Recommender system based on empirical study of geolocated clustering and prediction services for botnets cyber-intelligence in Malaysia

Nazri Ahmad Zamani, Aswami Fadillah Mohd Ariffin, Siti Norul Huda Sheikh Abdullah

Research output: Contribution to journalArticle

Abstract

A recommender system is becoming a popular platform that predicts the ratings or preferences in studying human behaviors and habits. The predictive system is widely used especially in marketing, retailing and product development. The system responds to users preferences in goods and services and gives recommendations via Machine Learning algorithms deployed catered specifically for such services. The same recommender system can be built for predicting botnets attack. Via our Integrated Cyber-Evidence (ICE) Big Data system, we build a recommender system based on collected data on telemetric Botnets networks traffics. The recommender system is trained periodically on cyber-threats enriched data from Coordinated Malware Eradication & Remedial Platform system (CMERP), specifically the geolocations and the timestamp of the attacks. The machine learning is based on K-Means and DBSCAN clustering. The result is a recommendation of top potential attacks based on ranks from a given geolocations coordinates. The recommendation also includes alerts on locations with high density of certain botnets types.

Original languageEnglish
Pages (from-to)473-478
Number of pages6
JournalInternational Journal of Advanced Computer Science and Applications
Volume9
Issue number12
DOIs
Publication statusPublished - 1 Jan 2018
Externally publishedYes

Fingerprint

Recommender systems
Learning systems
Product development
Learning algorithms
Marketing
Botnet

Keywords

  • Big Data
  • Botnets
  • Cyber-threat intelligence
  • DBSCAN
  • K-Means
  • Predictive analytics
  • Recommender system

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

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abstract = "A recommender system is becoming a popular platform that predicts the ratings or preferences in studying human behaviors and habits. The predictive system is widely used especially in marketing, retailing and product development. The system responds to users preferences in goods and services and gives recommendations via Machine Learning algorithms deployed catered specifically for such services. The same recommender system can be built for predicting botnets attack. Via our Integrated Cyber-Evidence (ICE) Big Data system, we build a recommender system based on collected data on telemetric Botnets networks traffics. The recommender system is trained periodically on cyber-threats enriched data from Coordinated Malware Eradication & Remedial Platform system (CMERP), specifically the geolocations and the timestamp of the attacks. The machine learning is based on K-Means and DBSCAN clustering. The result is a recommendation of top potential attacks based on ranks from a given geolocations coordinates. The recommendation also includes alerts on locations with high density of certain botnets types.",
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