Chronic Kidney Disease (CKD) implies that the human kidneys are harmed and unable to blood filter in the manner which they should. The disease is designated "chronic" in light of the fact that harm to human kidneys happen gradually over a significant time. This harm can make wastes to build up in your body. Many techniques and models have been developed to diagnos the CKD in early-stage. Among all techniques, Machine Learning (ML) techniques play a significant role in the early forecasting of different kinds ailments. ML techniques have been used for achieving analytical results which is one of the instruments utilize in medical analysis and prediction. In this paper, we employ experiential analysis of ML techniques for classifying the kidney patient dataset as CKD or NOTCKD. Seven ML techniques together with NBTree, J48, Support Vector Machine, Logistic Regression, Multi-layer Perceptron, Naïve Bayes, and Composite Hypercube on Iterated Random Projection (CHIRP) are utilized and assessed using distinctive evaluation measures such as mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), root relative squared error (RRSE), recall, precision, F-measure and accuracy.The experimental outcomes accomplished of MAE are 0.0419 for NB, 0.035 for LR, 0.265 for MLP, 0.0229 for J48, 0.015 for SVM, 0.0158 for NBTree and 0.0025 for CHIRP. Moreover, experimental results using accuracy revealed 95.75% for NB, 96.50% for LR, 97.25% for MLP, 97.75% for J48, 98.25% for SVM, 98.75% for NBTree, and 99.75% for CHIRP. The overall outcomes show that CHIRP performs well in terms of diminishing error rates and improving accuracy.

In device-to-device (D2D) communications, the channel gain between a transmitter and a receiver is difficult to predict due to channel variations. Hence, an attacker can easily perform an impersonation attack between two authentic D2D users. As a countermeasure, we propose a reinforcement learning-based technique that guarantees identification of the impersonator based on channel gains. To show the merit of our technique, we report its performance in terms of false alarm rate, miss-detection rate, and average error rate. The secret key generation rate is also determined under the impersonation attack based on physical layer security.

The next-generation Internet of vehicles (IoVs) seamlessly connects humans, vehicles, roadside units (RSUs), and service platforms, to improve road safety, enhance transit efficiency, and deliver comfort while conserving the environment. Currently, numerous entities communicate in the IoVs environment via insecure public channels that are susceptible to a variety of security assaults and threats. To address these security challenges, we design an anonymous authenticated key exchange mechanism for the IoVs in smart transportation supported by blockchain, referred to as AAKE-BIVT. AAKE-BIVT securely transmits traffic information to a cluster head, before heading to a nearby RSU utilizing the established secret session keys via mutual authentication and key agreement. A cloud server (CS) then securely aggregates data from related RSUs and generates transactions. The CS combines the transactions into blocks in a peer-to-peer network of CSs, and the blocks are confirmed and added to the blockchain via a voting-based consensus method. By means of rigorous informal security studies and formal security analysis through the random oracle model, we reveal that the proposed AAKE-BIVT is resistant to a broad range of potential security assaults in the IoVs environment. Furthermore, a comparative study reveals that AAKE-BIVT outperforms existing state-of-the-art techniques, in terms of security and functionality while being more efficient in terms of communication and computation. Additionally, the blockchain simulation validates the implementation viability of our proposed AAKE-BIVT.

In smart grids (SGs), the systematic utilization of consumer energy data while maintaining its privacy is of paramount importance. This research addresses this problem by energy theft detection while preserving the privacy of client data. In particular, this research identifies centralized models as more accurate in predicting energy theft in SGs but with no or significantly less data protection. Current research proposes a novel federated learning (FL) framework, namely FedDP, to tackle this issue. The proposed framework enables various clients to benefit from on-device prediction with very little communication overhead and to learn from the experience of other clients with the help of a central server (CS). Furthermore, for the accurate identification of energy theft, the use of a novel federated voting classifier (FVC) is proposed. FVC uses the majority voting-based consensus of traditional machine learning (ML) classifiers namely, random forests (RF), k-nearest neighbors (KNN), and bagging classifiers (BG). To the best of our knowledge, conventional ML classifiers have never been used in a federated manner for energy theft detection in SGs. Finally, substantial experiments are performed on the real-world energy consumption dataset. Results illustrate that the proposed model can accurately and efficiently detect energy theft in SGs while guaranteeing the security of client data.

The use of Internet of Drones (IoD) technology has surged across various domains such as logistics, surveying, industrial inspections, emergency response, security, infrastructure monitoring, crop management, and more. However, real-time communication with drones or Unmanned Aerial Vehicles (UAVs) in the IoD environment occurs over an insecure open channel, making it susceptible to various security and privacy vulnerabilities, including unauthorized access, data interception, denial of service attacks, and privacy concerns. Due to their unique characteristics, including long transmission distances, unstable communication environments, resource limitations, and the highly dynamic nature of UAVs, ensuring the security and privacy of IoD systems is of paramount importance for the success of IoD-based applications. Furthermore, drones are resource-constrained devices, and employing expensive security solutions is impractical, as it would significantly reduce the operational capacity of drones. In this paper, we present the design of an ultralightweight, secure, and robust user-authenticated key agreement framework for the IoD environment, named USAF-IoD. The proposed USAF-IoD is developed by incorporating authenticated encryption (ASCON), cryptographic hashing, XOR operations, and the use of physical unclonable functions (PUFs). PUFs are employed to enhance resistance against physical tampering attacks. The security analysis reveals that the proposed USAF-IoD meets the essential security requirements of the IoD environment. The comparative analysis further highlights the effectiveness of the proposed USAF-IoD, notably excelling in terms of security and functionality characteristics when compared to existing benchmark schemes, and showcasing competitive performance in computation, communication, and energy overheads.