Search

Internet of Things (IoT) devices are becoming a part of our daily life; from health monitors to critical infrastructure, they are used everywhere. This makes them ideal targets for malicious actors to exploit for nefarious purposes. Recent attacks like the Mirai botnet are just examples in which default credentials were used to exploit thousands of devices. This raises major concerns about IoT device security. In this work, we aimed to investigate security of IoT devices through performing automatic penetration test on IoT devices. A penetration test is a way of detecting security problems, but manually testing billions of IoT devices is infeasible. This work has therefore examined autonomous penetration testing on IoT devices. In recent studies, automated attack execution models were developed for modeling automated attacks in cyber ranges. We have (1) investigated how such models can be applied for performing autonomous IoT penetration testing. Furthermore, we have (2) investigated if some well known and severe Wi-Fi related vulnerabilities still exist in IoT devices. Through a case study, we have shown that the such models can be used to model and design autonomous penetration testing agents for IoT devices. In addition, we have demonstrated that well-known vulnerabilities are present in deployed and currently sold products used in IoT devices, and that they can be both autonomously revealed through our developed system.

In recent years, the cybersecurity attacks on the internet of things (IoT)-based healthcare systems became the major concern for researchers and health organizations. With time, the sophistication of these attacks is increasing. Therefore, healthcare service providers must implement efficient security mechanisms carefully while taking the advantages of connected devices without compromising the patient safety and disturbing the real-time health services. A digital twin (DT) is a virtual representation of a real-time counterpart of a physical world. DT offers significant advantages to cybersecurity experts, empowering them to predict risks without entering the physical world, and to simulate and test cyber-attacks that would otherwise be infeasible to do in real-time in the physical environment. DT in healthcare helps to identify security vulnerabilities, conduct attack simulations, and potential security breaches by creating a virtual replica of the targeted healthcare systems. In this paper, a novel and automated conceptual framework is developed for reinforcing the cybersecurity in IoT-based healthcare using DT technology. It includes the conceptualization and analysis of the proposed framework which can provide dynamic and adaptive security solution to identify real-time threats and vulnerabilities in IoTbased healthcare applications.

BackgroundTumor necrosis factor-α (TNF-α) has been implicated in the blood vessel wall response to hemodynamic forces. We hypothesized that TNF-α activity drives neointimal hyperplasia (NIH) during vein graft arterialization and that anti-TNF-α therapy would inhibit NIH.MethodsRabbits underwent bilateral vein grafting using jugular vein. All distal branches except the occipital artery were unilaterally ligated to create distinct flow environments between the bilateral grafts. Vein grafts were harvested sequentially up to 28 days for TNF-α messenger RNA (mRNA) quantitation. In separate experiments, animals received short-term or long-term dosing with pegylated soluble TNF-α type I receptor (PEG sTNF-RI) or vehicle. After 14 to 28 days, grafts were analyzed for morphometry, proliferation, apoptosis, and PEG sTNF-RI distribution.ResultsQuantitative mRNA assay (TaqMan) revealed shear-dependent (P < .001) and time-dependent (P < .001) TNF-α expression. TNF-α induction was maximal at day 1 and gradually decreased over time, but was persistently elevated even 4 weeks later (P < .001). Low shear (associated with increased NIH) resulted in significantly higher TNF-α mRNA expression (P = .03). PEG sTNF-RI was found in high concentrations in the serum and localized to NIH. The high-flow and low-flow vein grafts from treated animals demonstrated similar volumes of NIH compared with controls. PEG-sTNF-RI had only modest impact on vascular wall cell turnover, as reflected by terminal deoxynucleotide transferase-mediated deoxy uridine triphosphate nick-end labeling (P = .064) and anti-Ki-67 (P = .12) assays.ConclusionsPlacement of a vein into the arterial circulation acutely upregulates TNF-α; this expression level correlates with the degree of subsequent NIH. Pharmacologic interruption of this signaling pathway has no significant impact on NIH or wall cellular proliferation/apoptosis, suggesting that early vein graft adaptations can proceed via TNF-α–independent mechanisms.Clinical RelevanceNeointimal hyperplasia stands as a fundamental pathology of vein graft failure. This study reveals that low wall shear significantly upregulates early and acute proinflammatory cytokine tumor necrosis factor-α (TNF-α) in vein grafts (with robust neointimal formation), whereas high wall shear is associated with less TNF-α induction and neointimal hyperplasia. Further studies demonstrate that blockage of inflammatory TNF-α signaling fails to abrogate occlusive vein graft lesions. Thus, further research is required to delineate the biologic significance of the TNF-α expression dynamics observed in the early vein graft.