Predicting cybersecurity incidents using machine learning algorithms: a case study of Korean SMEs

Alaa Mohasseb, Benjamin Aziz, Jeyong Jung, Julak Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

The increasing amount and complexity of cyber security attacks in recent years have made text analysis and data-mining based techniques an important factor in detecting security threats. However, despite the popularity of text and other data mining techniques, the cyber security community has remained somehow reluctant in adopting an open approach to security-related data. In this paper, we analyze a dataset that has been collected from five Small and Medium companies in South Korea, this dataset represents cyber security incidents and response actions. We investigate how the data representing different incidents collected from multiple companies can help improve the classification accuracy and help the classifiers in distinguishing between different types of incidents. A model has been developed using text mining methods, such as n-gram, bag-of-words and machine learning algorithms for the classification of incidents and their response actions. Experimental results have demonstrated good performance of the classifiers for the prediction of different types of response and malware.
Original languageEnglish
Title of host publicationProceedings of the 5th International Conference on Information Systems Security and Privacy
PublisherINSTICC
Pages230-237
Number of pages8
ISBN (Print)978-989-758-359-9
Publication statusPublished - 24 Feb 2019
Event5th International Conference on Information Systems Security and Privacy - Prague, Czech Republic
Duration: 23 Feb 201925 Feb 2019
http://www.icissp.org/

Conference

Conference5th International Conference on Information Systems Security and Privacy
Abbreviated titleICISSP 2019
Country/TerritoryCzech Republic
CityPrague
Period23/02/1925/02/19
Internet address

Keywords

  • Text Mining
  • Machine Learning
  • Malicious Code
  • Malware
  • Cybersecurity

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