Method and apparatus for producing a machine learning system for malware prediction in low complexity sensor networks

Abstract

One embodiment of this invention describes a method and apparatus for the use of Machine Learning to efficiently detect, identify, prevent, and predict cyber-attacks on Low Power and Low Complexity Sensor(FIG.) networks that have low data transmission requirements, something that all current Machine Learning techniques are unable to accomplish due to numerous restrictions when applied to Low Power and Low Complexity Sensors. Low Power and Low Complexity Sensors are frequently found in various Internet of Things (IOT) network architectures. The IOT is a network of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, actuators, and connectivity which enables them to connect and exchange data, providing more direct integration of the physical world into computer-based systems. However, this should not restrict the applicability of any potential embodiment of this invention as described in this patent application. A further understanding of the nature and the advantages of the particular embodiments disclosed herein may be realized by referencing the remaining portions to the specification.