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The unstoppable growth of the Internet of Things (IoT) requires increasingly gleaming security measures. IoT, integrating a variety of into networks to enable intelligent services makes it vulnerable towards security issues. It is important to protect user privacy, and address attacks such as spoofing, denial of service, jamming and many more. Security solutions based on machine learning can protect data privacy. Machine learning approaches help to overcome the difficulty in detecting cyber attacks early due to computational limitations of IoT devices. The critical factors solving the practical challenges of IoT implementation include
IoT authentication is one vital security component, allowing IoT devices to distinguish between source nodes and outside attacks. Security is provided by focusing on the features of radio channels and transmitters in the physical layer of the device. It can determine whether the transmission is authentic. Machine learning can be used to select an optimum threshold to achieve the highest accuracy of authentication. ML can also be incorporated to improve spoofing resistance.
IoT devices prevent the access of resources by unauthorized users with access control. ML techniques are used to detect to detect unauthorized access. Access control protocol can be developed using machine learning to manage access control. ML techniques such as vector machines enable detecting attacks on internet traffic and electricity grid.
3.Secure IoT offloading:
Secure offloading enables IoT devices to use external, cloud-based computation and storage devices. Here ML can be used to identify the optimal rate of offloading data to combat spoofing attacks.
ML aided supervised learning techniques can be employed in IoT devices to detect malware from identifying aberrant behavior. It protects devices from privacy leakage, power depletion and network performance degradation against malware.
As connected devices are outnumbering humans, machine learning has proved its worth in securing the devices.