The information exchanged over the smart grid networks is sensitive and private. As such, proper mechanisms must be put in place to protect these messages from security and privacy violations. Although many schemes have been presented in literature to address these challenges, a number of them rarely consider concurrent authentication of smart meters, while some are inefficient or still lack some of the smart grid network security and privacy requirements. In this article, a novel concurrent smart meters authentication algorithm is presented, based on some trusted authority. Formal security analysis of this algorithm is executed using Burrows‐Abadi‐Needham logic, which shows that this algorithm provides strong authentication among the smart meter, utility service provider and trusted authority. In addition, session keys are independently computed and verified between the smart meter and utility service provider with the help of the trusted authority. Informal security analysis shows that this algorithm provides device anonymity, perfect forward key secrecy, strong mutual authentication and is resilient against replay, de‐synchronization, privileged insider, impersonation, eavesdropping, side‐channel, and traceability attacks. In terms of performance, the proposed algorithm exhibits the least communication and computation overheads when compared with other related schemes.

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Brain tumors are the 10th leading reason for the death which is common among the adults and children. On the basis of texture, region, and shape there exists various types of tumor, and each one has the chances of survival very low. The wrong classification can lead to the worse consequences. As a result, these had to be properly divided into the many classes or grades, which is where multiclass classification comes into play. Magnetic resonance imaging (MRI) pictures are the most acceptable manner or method for representing the human brain for identifying the various tumors. Recent developments in image classification technology have made great strides, and the most popular and better approach that has been considered best in this area is CNN, and therefore, CNN is used for the brain tumor classification issue in this paper. The proposed model was successfully able to classify the brain image into four different classes, namely, no tumor indicating the given MRI of the brain does not have the tumor, glioma, meningioma, and pituitary tumor. This model produces an accuracy of 99%.

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Various cancelable biometric techniques have been proposed to maintain user data security. In this work, a cancelable biometric framework is introduced to satisfy user data security and keeping the original biometric template safe away from intruders. Thus, our main contribution is presenting a novel authentication framework based on the evolutionary Genetic Algorithm (GA)-based encryption technique. The suggested framework produces an entirely unrecognized biometric template by hiding the whole discriminative features of biometric templates; this is with exploiting the outstanding characteristics of the employed Genetic operations of the utilized encryption technique. Firstly, the GA initiates its search from a population of templates, not a single template. Secondly, some statistical operators are used to exploit the resulting initial population to generate successive populations. Finally, the crossover and mutation operations are performed to produce the ultimate cancelable biometric templates. Different biometric databases of the face and fingerprint templates are tested and analyzed. The proposed cancelable biometric framework achieves appreciated sensitivity and specificity results compared to the conventional OSH (Optical Scanning Holography) algorithm. It accomplishes recommended outcomes in terms of the AROC (Area under the Receiver Operating Characteristic) and the probability correlation distribution between the original biometrics and the encrypted biometrics stored in the database. The experimental results prove that the proposed framework achieves excellent results even if the biometric system suffers from different noise ratios. The proposed framework achieves an average AROC value of 0.9998, an EER (Equal Error Rate) of 2.0243×10 -4 , FAR (False Acceptance Rate) of 4.8843×10 -4 , and FRR (False Rejection Rate) of 2.2693×10 -4 .