Vehicular ad hoc networks (VANETs) are an important communication paradigm in modern-day mobile computing for exchanging live messages regarding traffic congestion, weather conditions, road conditions, and targeted location-based advertisements to improve the driving comfort. In such environments, security and intelligent decision making are two important challenges needed to be addressed. In this paper, a trusted authority (TA) is designed to provide a variety of online premium services to customers through VANETs. Therefore, it is important to maintain the confidentiality and authentication of messages exchanged between the TA and the VANET nodes. Hence, we address the security problem by focusing on the scenario where the TA classifies the users into primary, secondary, and unauthorized users. In this paper, first, we present a dual authentication scheme to provide a high level of security in the vehicle side to effectively prevent the unauthorized vehicles entering into the VANET. Second, we propose a dual group key management scheme to efficiently distribute a group key to a group of users and to update such group keys during the users' join and leave operations. The major advantage of the proposed dual key management is that adding/revoking users in the VANET group can be performed in a computationally efficient manner by updating a small amount of information. The results of the proposed dual authentication and key management scheme are computationally efficient compared with all other existing schemes discussed in literature, and the results are promising.

Intrusion detection systems assume a noteworthy job in the provision of security in wireless Sensor networks. The existing intrusion detection systems focus only on the detection of the known types of attacks. However, it neglects to recognise the new types of attacks, which are introduced by malicious users leading to vulnerability and information loss in the network. In order to address this challenge, a new intrusion detection system, which detects the known and unknown types of attacks using an intelligent decision tree classification algorithm, has been proposed. For this purpose, a novel feature selection algorithm named dynamic recursive feature selection algorithm, which selects an optimal number of features from the data set is proposed. In addition, an intelligent fuzzy temporal decision tree algorithm is also proposed by extending the decision tree algorithm and integrated with convolution neural networks to detect the intruders effectively. The experimental analysis carried out using KDD cup data set and network trace data set demonstrates the effectiveness of this proposed approach. It proved that the false positive rate, energy consumption, and delay are reduced in the proposed work. In addition, the proposed system increases the network performance through increased packet delivery ratio.

The Internet of Things (IoT) is a distributed system which is made up of the connections of smart objects (things) that can continuously sense the events in their sensing domain and transmit the data via the Internet. IoT is considered as the next revolution of the Internet since it has provided vast improvements in day‐to‐day activities of humans including the provision of efficient healthcare services and development of smart cities and intelligent transport systems. The IoT environment, by the application of suitable security mechanisms through efficient security management techniques, intrusion detection systems provide a wall of defence against the attacks on the Internet and on the devices connected with Internet by effective monitoring of the Internet traffic. Therefore, the intrusion detection system (IDS) is a resolution proposed by the researchers to monitor and secure the IoT communication. In this work, a meticulous analysis of the security of IoT networks based on quality‐of‐service metrics is performed for deploying intrusion detection systems by carrying out experiments on secured communication and measuring the network’s performance based on comparing them with the existing security metrics. Finally, we propose a new and effective IDS using a deep learning‐based classification approach, namely, fuzzy CNN, for improving the security of communication. The major and foremost advantages of this system include an upsurge in detection accuracy, the accurate detection of denial of service (DoS) attacks more efficiently, and the reduction of false positive rates.