ABSTRACT Narrowband Internet of Things (NB‐IoT) is LPWAN operating using narrowband spectrum in IoTs, requiring low data rates and long battery life. Since NB‐IoT does not support handover, the requirement to sustain network connectivity during mobility may result in fake base station connections, hence applications are limited to stationary use‐cases. Researchers propose extending NB‐IoT in mobile applications, owing to higher signal quality and battery life, despite DoS attacks due to low bandwidth. As NB‐IoT is resource‐constrained, resources must be allocated based on application with data processing offloaded to optimise performance. Cloud servers, being centralised and memory‐intensive, may result in increased computational delay, lower throughput, and DoS attacks in NB‐IoT. Hence, in this paper, we implement fog computing, a decentralised technology, providing scalability, reduced bandwidth, and enhanced privacy, to provide distributed processing. Additionally, we develop secure handover protocols for private and service‐provider‐controlled fog networks under normal and cell‐splitting conditions to minimise fake base station attacks and provide seamless handover. We introduce reputation‐based mechanisms to determine device integrity and differentiate faulty behaviour and attacks. Further, we implement real‐time application‐aware resource allocation and QoS‐based load‐balancing using deep learning to distribute data processing between devices, fog and cloud servers. We simulate and prototype protocols on iFogSim2 and Raspberry Pi 4. Security of the fog computing framework is validated against various attacks and formally verified using Scyther. Evaluation shows that our approach consumes 12% and 43.75% lower power and communication overhead and approximately 6 and 16 times lower execution time and memory compared with existing solutions, thus making our approach lightweight.

The Internet of Things (IoT) is a network of interconnected devices and systems that collect and exchange data. Resource-constrained IoT devices are particularly susceptible to attacks due to their widespread use and often inadequate security setups, leading to potential vulnerabilities in individual devices. Traditional Signature-based Intrusion Detection Systems (SIDS) are insufficient for the dynamic IoT landscape, where new devices and protocols continually emerge. Anomaly-based IDS systems(AIDS), which detect deviations from normal behavior, are better suited for IoT environments as they provide continuous monitoring and real-time detection of malicious activities, enhancing threat intelligence and reducing false positives. In this research, we empirically analyze anomalybased IDS systems using various machine learning techniques deployed on Raspberry Pi. The effectiveness of the system is evaluated in terms of detection accuracy, computational efficiency, and resource utilization. Power consumption is measured using a source meter and CPU usage is monitored with the Glances software. This study demonstrates that Random Forest is the most balanced machine learning algorithm for anomaly-based IDS on IoT devices, offering high accuracy of 98.2% with efficient resource utilization (an average energy consumption of 40.94 Joules, peak CPU utilization of 35%, and average power consumption of 3.24 watts), paving the way for future research in adaptive and scalable intrusion detection.

In the realm of modern transportation, Electric Vehicles (EVs) have emerged as a promising solution to address environmental concerns and reduce dependence on fossil fuels. However, the integration of advanced technologies and connectivity features in EVs makes their charging networks vulnerable to various cyber threats and vulnerabilities, potentially compromising their functionality, reliability, and security. In response to these challenges, our research proposes a novel security solution that leverages human biometrics, specifically fingerprint recognition, to safeguard against cyberattacks such as identity theft, man-in-the-middle attacks, and unauthorized access to EV charging stations. We conduct a brute force attack against the proposed Biometric Multi-Factor Authentication (MFA) method and the conventional authentication mechanism to demonstrate the reliability of our proposed mechanism. By combining biometric verification with existing authentication methods, our approach aims to strengthen the security of EV charging networks. To evaluate its effectiveness, we conduct a comparative analysis of our proposed method against current authentication techniques, based on certain criteria, such as success rate, time to breach, and power consumption. The conventional method showed a success rate of 35 %, with the initial breach occurring in just 5 seconds, underscoring a significant vulnerability. In contrast, the proposed biometric-based MFA method proved to be resistant to brute-force attacks. Our findings demonstrate the feasibility and robustness of our biometric-based solution, offering a promising enhancement to the security framework of EV charging infrastructures.

The Industrial Internet of Things (IIoT) enhances the benefit of the Internet of Things (IoT) to a higher level, especially in industries where human error can lead to catastrophic effects. However, security is a major concern in IIoT as hackers can gain access to connected systems, thus potentially subjecting operations to a shutdown. Besides, the outbreak of the COVID-19 pandemic changed the operations style of organizations into a remote work model. Consequently, there has been a significant increase in cyber-attacks leveraging vulnerabilities of IoT devices connected to the Internet. Considering the above factors, we propose a method of remote user authentication combining Photo Response Non-Uniformity (PRNU) with fingerprint bio-metric, which can prevent attacks. PRNU uniquely identifies the scanner, thereby authenticates the device of the user. To prove the effectiveness of PRNU, we collect fingerprint images from various scanners prototyped using Raspberry Pi and evaluate the performance. Our performance evaluation with a set of 10 fingerprint scanners shows promising results. Moreover, our analysis shows that the proposed scheme achieves a classification accuracy of 99%.

