Chipless Radio Frequency Identification (RFID) tags are widely adopted due to their cost-effectiveness, lightweight design, and passive operation. However, their lack of computational capabilities makes them vulnerable to cloning and counterfeit attacks. This paper proposes a counterfeit detection framework that combines Differential Constellation Trace Figures (DCTFs) with machine learning techniques to address these challenges. Backscattered Time-Domain (TD) signals from seven identical chipless RFID tags were processed to generate DCTFs, which were enhanced using colormaps such as Turbo, Colorcube, and Prism to highlight subtle variations. Red, Green and Blue (RGB) color features were extracted across spatial (width and height), radial, and angular dimensions, forming a multi-dimensional dataset. Gaussian noise was added to simulate real-world conditions, with Signal-to-Noise Ratios (SNRs) ranging from 100 decibels (dB) to 0 dB. Machine learning models, Support Vector Machine (SVM) and Random Forest (RForest), were trained to classify authentic and counterfeit tags. RForest demonstrated superior performance, achieving an accuracy of 99.62% and Area Under the Receiver Operating Characteristic curve (ROC–AUC) of 99.18% with multi-dimensional input data at 70 dB SNR noise.

Wildfires pose a serious hazard to both life and the environment, necessitating accurate and timely detection methods to minimize their impact. However, traditional approaches have limitations in precision, response time, and adaptability to environmental factors. To address this gap, machine learning (ML) and deep learning (DL) approaches are increasingly being adopted for fire detection. This study proposes a hybrid approach that combines deep feature extraction using Transfer Learning (TL) with EfficientNetB0 and traditional Gradient Boosting Models (GBM), specifically LightGBM, XGBoost, and CatBoost, for wildfire detection using UAV imagery. EfficientNetB0 is used to extract features, which are then dimensionally reduced using Principal Component Analysis (PCA). Optuna framework is used for hyperparameter tuning, to maximize the performance of the model. The results show that the GBM models enhanced with TL outperform their baseline counterparts, with CatBoost + TL achieving the highest precision (92. 37%). These findings confirm the role of TL in improving model precision, efficiency, and computational resource utilization, making it ideal for real-time wildfire detection.

In recent years, the use of the Internet of Things (IoT) has grown rapidly in smart cities, households, and smart industries environments due to the benefits of convenience and easy implementation. The Long-Range Wide Area Network (LoRaWAN) is a key technology driving this growth, which has seen significant adoption due to its scalability, long-range capabilities, low power consumption, lost cost and wide area coverage. As the use of LoRaWAN-enabled IoT environment has increased, the cyber-attacks related to it have also increased. About 95 % of the cyber-attacks in IoT environment occurs due to the security vulnerabilities caused by the user's errors. However, much research in the LoRaWAN environ-ment is highly focused on the security vulnerabilities due to technical errors, leaving gaps in identifying the security vulnerabilities that occur due to user errors. Therefore, this paper addresses these gaps by identifying user-centric errors which may lead to cyber-attacks in an LoRAWAN IoT environment. It builds a taxonomy based on a comprehensive literature review and maps these user errors to potential security vulnerabilities within LoRa WAN- enabled IoT environments.

This article introduces a novel approach to address the security challenges of chipless tag systems in the context of Internet of Things (IoT) applications. The proposed technique focuses on preventing tag cloning by leveraging the inherent natural randomness in the fabrication process. A groundbreaking aspect of this research is the utilization of an affordable and portable Multiple Input Multiple Output (MIMO) antenna system in real-world scenarios to detect counterfeit tags. The study begins by validating the precision of the MIMO system, establishing its effectiveness compared to Vector Network Analyzer (VNA) equipment. Additionally, the clone detection system is thoroughly evaluated for accuracy, and improvements are introduced through various Machine Learning (ML) models. The ML techniques used were able to detect clones with 99.78% accuracy, outperforming VNA equipment. This research represents a significant advancement in chipless tag security for IoT applications, offering a cost-effective and efficient solution to the pressing security issue of tag cloning.

Accurate dielectric constant measurements are crucial in Internet of Things (IoT) sensing applications to characterize materials and their properties. This article introduces an innovative capacitor-based chipless radio frequency identification (RFID) sensory array tailored specifically for precise measurements of dielectric constants in IoT contexts. The array incorporates Pi-shaped resonators and cylindrical capacitors, addressing challenges, such as low accuracy, limited$\varepsilon _{r}$measuring range, and the reliance on bulky vector network analyzers (VNAs) as readers. Theoretical modeling, design considerations, sensor calibration, and validation processes are detailed, highlighting the array’s precision and adaptability. Real-world IoT applications are demonstrated, showcasing the array’s potential with a low-cost portable multiple-input-multiple-output (MIMO) reader, Walabot. This cost-effective solution overcomes conventional limitations, offering a versatile approach to dielectric constant measurements and opening up new possibilities for diverse IoT sensing applications. The integration of this sensory array with IoT technologies demonstrates the feasibility of smarter material characterization.

This article has proposed detection and physical-layer security provision for printed sensory tag systems for Internet of Things (IoT) applications. The printed sensory tags can be a very cost-effective way to speed up the proliferation of the intelligent world of IoT. The printed radio-frequency identification (RFID) of a sensory tag is chipless with the fully printable feature, Nonline-of-Sight (NLoS) reading, low cost, and robustness to the environment. The detection and adoption of security features for such tags in a robust environment are still challenging. This article initially presents a robust technology for detecting tags using both the amplitude and phase information of the frequency signature. After successfully identifying tag IDs, the article presents novel physical-layer security using a deep learning model to prevent the cloning of tags. Our experiment shows that the proposed system can detect and identify the unique physical attributes of the tag and isolate the clone tag from the genuine tag. It is believed that such real-time and precise detection and security features bring this technology closer to commercialization for IoT applications.

