The main aim is to investigate the security of the Z-wave communications and prevent security threats with Machine Learning.
“Smart home devices are more popular than ever before. While it brings many benefits into people’s homes, there are also concerning risks of the security in the Internet of Things (IoT) devices. Communication protocols plays are a massive role in the common language that smart appliances need to speak in order for the smart devices to exchange information with multiple devices in a smart environment.
In this study, a better knowledge of what Z-Wave protocol is gathered. Z-Wave is a wireless protocol for home automation appliances and is a leader in wireless control with 100 million products sold worldwide. It is a Radio Frequency based communication and it uses mesh networking with controllers and slaves. Implementing Machine Learning with wireless communications may be a solution to today’s security concerns.
Open-source data have been used to train Machine Learning algorithms, using Naïve Bayes, KStar, NNge, and Heoffdinger Tree. Training and testing machine learning algorithms to establish the best algorithm to use to prevent security threats from entering the Z-Wave network.”
“The benefits of this project are to explore what the Z-Wave protocol is and how Machine Learning could be used not only to secure the communications with devices that have S2 security but also those that have only S0 security.
Moreover, while executing attacks on the network may be successful to establish the areas for network improvements, training, and testing different Machine Learning algorithms that are more effective in the long run. It takes a long time to patch as-they-come vulnerabilities that may be overlooked, Machine Learning has the potential on detecting sooner than ethical attackers. “