s proposed in [90]. AttriGuard works in two phases. In Phase I, the minimum noise was found by adapting existing evasion attacks in adversarial ML. This noise protects users' attribute values by adding itself to the user's public data. In Phase II, the proposed method sampled one attribute value according to a certain probability distribution and added the corresponding noise found in Phase I to the user's public data. Blockchain techniques offer new security and privacy protection options to the distributed network. It can enhance security and privacy protection by providing authentication, access control, and confidentiality [58]. The authors in [5] developed a blockchain-based method to protect the user's security in the SS network. A Multiple Operators SS (MOSS) smart contract framework was proposed to allow users to share their spectrum decentralized and secure. Moreover, a combination of blockchain and a DRL framework named DeepCoin has been proposed in [57] to preserve the energy system to defend against Byzantine attacks. By using short signatures and functions to generate the blocks, it can prevent smart grid attacks. Furthermore, the DRL in their framework can detect network attacks and fraudulent trans- actions for the blockchain-based energy network by using recurrent neural networks. The performance of their proposed method has been verified. 11.3 Summary In this chapter, a comprehensive investigation of state-of-the- art ML-based SS solutions was presented. It has been noted that the dynamic access and sharing paradigms of SS networks may open the system to many security concerns. Two typical spectrum sensing attacks were discussed, i.e. PUE and SSDF. Two common attacks, i.e. jamming and eavesdropping, dur- ing wireless access and transmission in the context of the SS network were also addressed. Furthermore, the coexistence of different types of users and the application of ML all require massive information exchanges, generating tremendous concerns about privacy. Open Issues and Future Directions 