Due to the widespread functional benefits, such as supporting internet connectivity, having high visibility and enabling easy connectivity between sensors, the Internet of Things (IoT) has become popular and used in many applications, such as for smart city, smart health, smart home, and smart vehicle realizations. These IoT-based systems contribute to both daily life and business, including sensitive and emergency situations. In general, the devices or sensors used in the IoT have very limited computational power, storage capacity, and communication capabilities, but they help to collect a large amount of data as well as maintain communication with the other devices in the network. Since most of the IoT devices have no physical security, and often are open to everyone via radio communication and via the internet, they are highly vulnerable to existing and emerging novel security attacks. Further, the IoT devices are usually integrated with the corporate networks; in this case, the impact of attacks will be much more significant than operating in isolation. Due to the constraints of the IoT devices, and the nature of their operation, existing security mechanisms are less effective for countering the attacks that are specific to the IoT-based systems. This article presents a new insider attack, namedloophole attack, that exploits the vulnerabilities present in a widely used IPv6 routing protocol in IoT-based systems, calledRPL(Routing over Low Power and Lossy Networks). To protect the IoT system from this insider attack, a machine learning based security mechanism is presented. The proposed attack has been implemented using a Contiki IoT operating system that runs on the Cooja simulator, and the impacts of the attack are analyzed. Evaluation on the collected network traffic data demonstrates that the machine learning based approaches, along with the proposed features, help to accurately detect the insider attack from the network traffic data.

The cloud resource allocation for jobs must be further optimized and prioritised due to ever increasing demand for cloud computing resources to handle big data. In this research, we have examined the relationship between resource allocation, usages, and priority of tasks to reveal the influence of priority in resource allocation and resource usages. The analysis and modeling of this paper have used the Google cloud public dataset of 2011 and 2019. After processing and cleaning of one month data of Google cloud, we have revealed, the tasks are classified in 12 priorities in the 2011 cluster model whereas 500 priorities in the 2019 cluster model. However, both models have grouped these priorities into five groups. Therefore, we have modeled resource allocation versus usages based on five main priority groups using XGBoost (Extreme Gradient Boosting) and correlation coefficient. The comparative study on the developed models shows, the priority grouping of 2019 has better evenly distribution of resources for jobs but less efficient in most of the priority groups for resource allocation. Based on the performance parameters of the developed models, the resource allocation works more efficiently for most of the 2011 priority groups except `other'. These findings are useful for researchers to develop a balanced priority-based resource allocation-usages model to further optimise resources to reduce the management cost of cloud clusters.

In this paper, we propose a secure object tracking protocol to ensure the visibility and traceability of an object along the travel path to support the Internet of Things (IoT). The proposed protocol is based on radio frequency identification system for global unique identification of IoT objects. For ensuring secure object tracking, lightweight cryptographic primitives and physically unclonable function are used by the proposed protocol in tags. We evaluated the proposed protocol both quantitatively and qualitatively. In our experiment, we modeled the protocol using security protocol description language (SPDL) and simulated SPDL model using automated claim verification tool Scyther. The results show that the proposed protocol is more secure and requires less computation compared to existing similar protocols.

Networked systems have adapted Radio Frequency identification technology (RFID) to automate their business process. The Networked RFID Systems (NRS) has some unique characteristics which raise new privacy and security concerns for organizations and their NRS systems. The businesses are always having new realization of business needs using NRS. One of the most recent business realization of NRS implementation on large scale distributed systems (such as Internet of Things (IoT), supply chain) is to ensure visibility and traceability of the object throughout the chain. However, this requires assurance of security and privacy to ensure lawful business operation. In this paper, we are proposing a secure tracker protocol that will ensure not only visibility and traceability of the object but also genuineness of the object and its travel path on-site. The proposed protocol is using Physically Unclonable Function (PUF), Diffie-Hellman algorithm and simple cryptographic primitives to protect privacy of the partners, injection of fake objects, non-repudiation, and unclonability. The tag only performs a simple mathematical computation (such as combination, PUF and division) that makes the proposed protocol suitable to passive tags. To verify our security claims, we performed experiment on Security Protocol Description Language (SPDL) model of the proposed protocol using automated claim verification tool Scyther. Our experiment not only verified our claims but also helped us to eliminate possible attacks identified by Scyther.

The Radio frequency identification (RFID) system is a new technology which uses the open air to transmit information. RFID technology is one of the most promising technologies in the field of ubiquitous computing which is revolutionizing the supply chain. It has already been applied by many major retail chains such as Target, Wal-Mart, etc. The networked RFID system such as supply chain has very unique and special business needs which lead to special sets of RFID security requirements and security models. However, very little work has been done to analyze RFID security parameters in relation to networked RFID systems business needs. This paper presents a critical analysis of the networked application's security requirements in relation to their business needs. It then presents a comparative study of existing literature and the ability of various models to protect the security of the supply chain in a RFID deployment.

Radio frequency identification (RFID) is a remote identification technique promises to revolutionize the way a specific object use to identify in our industry. However, large scale implementation of RFID sought for protection, against Malware threat, information privacy and un-traceability, for low cost RFID tag. In this paper, we propose a framework to provide privacy for tag data and to provide protection for RFID system from malware. In the proposed framework, malware infected tag is detected by analysing individual component of the RFID tag. It uses sanitization technique for analysing individual component. Here authentication based shared unique parameters is used as a method to protect privacy. This authentication protocol will be capable of handling forward and backward security and identifying rogue reader better than existing protocols. Using this framework, the RFID system will be protected from malware and the privacy of the tag will be ensured as well.