Critical Infrastructures are one of the vital systems that support modern societies. The adoption of technologies like Industry 4.0, Industrial Internet of Things (IIoT) in critical infrastructures have made it a lucrative target for cyberattackers. Protecting critical infrastructure is of paramount importance due to the sensitive nature of the data coupled with the resource-constrained nature of the devices. The advent of blockchains can be a significant enabler for protecting critical infrastructure through the use of immutable ledger for storing the operations. However, blockchains are computationally expensive, have limited scalability and incur significant delays in processing transactions thus necessitating the development of a lightweight platform while retaining the functionality. This paper develops a lightweight blockchain based framework for protecting critical infrastructure by leveraging its hierarchical nature. Evaluation using embedded devices shows that our proposed framework minimizes the execution time of blockchain operations thus making it suitable for protecting critical infrastructure. Finally, our proposed framework is generic, in that it can be applied to any of the domains operating in the critical infrastructure.

Embedded devices in smart homes have become increasingly vulnerable to numerous security and privacy threats. Over the past few years, devices such as Smart Cameras have been used to launch Distributed Denial of Service (DDoS) attacks wherein attackers exploit weakly configured IoT devices and inject malicious code after discovering their credentials. Conventional methods used to identify anomalies cannot be applied due to the resource-constrained and heterogeneous nature of the IoT devices. To address this, a novel approach that leverages the power consumption of these devices needs to be devised. Towards this goal, a smart home scenario is simulated using Smart Cameras and brute-force, and DDoS attacks were launched to capture variations in power profile. Further, we develop machine learning models to detect anomalies based on the power consumption traces. Our proposed approach achieves an accuracy of 94.04% towards detecting the presence of anomalies. Our analysis reveals that power consumption is a promising factor that can be used to detect anomalies in IoT based smart homes.

Internet of Things (IoTs) offers a plethora of opportunities for remote monitoring and communication of everyday objects known as things with applications in numerous domains. The advent of blockchains can be a significant enabler for IoTs towards conducting and verifying transactions in a secure manner. However, applying blockchains to IoTs is challenging due to the resource constrained nature of the embedded devices coupled with significant delay incurred in processing and verifying transactions in the blockchain. Thus there exists a need for profiling the energy consumption of blockchains for securing IoTs and analyzing energy-performance trade-offs. Towards this goal, we profile the impact of workloads based on Smart Contracts and further quantify the power consumed by different operations performed by the devices on the Ethereum platform. In contrast to existing approaches that are focused on performance, we characterize performance and energy consumption for real workloads and analyse energy-performance trade-offs. Our proposed methodology is generic in that it can be applied to other platforms. The insights obtained from the study can be used to develop secure protocols for IoTs using blockchains.

As sensor‐related technologies have been developed, smartphones obtain more information from internal and external sensors. This interaction accelerates the development of applications in the Internet of Things environment. Due to many attributes that may vary the quality of the IoT system, sensor manufacturers provide their own data format and application even if there is a well‐defined standard, such as ISO/IEEE 11073 for personal health devices. In this paper, we propose a client‐server‐based sensor adaptation layer for an Android platform to improve interoperability among nonstandard sensors. Interoperability is an important quality aspect for the IoT that may have a strong impact on the system especially when the sensors are coming from different sources. Here, the server compares profiles that have clues to identify the sensor device with a data packet stream based on a modified Boyer‐Moore‐Horspool algorithm. Our matching model considers features of the sensor data packet. To verify the operability, we have implemented a prototype of this proposed system. The evaluation results show that the start and end pattern of the data packet are more efficient when the length of the data packet is longer.

Windows Azure Storage (WAS) is a cloud storage system that provides customers the ability to store seemingly limitless amounts of data for any duration of time. WAS customers have access to their data from anywhere at any time and only pay for what they use and store. In WAS, data is stored durably using both local and geographic replication to facilitate disaster recovery. Currently, WAS storage comes in the form of Blobs (files), Tables (structured storage), and Queues (message delivery). In this paper, we describe the WAS architecture, global namespace, and data model, as well as its resource provisioning, load balancing, and replication systems.