Vehicular ad hoc networks (VANET) are also known as intelligent transportation systems. VANET ensures timely and accurate communications between vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) to improve road safety and enhance the efficiency of traffic flow. Due to its open wireless boundary and high mobility, VANET is vulnerable to malicious nodes that could gain access into the network and carry out serious medium access control (MAC) layer threats, such as denial of service (DoS) attacks, data modification attacks, impersonation attacks, Sybil attacks, and replay attacks. This could affect the network security and privacy, causing harm to the information exchange within the network by genuine nodes and increase fatal impacts on the road. Therefore, a novel secure trust-based architecture that utilizes blockchain technology has been proposed to increase security and privacy to mitigate the aforementioned MAC layer attacks. A series of experiment has been conducted using the Veins simulation tool to assess the performance of the proposed solution in the terms of packet delivery ratio (PDR), end-to-end delay, packet loss, transmission overhead, and computational cost.

The revolutionary idea of the internet of things (IoT) architecture has gained enormous popularity over the last decade, resulting in an exponential growth in the IoT networks, connected devices, and the data processed therein. Since IoT devices generate and exchange sensitive data over the traditional internet, security has become a prime concern due to the generation of zero-day cyberattacks. A network-based intrusion detection system (NIDS) can provide the much-needed efficient security solution to the IoT network by protecting the network entry points through constant network traffic monitoring. Recent NIDS have a high false alarm rate (FAR) in detecting the anomalies, including the novel and zero-day anomalies. This paper proposes an efficient anomaly detection mechanism using mutual information (MI), considering a deep neural network (DNN) for an IoT network. A comparative analysis of different deep-learning models such as DNN, Convolutional Neural Network, Recurrent Neural Network, and its different variants, such as Gated Recurrent Unit and Long Short-term Memory is performed considering the IoT-Botnet 2020 dataset. Experimental results show the improvement of 0.57–2.6% in terms of the model’s accuracy, while at the same time reducing the FAR by 0.23–7.98% to show the effectiveness of the DNN-based NIDS model compared to the well-known deep learning models. It was also observed that using only the 16–35 best numerical features selected using MI instead of 80 features of the dataset result in almost negligible degradation in the model’s performance but helped in decreasing the overall model’s complexity. In addition, the overall accuracy of the DL-based models is further improved by almost 0.99–3.45% in terms of the detection accuracy considering only the top five categorical and numerical features.

Autonomous Vehicular Ad hoc Networks (A-VANET) is also known as intelligent transportation systems. A-VANET ensures timely and accurate communications between vehicle to vehicle and Vehicle to Roadside Unit (RSU) to improve road safety and enhance the efficiency of traffic flow. Due to open wireless boundary and high mobility, A-VANET is vulnerable to several security threats especially impersonation, denial of service, pollution attacks. This paper presents a novel Received Signal Strength Indicator (RSSI) based public key infrastructure (PKI) to address the above-mentioned attacks. Each incoming signal will be authenticated based on RSSI value and digital signal (obtained using PKI) is utilized for cryptography and communication within the insecure channel. The proposed solution is verified with and without the presence of attacker by evaluating the packet delivery ratio and packet overhead.

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