Machine learning algorithms for privacy preserving in vehicular ad hoc network

Abstract

Machine learning (ML) will improve the outcomes through the use of methods that categorize the information into the predetermined set. This work is to present an estimation and assessment of machine learning techniques for achieving privacy preservation in vehicular ad hoc networks (VANETs). This method generates two distinct group keys for prime and secondary users. Road side units (RSUs) are deployed to broadcast one group key from the trusted authority (TA) to the primary users, and secondary users are utilized to transmit the other group key. The main aim of this network is developed to avoid vulnerable attacks and to enhance the privacy of this network, Naïve Bayesian classifier (BC), support vector machine (SVM), K-nearest neighbor (KNN), artificial neural networks (ANN), Bayesian network (BN) methods are utilized in correlation with the proposed deep neural networks (DNN) with the black widow optimization (BWO) for protection preserving. These learning characterization procedures are assessed concerning delay, network lifetime, throughput, delivery ratio, and drop and this proposed calculation (DNN-BWO) shows improved results than the current methodologies.

Effective privacy preserving in cloud computing using position aware Merkle tree model

“…Cloud computingsecurity encompasses several challenges, including multi-tenancy, data loss [7] and leakage, ease of accessibility, identity management, unsafe APIs, inconsistencies in service level agreements, patch management, and internal threats. Enforcing security measures that provide to the diverse needs of all cloud users is challenging, as different users have varying security demands based on their objectives for using cloud services [8]. While previous research in cloud computing has predominantly focused on aspects such as technological architecture, distinguishing features from similar technologies, and security concerns, the paramount criterion that drives adoption remains security.…”

Implementation of Deep CNN Model for the Detection of Plant Leaf Disease

“…Global food security is threatened by plant diseases, causing severe consequences for small farmers whose lifestyle is based on healthy crops. Smallholder farmers contribute over 80% of the agricultural output, yet 50% to 60% of the yield is wasted due to pests and diseases [1]. Agricultural organisations and institutions consult with many domain experts to prevent/reduce crop loss.…”

Advances of vehicular ad hoc network using machine learning approach

“…In the subsequent testing stage, the trained model is utilized to make predictions or generate outputs based on new, unseen data. This two-step process allows us to harness the power of machine learning to address complex problems that were traditionally difficult to solve [23]- [27].…”