Assessing risks and threats with layered approach to Internet of Things security

Internet of Things is the next-generation Internet network created by intelligent objects with software and sensors, employed in a wide range of fields such as automotive, construction, health, textile, education and transportation. With the advent of Industry 4.0, Internet of Things has been started to be used and it has led to the emergence of innovative business models. The processing and production capabilities of Internet of Things objects in hidden and critical data provide great advantages for the next generation of Internet. However, the integrated features of Internet of Things objects cause vulnerabilities in terms of security, making them the target of cyber threats. In this study, a security model which offers an integrated risk-based Internet of Things security approach for the Internet of Things vulnerabilities while providing detailed information about Internet of Things and the types of attacks targeting Internet of Things is proposed. In addition, in this study, the vulnerabilities of Internet of Things were explained by classifying attack types threatening the physical layer, network layer, data processing layer and application layer. Moreover, the risk-based security model has been proposed by examining the vulnerabilities and threats of smart objects that generate the Internet of Things. The proposed Internet of Things model is a holistic security model that separately evaluates the Internet of Things layers against vulnerabilities and threats based on the risk-level approach.WoSScopu

Digitally twinned additive manufacturing: Detecting flaws in laser powder bed fusion by combining thermal simulations with in-situmeltpool sensor data

The goal of this research is the in-situ detection of flaw formation in metal parts made using the laser powder bed fusion (LPBF) additive manufacturing process. This is an important area of research, because, despite the considerable cost and time savings achieved, precision-driven industries, such as aerospace and biomedical, are reticent in using LPBF to make safety–critical parts due to tendency of the process to create flaws. Another emerging concern in LPBF, and additive manufacturing in general, is related to cyber security – malicious actors may tamper with the process or plant flaws inside a part to compromise its performance. Accordingly, the objective of this work is to develop and apply a physics and data integrated strategy for online monitoring and detection of flaw formation in LPBF parts. The approach used to realize this objective is based on combining (twinning) in-situ meltpool temperature measurements with a graph theory-based thermal simulation model that rapidly predicts the temperature distribution in the part (thermal history). The novelty of the approach is that the temperature distribution predictions provided by the computational thermal model were updated layer-by-layer with in-situ meltpool temperature measurements. This digital twin approach is applied to detect flaw formation in stainless steel (316L) impeller-shaped parts made using a commercial LPBF system. Four such impellers are produced emulating three pathways of flaw formation in LPBF parts, these are: changes in the processing parameters (process drifts); machine-related malfunctions (lens delamination), and deliberate tampering with the process to plant flaws inside the part (cyber intrusions). The severity and nature of the resulting flaws, such as porosity and microstructure heterogeneity, are characterized ex-situ using X-ray computed tomography, optical and scanning electron microscopy, and electron backscatter diffraction. The digital twin approach is shown to be effective for detection of the three types of flaw formation causes studied in this work