Challenges Facing IoT Expansion: Security and Energy

Abstract

The expansion of IoT (Internet of Things) is facing some resistance from organizations’ managers fearing security issues as well as technical and operational feasibility obstacles, in particular the energy consumption problem. Despite the legitimacy of these concerns, possible technical solutions exist to face them. These solutions are based on the use of machine learning techniques, namely Random Forest, Support Vector Machines and Artificial Neural Networks, to face the security risk by detecting intrusions at a high accuracy rate. In addition, our approach applies dimension reduction techniques such as Principal Components Analysis to minimize data sending and consumption. Therefore, our proposed solution show promising results in boosting security and quality of services (QoS) in IoT-cloud environments based on machine learning.