Evaluation of Ensemble Machine Learning Methods in Mobile Threat Detection
Kumar, S., Viinikainen, A., & Hämäläinen, T. (2017). Evaluation of Ensemble Machine Learning Methods in Mobile Threat Detection. In ICITST 2017 : The 12th International Conference for Internet Technology and Secured Transactions (pp. 261-268). Infonomics Society. https://doi.org/10.23919/ICITST.2017.8356396
Date
2017Copyright
© 2017 IEEE. Personal use of this material is permitted.
The rapid growing trend of mobile devices continues to soar causing massive increase in cyber security threats. Most pervasive threats include ransom-ware, banking malware, premium SMS fraud. The solitary hackers use tailored techniques to avoid detection by the traditional antivirus. The emerging need is to detect these threats by any flow-based network solution. Therefore, we propose and evaluate a network based model which uses ensemble Machine Learning (ML) methods in order to identify the mobile threats, by analyzing the network flows of the malware communication. The ensemble ML methods not only protect over-fitting of the model but also cope with the issues related to the changing behavior of the attackers. The focus of this study is on android based mobile malwares due to its popularity among users. We have used ensemble methods to combine output of 5 supervised ML algorithms such as RF, PART, JRIP, J.48 and Ridor. Based on the evaluation results, the proposed model was found efficient at detecting known and unknown threats with the accuracy of 98.2%.
...
Publisher
Infonomics SocietyParent publication ISBN
978-1-908320-93-3Conference
International Conference for Internet Technology and Secured TransactionsIs part of publication
ICITST 2017 : The 12th International Conference for Internet Technology and Secured TransactionsKeywords
Publication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/27862488
Metadata
Show full item recordCollections
License
Related items
Showing items with similar title or keywords.
-
A Network-Based Framework for Mobile Threat Detection
Kumar, Sanjay; Viinikainen, Ari; Hämäläinen, Timo (IEEE, 2018)Mobile malware attacks increased three folds in the past few years and continued to expand with the growing number of mobile users. Adversary uses a variety of evasion techniques to avoid detection by traditional systems, ... -
Anomaly detection in wireless sensor networks
Lateef, Asim (2016)Wireless Sensor Network can be defined as a network of integrated sensors responsible for environmental sensing, data processing and communication with other sensors and the base station while consuming low power. Today, ... -
Unsupervised network intrusion detection systems for zero-day fast-spreading network attacks and botnets
Vahdani Amoli, Payam (University of Jyväskylä, 2015)Today, the occurrence of zero-day and complex attacks in high-speed networks is increasingly common due to the high number vulnerabilities in the cyber world. As a result, intrusions become more sophisticated and fast ... -
Problem Transformation Methods with Distance-Based Learning for Multi-Target Regression
Hämäläinen, Joonas; Kärkkäinen, Tommi (ESANN, 2020)Multi-target regression is a special subset of supervised machine learning problems. Problem transformation methods are used in the field to improve the performance of basic methods. The purpose of this article is to test ... -
Dimensionality reduction framework for detecting anomalies from network logs
Sipola, Tuomo; Juvonen, Antti; Lehtonen, Joel (CRL Publishing, 2012)Dynamic web services are vulnerable to multitude of intrusions that could be previously unknown. Server logs contain vast amounts of information about network traffic, and finding attacks from these logs improves the ...