Software-defined vehicles (SDVs) leverage vehicle-to-everything (V2X) communication to enable advanced connectivity and autonomous driving capabilities. However, this increased interconnectivity also exposes them to cyber threats such as spoofing, denial-of-service attacks, and data manipulation, making intrusion detection systems (IDS) essential for ensuring SDV security and reliability. In this work, we propose a novel intrusion mitigation approach that integrates Advantage Actor-Critic (A2C) reinforcement learning with a Long Short-Term Memory (LSTM) network to detect anomalies and intrusions in V2X communications. The LSTM component captures temporal dependencies in V2X data, enhancing the model’s ability to identify emerging attack patterns, while the A2C framework dynamically adjusts defensive actions, including flagging, blocking or monitoring traffic, based on evolving threat levels. Experimental results demonstrate the model’s effectiveness, achieving high detection accuracy and sensitivity. Additionally, we analyze how the system adapts over time, becoming more confident in its decision-making and optimizing security enforcement. This work enhances SDV cybersecurity by introducing a learning-based adaptive intrusion response system aiming at mitigating threats in highly dynamic vehicular networks. © 2025 Copyright held by the owner/author(s).

Software-Defined Vehicles (SDVs) is a paradigm of the wider internet of autonomous vehicles where its features and functions are enabled and managed through software, allowing for continuous updates and enhancements throughout its lifecycle. Enabling this technology relies on secure and efficient authentication mechanisms to ensure trusted communication in Vehicle-to-Everything (V2X) networks, over-the-air (OTA) updates, and intra-vehicle Electronic Control Unit (ECU) communication. As SDVs progress to support complex applications such as autonomous driving, platooning, and edge-assisted vehicular computing, selecting an optimal authentication scheme becomes critical for maintaining security without compromising real-time performance. In this work, we present a comparative analysis of state-of-the-art cryptographic authentication techniques and evaluate the impact on authentication latency, energy consumption and computational overhead. This work highlights the effectiveness of different authentication strategies and provides insights into the optimal selection of cryptographic primitives for SDV applications, balancing security and power consumption. Index Terms-Authentication Protocols, Energy profiling, Software-defined Vehicles © 2025 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.

The Internet of Things (IoT) can be defined as the network of physical objects, or “things,” embedded with sensors and software for processing and exchanging data with other devices and ecosystems using the Internet as a medium. With its rapid growth over the past decade, it has permeated several application domains, including intelligent vehicular systems. The Internet of Autonomous Vehicles (IoAV) is a subset of IoT that envisions dynamic autonomous driving without human intervention. The dynamic nature of the environment in which autonomous vehicles operate introduces significant challenges, such as real-time communication and security vulnerabilities. These challenges cannot be directly addressed by standard cybersecurity solutions designed primarily for static IoT environments. In this work, we outline the various vulnerabilities of the IoAV systems, and we delve into the critical importance of adopting lightweight security protocols. These protocols are crucial to ensure robust protection while at the same time not jeopardizing the performance of the IoAV system. We also highlight the fast lightweight security protocols implemented on heterogeneous embedded, low-power, high-performance computing platforms as a viable solution to address these challenges. © The authors.

Due to cyber attacks targeting RFID systems, this paper briefly summarizes parts 3 and 4 of the ISO/IEC 14443 standard, which specify the initialization, selection, and trans-mission protocols in high-frequency RFID smart-card and reader communication. The communication has been modeled, and two experiments have been performed using a security protocol ana-lyzer tool called Scyther. The protocol verification results shows that implementing Random UID can prevent many RFID attacks, such as eavesdropping and replay attacks and successfully protect the cardholder's privacy. © 2023 IEEE.

