Universiti Teknologi Malaysia Institutional Repository

Cyber-attack features for detecting cyber threat incidence from online news

Abdullah, M. S. and Zainal, A. and Maarof, M. A. and Kassim, M. N. (2019) Cyber-attack features for detecting cyber threat incidence from online news. In: 2018 Cyber Resilience Conference (CRC), 13-15 Nov 2018, Putrajaya, Malaysia.


Official URL: http://www.dx.doi.org/10.1109/CR.2018.8626866


There are large volume of data from the online news sources that are freely available which might contain valuable information. Data such as cyber-attacks news keep growing bigger and can be analyzed to gather informative insights of current situation. However, news is reported in many styles, added with the emerging of new cyber-attack and the ambiguous terms used have made the detection of the related news become more difficult. Thus, to handle these situations, the aim of this paper is to propose a scheme on detecting the related news about cyber-attacks. The scheme starts with identifying the cyber-attack features which will be used to classify the cyber-attack news. The scheme also includes a machine learning approach using Conditional Random Field (CRF) classifier and Latent Semantic Analysis (LSA) for further analysis. The results from this research should help people by showing the actual picture of cyber-attack occurrences in our surrounding and give valuable information to public thus raising social awareness about cyber-attack activities.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:cyber-attacks, internet, Conditional Random Field (CRF)
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
ID Code:91124
Deposited By: Narimah Nawil
Deposited On:31 May 2021 21:21
Last Modified:31 May 2021 21:21

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