growth rate (CAGR) is expected over the 2017–2022 period with an estimated investment of 1.2 trillion US dollars by 2022. The proliferation of the IoT will lead to new challenges in the near future

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