# From Bottom of Sea to Space: Quo Vadis IoT? So What About Security?
## 5 Future Roadmap
### A Future Look-Ahead: The Promise of AI
#### 5.2.2 AI-Assisted Secure IoT Extensions
Issues such as bandwidth and privacy can make it hard to effectively share the information required for such a system to work. In [238] the authors propose a federated learning-based architecture that is able to help with privacy and better disseminate information across the network. The architecture is a hybrid blockchain that uses the main permissioned blockchain. The blockchain is maintained by RSUs as well as Directed Acyclic Graphs (DAGs), allowing for a better data sharing scheme. Federated learning is used to find the nodes that minimize the total cost of communication. Learned parameters and verification of parameters are then implemented into the blockchain in order to enhance the reliability of the learned models. When tested numerically, the scheme was proven to be effective with regard to accuracy and efficiency. AI-Assisted Secure Internet of Medical Things  
Increased use of machine learning in the IoMT has led to concerns with data privacy. To allow for a more secure system, the authors of [239] propose a privacy-preserving outsourced support vector machine scheme (EPoSVM). A distributed two-trapdoor public-key cryptosystem was used to design a security protocol. To increase the speed of the encryption process, the authors implement a secure data partitioning protocol that limits the bit length, removing the need for a system to collect message length. Then, using the secure protocols, the scheme outsources the training of the SVM to a gradient descent-based training model. Secure Outsourced SVM Classification is performed when the service requester uploads their encrypted data to the cloud for a secure classification. The cloud returns a ciphertext only able to be decrypted by the service requester. Through testing the scheme is proven to be secure and provide reliable and accurate classifications. Data privacy is an important concern, but there are other concerns that have to do with the security of IoMT, including trust, integrity of data, real-time processing, and network constraints. Behavior detection and a robust approach to maintaining the integrity of data are important to a secure system. With this in mind, the authors of [240] propose a scheme that eliminates and identifies malicious nodes, thus allowing it to be a robust network. To evaluate the degree of trust for a node, the scheme uses a NeuroTrust method that provides reliability, packet delivery, and compatibility. Through the use of homomorphic encryption, the method can remain lightweight and efficient while protecting data integrity and confidentiality. Simulations prove the system to be effective in terms of accuracy, computation cost, and ability to identify and remove malicious nodes. Authentication is an important part of security that needs to be effectively implemented in every system. In [241], the authors propose a lightweight and practical anonymous authentication protocol that can resist machine learning attacks on PUFs. The scheme is split into two steps: registration and authentication. Each device must first be registered with the server. In authentication, the server checks the device's identity and then allows it to enter the server if authenticated. To the authors' best knowledge, this is the first time that a PUF-based authentication scheme can prevent attacks from machine learning algorithms. To test the scheme, it was analyzed with a strict analysis scheme and proven to be able to resist attacks. The ability of a network to safely encrypt and decrypt images is useful in the medical field as most images include sensitive information. The authors of [242] propose a deep-learning-based image encryption and decryption network. Deep learning is applied to an image-to-image transformation. The encryption method used is one time pad and is sensitive to key change. Region of interest mining is used to find the interested segmentation of the image instead of decrypting the ciphertext first. After experimental testing using X-Ray datasets, the scheme is proven to be secure and cost-efficient in terms of computation and time.