Abstract

The relentless advancements in Cyber-Physical Systems (CPS) and Wireless Sensor Networks (WSN) have paved the way for various practical applications across networking, public safety, smart transportation, and industrial sectors. The Industrial Internet of Things (IIoT) integrates these technologies into complex, interconnected environments where vast amounts of data are transmitted between devices and systems. However, the inherent openness of communication channels in IIoT systems introduces distinctive security and privacy vulnerabilities, where malevolent entities can effortlessly intercept, forge, or delete communication messages. These vulnerabilities are exacerbated by the critical nature of industrial applications, where breaches can lead to significant operational disruptions or safety hazards To address these challenges, several authentication protocols have been proposed. Nevertheless, many of these protocols remain susceptible to various security attacks. To ensure privacy and security in IIoT environments, we introduce a robust and efficient authentication protocol for a WSN-based IIoT environment. This protocol preserves the privacy of information transmitted among all entities and provides an effective solution for securing this sensitive information. Additionally, we present a detailed security analysis of the proposed protocol to formally and informally demonstrate its security strength. The performance analysis is carried out to compare the proposed protocol against existing related protocols, with results unequivocally demonstrating that our protocol offers enhanced privacy and security with reduced costs.

Abstract

— Multi-domain vehicle to grid (V2G) is a network environment in which numerous service providers offer charging and discharging services to EV users. This can enhance energy management and traffic flow for efficient intelligent transportation systems (ITS). However, the combination of multiple domains can suffer from various security vulnerabilities, highlighting the need for robust countermeasures. Moreover, existing multidomain V2G protocols utilized a central trusted authority (TA) which can create a single point of failure (SPOF), or required high computational resources. In this paper, we propose a multi-domain authentication protocol for secure and efficient V2G services using consortium blockchain. The proposed protocol provides lightweight intra-domain authentication using hash functions and XOR operators. Furthermore, the proposed protocol ensures secure cross-domain authentication by integrating elliptic curve cryptography (ECC) and physical unclonable function (PUF). Therefore, the proposed protocol can establish trust, enable efficient communications, and prevent congestion at charging stations. To validate security robustness, comprehensive evaluations are conducted using “Real-Or-Random (ROR) model”, “Scyther tool”, and informal analyses. Comparative computational overheads of the proposed and related protocols are measured using “Multiprecision Integer and Rational Arithmetic Cryptographic Library (MIRACL)” testbed experiments. Additionally, a simulation of the practical deployment is conducted using “Network Simulator-3 (NS-3)”. Results indicate that the proposed protocol can improve ITS by providing secure and efficient services for multi-domain V2G environments

Abstract

Unmanned aerial vehicles (UAVs) integrated with the internet of things (IoT) guarantee useful advantages such as facilitating ground communications in regions where the availability of connectivity is restricted owing to physical obstacles. However, the data transmitted by sensors and IoT embedded in UAVs are facing new security issues and privacy challenges with the known security attacks over time. To address these security attacks and threats and meet lightweight UAV communication requirements, a secure and lightweight authentication and key agreement (AKA) scheme is essential. Recently, researchers have designed a lightweight blockchain-enabled AKA scheme with privacy-preserving for UAVs to provide useful and reliable services. However, we prove that the existing scheme is fragile to various security attacks and does not ensure mutual authentication. Thus, we propose a robust and lightweight blockchain-based AKA scheme for PUF-enabled UAVs, called RLBA-UAV to enhance the security problems of the existing scheme. We demonstrate the security of RLBA-UAV by using informal/formal security analyses such as the ROR oracle model and AVISPA simulation. Moreover, we demonstrate the performance comparison analysis between RLBA-UAV and related schemes for UAVs. We demonstrate an implementation of a network simulator (NS) 3 compliant with IEEE 802.11p standards to show its validation and feasibility that RLBA-UAV is appropriate for practical UAVs. Thus, RLBA-UAV offers enhanced security and operational efficiency compared to related schemes and can be applied to practical blockchain-based AKA systems for UAVs.

