Malware Detection on Smart Wearables Using Machine Learning Algorithms

Fadele Ayotunde Alaba works with the Department of Computer Science, Federal College of Education, Zaria. He is researching with several global awards in the IoT and security domain. He received a Bachelor of Computer Science (First Class Honours) from Nasarawa State University, Keffi, Nasarawa State, Nigeria, in 2008. He then received a Post Graduate Diploma in Education (PGDE) from Usman Danfodio University, Sokoto State, Nigeria, in 2012. He received a Master of Computer Science from Ahmadu Bello University, Zaria, Kaduna State, Nigeria, in 2014. Finally, he received a Ph.D. in Computer Science from the University of Malaya, Malaysia (QS WUR-65). He is an active reviewer of several top journals including Future Generation Computer Systems (FGCS), Information Systems, Journal of Network and Computer Applications (JNCA), IEEE Wireless Communications, and International Journal of Environmental Science and Technology. Álvaro Rocha is World's Top 1% Scientist by Stanford University (USA) and Elsevier and World’s Top 1% Scientist by Research Gate for the domains of Information Science and Information Systems. He is a professor at ISEG, University of Lisbon, invited professor at the University of Calabria, honorary professor at Amity University, chair of ITMA—Information and Technology Management Association, founder and vice-chair of IEEE SMC Portugal Chapter, and book series scientific manager at Springer-Nature. He holds the title of Honorary Professor and holds a D.Sc. in Information Science, Ph.D. in Information Systems and Technologies, M.Sc. in Information Management, and BCs in Computer Science. He is an integrated researcher at the ADVANCE (the ISEG Centre for Advanced Research in Management), and a collaborator researcher at CINTESIS (Center for Research in Health Technologies and Information Systems).

1. Introduction.- 2.Security Challenges of Wearable Technology.- 3. Machine Learning Algorithms On Malware Detection Against Smart Wearable Devices.- 4.Implementation Results.- 5. Conclusions, Future Directions, and Recommendations.