Recommender System based on Empirical Study of Geolocated Clustering and Prediction Services for Botnets Cyber-Intelligence in Malaysia

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.

Analytics in developing countries: methods, applications, and the impact on the UN Sustainable Development Goals

“…Zamani et al. (2018) develop a recommendation system for predicting botnets attack in Malaysia, in which they use K‐means clustering to group threats based on geolocations. Araújo et al.…”

“…(2017) propose a spectrum management framework to govern wireless technologies within the context of developing countries. Predictive studies have been conducted on a recommendation system for predicting botnet attacks in Malaysia (Zamani et al., 2018), cloud computing adoption in Oman (Sharma et al., 2016), and mobile commerce adoption determinants in China (Chong, 2013). In addition, business intelligence initiatives have gained attention.…”

Digital platform based on geomarketing as an improvement in micro and small enterprises

“…Research has been developed based on the e-commerce system in contribution to the sales of micro and small enterprises [20], with the purpose of generating more sales opportunities, evidencing that the adoption of technologies provides options to excel in the business environment by generously increasing their income [21]. Likewise, recommendation systems with geolocated clusters [22] have been used to study customer behavior in contribution to retailing, which helps to provide recommendations on marketing strategy, as well as to predict cyber threats.…”