With its significant security potential, the quantum internet is poised to revolutionize technologies like cryptography and communications. Although it boasts enhanced security over traditional networks, the quantum internet still encounters unique security challenges essential for safeguarding its Confidentiality, Integrity, and Availability (CIA). This study explores these challenges by analyzing the vulnerabilities and the corresponding mitigation strategies across different layers of the quantum internet, including physical, link, network, and application layers. We assess the severity of potential attacks, evaluate the expected effectiveness of mitigation strategies, and identify vulnerabilities within diverse network configurations, integrating both classical and quantum approaches. Our research highlights the dynamic nature of these security issues and emphasizes the necessity for adaptive security measures. The findings underline the need for ongoing research into the security dimension of the quantum internet to ensure its robustness, encourage its adoption, and maximize its impact on society.

Intrusion detection systems (IDS) for the Internet of Things (IoT) systems can use AI-based models to ensure secure communications. IoT systems tend to have many connected devices producing massive amounts of data with high dimensionality, which requires complex models. Complex models have notorious problems such as overfitting, low interpretability, and high computational complexity. Adding model complexity penalty (i.e., regularization) can ease overfitting, but it barely helps interpretability and computational efficiency. Feature engineering can solve these issues; hence, it has become critical for IDS in large-scale IoT systems to reduce the size and dimensionality of data, resulting in less complex models with excellent performance, smaller data storage, and fast detection. This paper proposes a new feature engineering method called LEMDA (Light feature Engineering based on the Mean Decrease in Accuracy). LEMDA applies exponential decay and an optional sensitivity factor to select and create the most informative features. The proposed method has been evaluated and compared to other feature engineering methods using three IoT datasets and four AI/ML models. The results show that LEMDA improves the F1 score performance of all the IDS models by an average of 34% and reduces the average training and detection times in most cases.

Crowdsourcing applications, such as Uber for ride-sharing, enable distributed problem-solving. A subset of these applications is intelligent crowd-forecasting applications, e.g., Virustotal, for malware detection. In crowd-forecasting applications, multiple agents respond with predictions about potential future event outcome(s). These responses are then combined to assess the events collaboratively and act accordingly. Unlike conventional crowdsourcing applications that only communicate information, crowd-forecasting applications need to additionally process information to achieve a collaborative assessment. Hence, they require knowledge-based systems instead of simple storage-based ones for crowdsourcing applications. Most existing crowd-forecasting systems are centralized, leading to the inherent single point of failure and inefficient collaborative assessment. This paper presents CrowdFAB,CrowdsourcedForecastingApplications usingBlockchains. We deploy a knowledge-based blockchain paradigm that transforms blockchains from simple storage to knowledge-based systems, thereby achieving crowd-forecasting requirements without centralization. In addition, we formulate a novel reputation scheme that assigns reputations to agents based on their performance. We then use this scheme when making assessments. We implement and analyze CrowdFAB in terms of overhead and security features. Further, we evaluate CrowdFAB for a collaborative malware detection use case, where multiple detectors are involved for crowd forecasting. Results demonstrate CrowdFAB's superior accuracy and other metrics performance compared to other works with the same settings.

The rapid evolutions in microcomputing, mini-hardware manufacturing, and machine-to-machine (M2M) communications have enabled novel Internet-of-Things (IoT) solutions to reshape many networking applications. Healthcare systems are among these applications that have been revolutionized with IoT, introducing an IoT branch known as the Internet-of-Medical Things (IoMT) systems. IoMT systems allow remote monitoring of patients with chronic diseases. Thus, it can provide timely patients' diagnostic that can save their life in case of emergencies. However, security in these critical systems is a major challenge facing their wide utilization. In this article, we present state-of-the-art techniques to secure IoMT systems' data during collection, transmission, and storage. We comprehensively overview IoMT systems' potential attacks, including physical and network attacks. Our findings reveal that most security techniques do not consider various types of attacks. Hence, we propose a security framework that combines several security techniques. The framework covers IoMT security requirements and can mitigate most of its known attacks.

Internet of Things (IoT) devices frequently utilize wireless networks operating in the Industrial, Scientific, and Medical (ISM) Sub‐1 GHz spectrum bands. Compared with higher frequency bands, the Sub‐1 GHz band provides broader coverage and lower power consumption, which are desirable properties for low‐cost IoT applications. However, low‐power and low‐cost IoT modules cause high variability in network performance. The varying influence from real‐world environments additionally undermines wireless propagation and aggravates this variability. We explore these influences and provide a checklist of potential factors affecting wireless network performance in real‐world environments. Using multiple low‐cost IoT modules, we conduct multiple experiments in five real‐world scenarios: indoor, street, open field, ground‐to‐drone (G2D), and drone‐to‐drone (D2D). Specifically, the tests are conducted inside a building, on a straight street with wooded sidewalks and aligned houses, on an open field golf course, and high up in the air between drones. To understand the difficulty of reproducibility in IoT deployments, we studied the effect of factors in four categories. This includes the effect of path (line of sight, distance, and obstruction), configuration (transmit power level), weather (precipitation, temperature, and humidity), and installation (IoT module mobility and position). We find that some of the factors in the path and weather categories have the most influence among all the factors, while the rest have moderate to low impacts. In the end, we provide a complete checklist of all the tested factors, which we believe would be constructive not only to academics but also to industrial practitioners working on wireless IoT systems.

Federal Aviation Administration (FAA) of the United States is considering Remote ID systems for unmanned aerial vehicles (UAVs). These systems act as license plates used on automobiles, but they transmit information using radio waves. To be useful, the transmissions in such systems need to reach long distances to minimize the number of ground stations to capture these transmissions. LoRaWAN is designed as a cheap long-range technology to be used for long-range communication for the Internet of Things. Several manufacturers make LoRaWAN modules, which are readily available on the market and are, therefore, ideal for the UAVs Remote IDs at a low cost. In this paper, we present our experiences in using LoRaWAN technology as a communication technology. Our experiments to identify and locate the UAV systems uncovered several issues of using LoRaWAN in such systems that are documented in this paper. Using several ground stations, we can determine the location of a UAV equipped with a LoRaWAN module that transmits the UAV Remote ID. Hence, it can help identify UAVs that unintentionally, or intentionally, fly into restricted zones.