Unsupervised Machine Learning Techniques for Detecting Malware Applications in Wireless Devices

  • Jackson Akpojaro Dept of Mathematics and Computer Science, Western Delta University, Oghara, Delta State, Nigeria
  • Princewill Aigbe Department of Mathematics and Computer Science, Western Delta University Oghara, Delta State
  • Ugochukwu Onwudebelu Department of Mathematics & Computer Science, Federal University, Ndufu Alike Ikwo, Abakiliki, Ebonyi State
Keywords: big data, wireless devices, malware, supervised algorithms, unsupervised algorithms


It is no doubt that we are in the era of ‘big data’, and different machines and tools are being developed every day to enable users to effectively access, manipulate and process data to provide timely information needed for decision making. The situation has led to increasingly use of wireless devices including smartphones, tablets, pacemakers, etc., with different platforms. As professionals including doctors, engineers, scientists, artists, etc., use these devices in accessing, process and disseminating information services are available, so also malware attackers are strategizing. Hence the last one decade has witnessed constant literatures in the design and development of both supervised and unsupervised machine learning algorithms to checkmate malware applications in wireless devices. In this paper, we study the properties of unsupervised learning algorithms; in particular, we quantify the performance of these algorithms under two scenarios; using data sets from unknown attackers and data sets from known attackers. Our findings show that the recently -algorithm appears superior to the other unsupervised algorithms investigated.    

Author Biography

Jackson Akpojaro, Dept of Mathematics and Computer Science, Western Delta University, Oghara, Delta State, Nigeria

Dept of Mathematics and Computer Science

Senoir Lecturer


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How to Cite
Akpojaro, J., Aigbe, P., & Onwudebelu, U. (2014). Unsupervised Machine Learning Techniques for Detecting Malware Applications in Wireless Devices. Transactions on Machine Learning and Artificial Intelligence, 2(3), 20-29. https://doi.org/10.14738/tmlai.23.206