Abstract

The Smart Power Grid (SPG) is pivotal in orchestrating and managing demand response in contemporary smart cities, leveraging the prowess of Information and Communication Technologies (ICTs). Within the immersive SPG environment, the ubiquitous deployment of smart meters stands as a testament to their paramount importance in the realm of vigilant monitoring and oversight. These smart meters are installed on high-tension electricity lines and transmit information about electricity outages and other issues to the utility center. To access services from utility centers, smart meters need to communicate securely over a public channel, even though the network itself is insecure. However, potential attacks from adversaries (Ad) can exploit this communication. Therefore, protecting this communication is of utmost importance. Several privacy-preserving authentication protocols designed for SPG have been introduced in the literature. Nevertheless, a significant number of these protocols exhibit vulnerabilities to diverse security attacks. This article introduces a lightweight and anonymous authentication protocol specifically designed to address these concerns in the SPG environment. Our protocol ensures both security and efficiency, surpassing other comparable protocols in terms of its lightweight nature. By employing both formal and informal analysis, we showcase the robustness of our protocol against significant attacks while remaining lightweight. The proposed protocol provides added security features and incurs 23.8879% lower computation cost than related protocols. Consequently, our protocol is highly suitable for implementation in the SPG system.

Abstract

The use of Internet of Things (IoT) devices in Artificial Intelligence of Things (AIoT) applications has generated significant interest in recent years. AIoT security comes under cybersecurity, which focuses on protecting cloud-based, internet-connected hardware, also known as IoT devices and the networks they are connected to. As the waste of energy is one of the major issues in the world each day, we waste a lot of energy through various sources. A certain light-controlling mechanism can be implemented to reduce the unnecessary power consumption load over the sources. In this paper, a secure artificial Intelligence of Things (AIoT)-enabled authenticated key agreement technique for a smart living environment is proposed (in short, SAIoT-SL). The unique feature of the proposed SAIoT-SL is that it provides secure data transmissions throughout the communication of servers (i.e., cloud servers), users, and sensors. The conducted security analysis and formal security verification (through Scyther tool) prove the resilience of the proposed SAIoT-SL against various potential attacks. In the end, a practical implementation of the proposed SAIoT-SL is also provided to check its impact on real-world scenarios. In the conducted comparative study, it has been identified that the proposed SAIoT-SL outperformed the other existing techniques.

Abstract

The Internet of Drones (IoD) extends the capabilities of unmanned aerial vehicles, enabling them to participate in a connected network. In IoD infrastructure, drones communicate not only among themselves but also with users and a control center. This interconnected communication framework holds promise for various applications, from collaborative decision-making to real-time data exchange. However, the expansion of communication in IoD also introduces new challenges, particularly in terms of security, privacy and authentication. Unfortunately, the current authentication protocols are inadequate in offering robust security features against various attacks in the IoD environment. To address these security issues and limitations, we proposed a secure protocol for the IoD environment using chaotic maps and hash functions. In addition, we also employed a physically unclonable function in the development of the proposed protocol. We assess the security of the protocol through both informal and formal security analysis. The formal security analysis is conducted through a widely used random or real (RoR) model. The informal analysis shows the rigorous security features against various attacks, such as masquerading, anonymity violation, and physical cloning attacks. Moreover, we compare the performance of the devised protocol with similar existing protocols across important performance parameters, such as communication overhead, computation overhead, and security features. The devised protocol provides a 67.86% and 17.80% reduction in computation and communication overheads, respectively, as compared to related protocols. The analysis demonstrates the proposed protocol’s capacity to support secure communication in the IoD environment and satisfy desirable security attributes.

Abstract

With the emergence, development, and maturity of various Internet technologies, the healthcare industry is also trying to integrate with these technologies to provide more convenient healthcare services to patients by establishing telemedicine to develop and continuously improve the public healthcare system. Various protocols were proposed in the recent past to provide attack resistance in addition to computational efficiency; however, several such protocols were proved inefficient or prone to one or more attacks. Some of these protocols lack user anonymity and privacy. Keeping in consideration the flaws of existing protocols, in this article, we propose an efficient and secure protocol based on extremely lightweight symmetric key operations. In addition, we provide formal and informal analyses to demonstrate the protocol’s security. Moreover, to measure the efficiency of the proposed protocol, we conducted a real-time experiment, which confirms that the proposed protocol completes an authentication round in 117.1071 ms with an exchange of four messages and 2592 bits among the three participating entities. Finally, by comparing it with other protocols, we show that the proposed protocol has significant security advantages and performance benefits.

