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Cognitive Radio Networks (CRNs) allows opportunistic usage of spectrums owned by licensed users or primary users (PUs). The unlicensed users or secondary users (SUs) that use the spectrum rely opportunistically on spectrum sensing to determine the presence of PU signal. Unfortunately, this attribute opens the door for attacks such as the as the Primary User Emulation Attack (PUEA). This attack happens when an attacker emulates a PU signal. The intention of the attacker might be to grab the vacant channels for its data transmission or entirely disrupt the working of the CRN. Hence it is necessary to combat the consequences of PUEA effectively. Artificial Intelligence (AI) has shown its excellence in various applications including the detection of PUEA. To further enhance the security of the CRN, this research work proposes a novel classification framework called Online Adaptive Memory based Genetically optimized Artificial Neural Network (OAM-GANN) which introduces adaptive online learning of network parameters to identify the presence of PUEA. The proposed OAM-GANN involves Computational Intelligence (CI) algorithm called Memory based Genetic Algorithm (MGA) to optimally tune the hyperparameters of the developed ANN. The advantages of online adaptive training and optimal hyperparameter tuning of the AI model, result in improved security to the network and the data being transmitted in the CRN. The performance of the proposed attack detection model is evaluated in terms of accuracy, sensitivity, specificity, error rate, and detection probability. In addition, the performance of the proposed secure CRN is evaluated in terms of throughput and packet delivery ratio.

Crowdfunding is a method of raising funds from a large number of individuals or businesses. Investors can contribute to any project they are interested in and earn if the initiative is successful. Many crowdfunding sites now exist, and they accept large sums of money from investors and contributors and then leave them with bogus promises. Blockchain-based crowdfunding alters the usual approach to company finance. Generally, when people need to acquire funds to start a firm, they must first develop a strategy, statistical surveys, and models, and then offer their ideas to attract people or organisations. Banks, individual investors, and venture capital firms were among the sources of funding. The modern crowdfunding concept is based on three types of on-screen characters: the task initiator who presents the idea or venture to be financed, individuals or investors who invest in the idea, and a platform that connects these two characters to make the venture successful. It can be used to fund a wide range of start-ups and new concepts, such as inventive activities, medical improvements, travel, and social commercial enterprise projects. This work presents a practical implementation of a crowdfunding application that is secured by a lattice-based cryptosystem for encryption of user data and zero-knowledge proof for the identification of application users. Additionally, machine learning has been used for prediction of campaign success for the benefit of fund contributors.