The Internet of Medical Things (IoMT) has risen to prominence as a possible backbone in the health sector, with the ability to improve quality of life by broadening user experience while enabling crucial solutions such as near real-time remote di- agnostics. However, privacy and security problems remain largely unresolved in the safety area. Various rule-based methods have been considered to recognize aberrant behaviors in IoMT and have demonstrated high accuracy of misbehavior detection appropriate for lightweight IoT devices. However, most of these solutions have privacy concerns, especially when giving context during misbe- havior analysis. Moreover, falsified or modified context generates a high percentage of false positives and, in some cases, causes a by-pass in misbehavior detection. Relying on the recent pow- erful consolidation of Blockchain and federated learning (FL), we propose an efficient privacy-preserving framework for secure mis- behavior detection in lightweight IoMT devices, particularly in the artificial pancreas system (APS). The proposed approach employs privacy-preserving bidirectional long-short term memory (BiLSTM) and augments the security through the integration of Blockchain technology based on Ethereum smart contract environment. Fur- thermore, the effectiveness of the proposed model is bench- marked empirically in terms of sustainable privacy preservation, commensurate incentive scheme with an untraceability feature, ex- haustiveness, and the compact results of a variant neural network approach. As a result, the proposed model has a 99.93% recall rate, showing that it can detect virtually all possible malicious events in the targeted use case. Furthermore, given an initial ether value of 100, the solution's average gas consumption and Ether spent are 84,456.5 and 0.03157625, respectively.

We propose a lightweight specification-based misbehavior detection management technique to efficiently and effectively detect misbehavior of an IoT device embedded in a medical cyber physical system through automatic model checking and formal verification. We verify our specification-based misbehavior detection technique with a patient-controlled analgesia (PCA) device embedded in a medical health monitoring system. Through extensive ns3 simulation, we verify its superior performance over popular machine learning anomaly detection methods based on support vector machine (SVM) and k-nearest neighbors (KNN) techniques in both effectiveness and efficiency performance metrics.

Smart farming is rapidly revolutionizing the agricultural sector where embedded Internet of Things (IoT) devices are integrated into the field to maintain or improve the quality of products as well as increase food production. Despite the tremendous benefits, various cybersecurity threats of IoT can also be inherited by the sector. In this paper, we propose a lightweight specification-based distributed detection to identify the misbehavior of heterogeneous embedded IoT nodes efficiently and effectively in a closed-loop smart greenhouse farming system. To expand the monitoring space of a node, we exploited the Kalman-filter algorithm and simple statistical operations to obtain estimates of data. Accordingly, this enables a monitoring node to assess a target node that has distinct physical characteristics and access to natural phenomena. Along with this, we derive the behavior-rules that are specific to the target system and carefully translate these rules into a state machine diagram. Besides, we formally verify the functional correctness of the monitoring processes as well as ensure that the behavior specifications are completely covered by using the model checker tool UPPAAL. Through extensive experimental simulation using Proteus, we verify its applicability to resource-constrained embedded devices, e.g., Arduino-Uno, as well as show high accuracy in detecting misbehaving nodes while having low false alarms.

Advances of implantable medical devices (IMD) are transforming the traditional method of providing medical treatment, especially those patients under the most challenging condition. Accordingly, the IMD-enabled artificial pancreas system (APS) has now reached global market. It helped many patients suffering from chronic disease, called diabetes mellitus, in monitoring and maintaining blood glucose level conveniently. However, this advancement is accompanied by various security threats that place the life of patients at risk. Hence, protective measures, especially against yet unknown threats, are of paramount importance. This paper proposes a specification-based misbehavior detection system (SMDS) as an alternative solution to effectively mitigate security threats. Moreover, an outlier detection algorithm is also introduced to validate integrity of unprotected data transmitted by the different components. The monitor agent applies a smoothened-trust-based scheme to assess the trustworthiness of the APS. To demonstrate effectiveness of the proposed method, we first extend the UVA/Padova simulator for glucose-insulin data collection and subsequently simulate scenario with well-behave and malicious APS in MATLAB. The results show that there exists an optimal trust threshold that can achieve high specificity and sensitivity rate. Moreover, the proposed technique was compared to contemporary machine learning classifier including decision tree, support vector machine, k-nearest neighbor, and the SMDS called SMDAps. It is shown that our approach can dominate detection performance, especially to malicious behavior that manifests habitually (hidden mode).

Cellular technology has evolved over the decades for mobile network operators to accommodate the ever-growing demands of services for connecting Vehicle-to-Everything (V2X). The 5G infrastructure facilitates V2X communications, where a small-cell base station operating at ultra-high radio frequency with limited coverage becomes pervasive. These small-cell base stations in 5G-V2X must be strategically deployed near the consumers to realize several use cases. More recently, the architectural split solutions in Next Generation Radio Access Network (NG-RAN) are introduced, in which the gNB is divided into the distributed unit (gNB-DU) and control unit (gNB-CU). This functional split intends to improve scalability, performance, and network orchestration optimization. In this case, frequent user equipment (UE) handover between gNB-DUs is inevitable. However, the current 5G standard did not consider securing the path between these two entities. Hence, the NG-RAN could likely experience various security threats if the current handover procedure standard is employed without changes. Consequently, this paper introduces potential threats like resource depletion at NG-RAN caused by the useless execution of resource-demanding procedures to complete the transfer of attachment of UE to target gNB-DU. Another is UE being denied from accessing services caused by unsuccessful uplink and downlink synchronization during random access procedure execution, requiring establishing security and mutual authentication between the entities. Motivated by this, we proposed a security protocol composed of two phases, namely initial and handover. While the former phase assists in mutual authentication and key agreement between UE and serving gNB-DU, the latter secures UE’s mobility in inter-gNB-DU handover. This protocol aims to preserve the existing quality of service and support essential security requirements, including confidentiality, integrity, mutual authentication, secure key exchange, and perfect forward secrecy. The security requirements are formally verified using BAN logic and Scyther, and the proposed protocol demonstrated lower handover latency than EAP-AKA’, AKA, EAP-TLS, and EAP-IKEv2.

Geothermal energy piles or ground heat exchange (GHE) systems embrace a sustainable source of energy that utilizes the geothermal energy naturally found inside the ground in order to heat and/or cool buildings. GHE is a highly innovative system that consists of energy loops within foundation elements (shallow foundations or piles) through which a heat carrier fluid circulates, enabling heat extraction or storage in the ground. Despite the innovation and potential of GHE systems, there are significant challenges in harmonizing their thermal and mechanical designs due to the complex interactions involved. This review critically examines state-of-the-art design methodologies developed to address these complexities, providing insights into the most recent advancements in GHE performance and design. Key findings include innovative techniques such as advanced numerical modeling to predict thermomechanical behavior, the use of different pipe configurations to optimize heat transfer, and strategies to minimize thermal stress on the foundation. Additionally, this review identifies research gaps, including the need for more comprehensive full-scale experimental validations, the impact of soil properties on system performance, and the long-term effects of thermal cycling on pile integrity. These insights aim to contribute to a better understanding of the thermomechanical behavior of energy piles, ultimately facilitating more accurate and effective design solutions.