Smart driving has become conceivable due to the rapid growth of vehicular ad hoc networks. VANETs are considered as the main platform for providing safety road information and instant vehicle communication. Nevertheless, due to the open wireless nature of communication channels, VANET is susceptible to security attacks by malicious users. For this reason, secure anonymous authentication schemes are essential in VANETs. However, when vehicles reach a new roadside unit (RSU) coverage area, the vehicles need to perform reauthentication with the current RSU, which significantly diminishes the efficiency of the entire VANET. Therefore, the introduction of blockchain technology has created opportunities for VANETs to resolve the aforementioned challenges. Due to the decentralized nature of blockchain technology, rapid reauthentication of vehicles is achieved in this paper through secure authentication code transfer between the consecutive RSUs. The security strength of the proposed blockchain-based anonymous authentication scheme against various harmful security attacks is proven in the security analysis section to ensure that it provides better security. In addition, blockchain, as presented in the performance analysis section, is used to substantially diminish the computational cost compared to conventional authentication schemes.

IoT (Internet of Things) usage in industrial and scientific domains is progressively increasing. Currently, IoTs are utilized in numerous applications in different domains, similar to communication technology, environmental monitoring, agriculture, medical services, and manufacturing purposes. But, the IoT systems are vulnerable against various intrusions and attacks in the perspective on the security view. It is essential to create an intrusion detection model to detect and secure the network from different attacks and anomalies that continually happen in the network. In this paper, the anomaly detection model for an IoT network using deep neural networks (DNN) with chicken swarm optimization (CSO) algorithm was proposed. Presently, the DNN has demonstrated its efficiency in different fields that are applicable to its usage. Deep learning is the type of algorithm based on machine learning which used many layers to gradually extricate more significant features of level from the raw inputs. The UNSW-NB15 dataset was utilized to evaluate the anomaly detection model. The proposed model obtained 94.85% accuracy and 96.53% detection rate which is better than other compared techniques like GA-NB, GSO, and PSO for validation. The DNN-CSO model has performed well in detecting most of the attacks, and it is appropriate for detecting anomalies in the IoT network.

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Hospital data management is one of the functional parts of operations to store and access healthcare data. Nowadays, protecting these from hacking is one of the most difficult tasks in the healthcare system. As the user’s data collected in the field of healthcare is very sensitive, adequate security measures have to be taken in this field to protect the networks. To maintain security, an effective encryption technology must be utilised. This paper focuses on implementing the elliptic curve cryptography (ECC) technique, a lightweight authentication approach to share the data effectively. Many researches are in place to share the data wirelessly, among which this work uses Electronic Medical Card (EMC) to store the healthcare data. The work discusses two important data security issues: data authentication and data confidentiality. To ensure data authentication, the proposed system employs a secure mechanism to encrypt and decrypt the data with a 512-bit key. Data confidentiality is ensured by using the Blockchain ledger technique which allows ethical users to access the data. Finally, the encrypted data is stored on the edge device. The edge computing technology is used to store the medical reports within the edge network to access the data in a very fast manner. An authenticated user can decrypt the data and process the data at optimum speed. After processing, the updated data is stored in the Blockchain and in the cloud server. This proposed method ensures secure maintenance and efficient retrieval of medical data and reports.