Abstract

With the rapid advancements in the Internet of Things (IoT), edge computing has a significant role in many eHealth applications. However, security and data privacy are major challenges due to the widespread popularity of the digital health domain. The network and data storage should withstand adversarial entities and allow access to only legitimate users. Medical users and servers must be registered with a trusted third-party authority to obtain permissions to authenticate remaining users. Millions of smart medical devices connect online to collect critical patient information, analyze reports, and perform meaningful decisions without human interaction. In this scenario, standard security is essential to safeguard eHealth applications. This research provides a secured, lightweight mobile edge computing framework to address these issues. The empirical results show that our framework mitigates computational overheads. The informal and formal analysis shows that our framework withstands potential attacks.

Abstract

Deepfake refers to synthetic media generated through artificial intelligence (AI) techniques. It involves creating or altering video, audio, or images to make them appear as though they depict something or someone else. Deepfake technology advances just like the mechanisms that are used to detect them. There’s an ongoing cat-and-mouse game between creators of deepfakes and those developing detection methods. As the technology that underpins deepfakes continues to improve, we are obligated to confront the repercussions that it will have on society. The introduction of educational initiatives, regulatory frameworks, technical solutions, and ethical concerns are all potential avenues via which this matter can be addressed. Multiple approaches need to be combined to identify deepfakes effectively. Detecting deepfakes can be challenging due to their increasingly sophisticated nature, but several methods and techniques are being developed to identify them. Mitigating the negative impact of deepfakes involves a combination of technological advancements, awareness, and policy measures. In this paper, we propose a secure deepfake mitigation framework. We have also provided a security analysis of the proposed framework via the Scyhter tool-based formal security verification. It proves that the proposed framework is secure against various cyber attacks. We also discuss the societal impact of deepfake events along with its detection process. Then some AI models, which are used for creating and detecting the deepfake events, are highlighted. Ultimately, we provide the practical implementation of the proposed framework to observe its functioning in a real-world scenario.

Abstract

Critical infrastructure refers to the key systems, assets, and facilities, whether they are physical or virtual, that are necessary for the overall functioning of a country. As our reliance on technology and networked systems continues to expand, so does the significance of taking precautions to protect critical infrastructure from being compromised by cyberattacks. We need some security schemes to secure the communication happening in the critical infrastructure devices. Therefore, in this paper, we focus on the design of an authentication and key establishment scheme, which is used in the critical infrastructure to secure its data transmissions. A secure authentication and key management mechanism for fog computing-based IoT-driven critical infrastructures (in short, AKM-FCCI) is proposed in the paper. We then provide the network model and threat model of the proposed AKM-FCCI to explain its deployment and organization of devices and servers. The threat model further explains the various threats of this communication environment. In addition, the security analysis of AKM-FCCI is presented to demonstrate that it is secure against the many different kinds of attacks that could be launched against it. The comparisons demonstrate that the performance of AKM-FCCI is superior to that of the other currently used schemes. In addition to that, it offers a high level of security and utility. Therefore, the proposed AKM-FCCI is appropriate for use in protecting critical infrastructure equipment against a wide variety of threats.

Abstract

In the present era, the increasing number of network devices and ubiquitous computing leads to the flow of enormous amounts of data traffic, including sensitive and confidential information, on the internet and carrying out commercial and banking transactions online. This gives cybercriminals an opportunity to launch an attack like phishing to steal confidential information from the user and gain unauthorized access. This can be mitigated by the help of an intelligent machine learningbased phishing detection system, which can detect potential phishing attacks and take appropriate action. In this paper, we have addressed this major cyber issue and proposed a machine learning-based phishing detection scheme (in short, EM-PAD), which is trained on a benchmark dataset and evaluated on standard metrics: F1-score and Accuracy. The proposed model is compared with different existing schemes based on Accuracy, indicating that it has outperformed them with remarkable results.

Abstract

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Abstract

Maritime communication helps vessels and ports plan their movements, exchange environmental information, and communicate among themselves. The vessels' movement and changing location are critical to keep them secure from data interception and data tampering by unauthorized parties during transmission. To secure maritime communication, we propose a novel lightweight authentication scheme sensitive to the current ship location. We assess the effectiveness of the proposed protocol in defending against a range of security threats while keeping communication and computation costs low, and meeting the desired security and functional requirements of anonymity and untraceability. The detailed security analysis using the widely accepted Scyther tool demonstrates that location-based keys as proposed in our protocol are secure against location inference and spoofing attacks among others.