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In this study, we focus on real-time anomaly detection using the gated graph neural network (GGNN) and long short-term memory (LSTM) algorithms for the most commonly used network protocols of HTTP and TELNET. In a network, the flow of the various protocols is determined by their respective roles. Each protocol consists of options with individual tasks, and protocols are processed based on these tasks. Therefore, when analyzing flow, a unique repetitive pattern emerges according to the flow and mission. Accordingly, when an anomaly signal is involved, the pattern shows different characteristics from the existing pattern. This study identifies the flow of input and output values based on the correlation between the options defined at each port, whereby the correlation is analyzed, and the detection accuracy of the anomaly signal is determined using the GGNN and LSTM algorithms. The experimental results demonstrate an accuracy of 99.36% for GGNN and 89.46% for LSTM in detecting network anomalies. [ABSTRACT FROM AUTHOR]

Vehicular Internet of Things (VIoT) refers to integrating Internet of Things (IoT) technology into the transportation sector, specifically vehicles. It aims to enhance transportation safety, efficiency, and sustainability by using connected devices and real-time data exchange between vehicles, road infrastructure, and transportation management systems. RFID can provide valuable insights into the functioning and performance of vehicles, enabling the development of intelligent transportation systems and contributing to the growth of the VIoT. To secure essential driving data tag location, data privacy, and road safety are the primary concerns in the VIoT system. An attacker can perform a variety of attacks on the tag and compromise its privacy through the wireless channel between it and the reader. To ensure secure communication for the various application of the VIoT system, An RFID-based protocol is proposed, which is referred to as UPSRVNet (Ultralightweight, Privacy preserved, and Secure RFID-based authentication protocol for VIoT Networks). The proposed protocol guarantees secure authentication in VIoT networks using ultralightweight, privacy-preserving RFID tags by substituting bit formation and right-shift rotation for high-computing operations like hash functions and encryption/decryption algorithms. It also reduces RFID tags' storage and communication overhead and aims to secure the driver's location, personal information, and resistance from known attacks. As per the experiment result, the proposed UPSRVNet protocol showed a significant reduction in communication overhead between tag and reader, that is 50%, and the storage cost on the tag is also reduced by 33.33% compared to existing protocols. Due to this, the certification and authentication processes may be performed quickly. The protocol ensured the security requirements by informal analysis using the Scyther tool. A comprehensive analysis of the proposed protocol reveals its substantial advantages over the existing protocol concerning computation complexity, communication efficacy, and storage expenditure. [ABSTRACT FROM AUTHOR]