The Internet of Things (IoT) revitalizes the world with tremendous capabilities and potential to be utilized in vehicular networks. The Smart Transport Infrastructure (STI) era depends mainly on the IoT. Advanced machine learning (ML) techniques are being used to strengthen the STI smartness further. However, some decisions are very challenging due to the vast number of STI components and big data generated from STIs. Computation cost, communication overheads, and privacy issues are significant concerns for wide-scale ML adoption within STI. These issues can be addressed using Federated Learning (FL) and blockchain. FL can be used to address the issues of privacy preservation and handling big data generated in STI management and control. Blockchain is a distributed ledger that can store data while providing trust and integrity assurance. Blockchain can be a solution to data integrity and can add more security to the STI. This survey initially explores the vehicular network and STI in detail and sheds light on the blockchain and FL with real-world implementations. Then, FL and blockchain applications in the Vehicular Ad Hoc Network (VANET) environment from security and privacy perspectives are discussed in detail. In the end, the paper focuses on the current research challenges and future research directions related to integrating FL and blockchain for vehicular networks.

The world of the Internet and networking is exposed to many cyber-attacks and threats. Over the years, machine learning models have progressed to be integrated into many scenarios to detect anomalies accurately. This paper proposes a novel approach named cloud-based anomaly detection (CAD) to detect cloud-based anomalies. CAD consist of two key blocks: ensemble machine learning (EML) model for binary anomaly classification and convolutional neural network long short-term memory (CNN-LSTM) for multiclass anomaly categorization. CAD is evaluated on a complex UNSW dataset to analyze the performance of binary anomaly detection and categorization of multiclass anomalies. Furthermore, the comparison of CAD with other machine learning conventional models and state-of-the-art studies have been presented. Experimental analysis shows that CAD outperforms other studies by achieving the highest accuracy of 97.06% for binary anomaly detection and 99.91% for multiclass anomaly detection.

Call record analysis is the most critical task for the Law Enforcement Agencies (LEAs) in a cyber-investigation process. It provides valuable information in the investigation, such as time and date and the duration of incoming and outgoing calls. The technological advancement of smartphones and the versatility of Instant Messaging (IM) applications provide multiple communication channels to cybercriminals for communication, making it difficult for the LEAs to monitor/investigate using traditional forensics tools and techniques. The most challenging part is to retrieve specific information from the network traffic of a particular IM Application such as WhatsApp. This research article’s primary purpose is to find the IP address of the cybercriminal using WhatsApp through existing sniffing techniques and tools. A method called rule-based extraction for sniffing packets is proposed for extracting the most relevant data from the network traffic. The results support LEAs to identify the cybercriminals’ specific traffic and help in analyzing and comparing the mobile phone data with the network traffic.

Electronic Health Records (EHRs) are the very personal data and ensuring its privacy and security is an utmost priority. Various laws require the privacy of this data to be ensured and usually this is achieved using strict access control methods. However, these methods have limitations, specifically in case of a server breach. In this paper, we use CryptDB to ensure data confidentiality in EHR Systems. In particular, we investigate the performance of CryptDB with OpenEMR, on different deployment scenarios and varying workloads over the cloud and a local testbed. We identify that CryptDB successfully provides the data confidentiality on the database server when deployed on the cloud. We also find that for a mix workload, the average performance of the OpenEMR with CryptDB in the cloud remains under two seconds which makes CryptDB a viable option for providing security to EHR systems deployed in the cloud. This is the first study to integrate CryptDB with OpenEMR and to profile performance overhead to ensure the data confidentiality under different deployment and varying workload scenarios in the cloud.