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

In the realm of smart city surveillance, consumer electronics (CE) devices communicate through vulnerable wireless channels, posing significant security risks. To ensure secure and uninterrupted services for residents, continuous monitoring is imperative. Existing authentication methods for CE in smart cities authenticate users/devices at the onset of a communication session, assuming they remain authenticated throughout. However, temporary access by an attacker to the user's or CE device can lead to impersonation. To mitigate this security threat, we propose a continuous authentication (CA) protocol utilizing vector similarity search (VSS), which has emerged as a novel approach to enhance security and privacy in CE deployed within smart city surveillance systems. Through the employment of VSS techniques and secure communication via session key establishment, the proposed scheme continuously monitors user behavior and verifies legitimacy, ensuring seamless operation and protection against a myriad of potential threats. This innovative framework enhances the resilience of smart city infrastructures against unauthorized access, data tampering, data poisoning attacks, and other malicious activities.

Abstract

No Results Found

Abstract

The rise of smart cities and the increasing demand for drones has sparked considerable interest in the Internet of Drones (IoD) within the realms of academia and industry. IoD presents a multitude of advantages in smart city settings, facilitating services like traffic monitoring, environmental surveillance, and disaster management by harnessing the potential of IoT and Flying Ad-Hoc Networks (FANET) infrastructures. However, the transmission of sensitive messages between drones in IoD-based smart cities is disseminated over insecure channels, leaving them exposed to security vulnerabilities. Furthermore, drones operating in IoD architectures are prone to physical capture attacks as they operate in unattended environments with minimal human intervention. Moreover, the limited resources of drones pose challenges to the practicality of employing computationally intensive cryptographic methods. In response to these challenges, we introduce PAF-IoD, an authentication framework that prioritizes security and efficiency. PAF-IoD leverages physical unclonable functions (PUFs) and the AEGIS authenticated encryption scheme to guarantee trustworthy and secure communication between users and drones in smart cities. In terms of security validation, we perform both random and real model-based formal analyses. Furthermore, we employ the Scyther tool to ensure the resilience of PAF-IoD against different security vulnerabilities. Additionally, an informal analysis is conducted to demonstrate the resilience of PAF-IoD against various attacks. By introducing PAF-IoD, we offer a secure solution that addresses vulnerabilities and resource limitations associated with drone communication. The proposed framework guarantees the integrity and confidentiality of data while optimizing computational and communication resources, thereby enabling reliable and effective IoD operations in smart cities.

Abstract

—Smart Microfactories use additive manufacturing to create products with mixed materials and variable sizes. Digital twin technology enhances control of the additive manufacturing equipment in these factories, increasing productivity and minimizing errors. The digital twins communicate with the machines to furnish sensitive data and instructions, which must be protected from tampering. Authentication rescues the digital and physical twins from menacing attacks such as privileged insider, impersonation, Ephemeral Secret Leakage (ESL) and Man-in-The-Middle (MiTM) attacks. To this end, we propose lightweight authentication among the digital and physical twins with the undeniability of issued commands and deniable key agreement. It achieves perfect forward secrecy, unforgeability and anonymity. Rigorous formal verification with AVISPA and informal security analysis show the robustness of this protocol to the aforesaid attacks.

Abstract

Big data analytics in the Internet of Things (IoT) realm demands a substantial volume of data for training models and making reliable inferences. In most cases, data availability is scarce, and synthetic data is generated from real-world data to meet the needs. Yet, there remains a risk of exposing private and sensitive information without proper data security measures. In this article, we aim to develop a secure collaborative model learning methodology trained on synthetic data, ensuring data availability, privacy and confidentiality through differential privacy and key management. Additionally, we propose a secured inference framework where user data, sent for inference to the deployed model is protected, preserving both the accuracy of the predicted data and the security of the input data. Our experimental evaluation, along with performance and security analysis, exhibits that our approach offers accuracy and scalability while maintaining privacy and security.

Abstract

—With the rise of Big data generated by Internet of Things (IoT) smart devices, there is an increasing need to leverage its potential while protecting privacy and maintaining confidentiality. Privacy and confidentiality in Big Data aims to enable data analysis and machine learning on large-scale datasets without compromising the dataset sensitive information. Usually current Big Data analytics models either efficiently achieves privacy or confidentiality. In this article, we aim to design a novel blockchain-enabled secured collaborative machine learning approach that provides privacy and confidentially on large scale datasets generated by IoT devices. Blockchain is used as secured platform to store and access data as well as to provide immutability and traceability. We also propose an efficient approach to obtain robust machine learning model through use of cryptographic techniques and differential privacy in which the data among involved parties is shared in a secured way while maintaining privacy and confidentiality of the data. The experimental evaluation along with security and performance analysis show that the proposed approach provides accuracy and scalability without compromising the privacy and security.