High-Quality in Data Authentication Dodging Massive Attack in VANETS

VANET plays an important role in the Security terms. VANET network is due to their unique features like as a high dynamic network (topology) and Mobility prediction. It attracts so much attention to the industry. VANET wireless networks are rapidly increased commercial and academic interests. Mobile connectivity, Traffic congestion management and road safety are some applications that have arisen within this network model. The routing protocol is a reactive type which means if there is data to be sent then the way will create. On-demand Distance Vector routing protocol is a generally used network topology based on rules for VANET. In surveyed of the routing protocol implemented a balance AODV method used for identifying the malicious nodes in the network. A balanced AODV routing method is defined with following characteristics:- (i) Use of threshold adaptive according to the network situations and balance index i.e node nature. (ii) Detect the malicious node in the network. (iii) Detection and prevention methods in real-time and independent on each vehicle node. In research paper, implement a B-AODV routing protocol and RSA method for detection and prevention the malicious node in the vehicular network. In this proposed algorithm, each vehicle node is employing balance index for acceptable and reject able REQ information’s (Bits). The consequences of the simulation tool in MATLAB (Matrix Laboratory) indicates BAODV and RSA method is used to detect and prevent the flood attach and loss of network bandwidth. Comparison between AODV, BAODV, RSA in normal phase defines B-AODV is exactly matched with AODV in the vehicular network and performance analysis overhead, an end to end delay and packet delivery rate

Software engineering based self-checking process for cyber security system in VANET

Newly, the cyber security of Vehicle Ad hoc Network (VANET) includes two practicable: Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I), that have been considered due to importance. It has become possible to keep pace with the development in the world. The people safety is a priority in the development of technology in general and particular in of VANET for police vehicles. In this paper, we propose a software engineering based self-checking process to ensure the high redundancy of the generated keys. These keys are used in underlying cyber security system for VANET. The proposed self-checking process emploies a set of NIST tests including frequency, block and runs as a threshold for accepting the generated keys. The introduced cyber security system includes three levels: Firstly, the registration phase that asks vehicles to register in the system, in which the network excludes the unregistered ones. In this phase, the proposed software engineeringbased self-checking process is adopted. Secondly, the authentication phase that checks of the vehicles after the registration phase. Thirdly, the proposed system that is able to detect the DOS attack. The obtained results show the efficient performance of the proposed system in managing the security of the VANET network. The self-checking process increased the randomness of the generated keys, in which the security factor is increased

A Practical Implementation of Medical Privacy-Preserving Federated Learning Using Multi-Key Homomorphic Encryption and Flower Framework

The digitization of healthcare data has presented a pressing need to address privacy concerns within the realm of machine learning for healthcare institutions. One promising solution is federated learning, which enables collaborative training of deep machine learning models among medical institutions by sharing model parameters instead of raw data. This study focuses on enhancing an existing privacy-preserving federated learning algorithm for medical data through the utilization of homomorphic encryption, building upon prior research. In contrast to the previous paper, this work is based upon Wibawa, using a single key for HE, our proposed solution is a practical implementation of a preprint with a proposed encryption scheme (xMK-CKKS) for implementing multi-key homomorphic encryption. For this, our work first involves modifying a simple “ring learning with error” RLWE scheme. We then fork a popular federated learning framework for Python where we integrate our own communication process with protocol buffers before we locate and modify the library’s existing training loop in order to further enhance the security of model updates with the multi-key homomorphic encryption scheme. Our experimental evaluations validate that, despite these modifications, our proposed framework maintains a robust model performance, as demonstrated by consistent metrics including validation accuracy, precision, f1-score, and recall.publishedVersio