The accelerated move toward the adoption of the Industrial Internet-of-Things (IIoT) paradigm has resulted in numerous shortcomings as far as security is concerned. One of the IIoT affecting critical security threats is what is termed as the false data injection (FDI) attack. The FDI attacks aim to mislead the industrial platforms by falsifying their sensor measurements. FDI attacks have successfully overcome the classical threat detection approaches. In this article, we present a novel method of FDI attack detection using autoencoders (AEs). We exploit the sensor data correlation in time and space, which in turn can help identify the falsified data. Moreover, the falsified data are cleaned using the denoising AEs (DAEs). Performance evaluation proves the success of our technique in detecting FDI attacks. It also significantly outperforms a support vector machine (SVM)-based approach used for the same purpose. The DAE data cleaning algorithm is also shown to be very effective in recovering clean data from corrupted (attacked) data. © 2014 IEEE.

The boom of Internet of Things (IoT) devices has brought along new security concerns which earlier were not thought of. This has expanded the potential for digital forensic investigators to gather rich evidence from these sources. The data on these IoT devices is not easily accessible due to the lack of proper techniques to investigate such devices. This paper presents a sophisticated non-invasive method to investigate IoT devices. The software activities running on a device can be inspected by observing the electromagnetic radiation emitted from the devices during the process. The objective of this project is to evaluate if it is possible to classify the software activities being run on an IoT device by performing an electromagnetic side-channel analysis (EM-SCA). This paper presents a methodology for analyzing the EM side-channels and classifying encryption algorithms being run on a Raspberry Pi 4. This work demonstrates that the cryptographic encryptions can be classified with over 95% accuracy by using deep neural network-based classifiers. © 2020 IEEE.

This Special Section on "Security, privacy, and trust for Industrial Internet of Things" of the IEEE Transactions on Industrial Informatics (TII) highlights the main research challenges in the industrial Internet of Things (IoT) security, privacy, and trust. The designated nine high-quality research articles cover a wide range of the special section theme, including innovative solutions and novel technologies. These articles are briefly summarized. © 2019 IEEE.

The Internet of Things (IoT) is rapidly becoming an integral component of the industrial market in areas such as automation and analytics, giving rise to what is termed as the Industrial IoT (IIoT). The IIoT promises innovative business models in various industrial domains by providing ubiquitous connectivity, efficient data analytics tools, and better decision support systems for a better market competitiveness. However, IIoT deployments are vulnerable to a variety of security threats at various levels of the connectivity and communications infrastructure. The complex nature of the IIoT infrastructure means that availability, confidentiality and integrity are difficult to guarantee, leading to a potential distrust in the network operations and concerns of loss of critical infrastructure, compromised safety of network end-users and privacy breaches on sensitive information. This work attempts to look at the requirements currently specified for a secure IIoT ecosystem in industry standards, such as Industrial Internet Consortium (IIC) and OpenFog Consortium, and to what extent current IIoT connectivity protocols and platforms hold up to the standards with regard to security and privacy. The paper also discusses possible future research directions to enhance the security, privacy and safety of the IIoT.

Internet of Things (IoT) technology has drawn the attention of the industry, where it has been able to, and still can, solve many industrial intractable issues. However, the emerging technology suffers from severe security shortcomings. Authentication is a cornerstone of IoT security, as it presents the measures of checking the legitimacy of communication entities. The Industrial IoT (IIoT) technology has special conditions, resulting from a lack of resources and a shortage of security skills. As far as we can tell, from the literature, IIoT user authentication has not been studied extensively. In 2017 Tai et al. presented an authenticated key agreement for IoT networks. Here we prove that Tai et al. scheme is susceptible to sever security weaknesses, such as; i. unknown key share attacks, ii. node capturing attacks, iii node information secrecy. In this research article, we offer an innovative IIoT user authentication protocol that can achieve secure remote user authentication without timestamping requiring precise synchronization, our protocol only needs Hashing and Xor-ing. We examine the efficiency of the presented protocol using Tmote Sky node over an MSP430 microcontroller using a COOJA simulator. we also show its correctness using the Scyther verification tool.