Model based IoT security framework using multiclass adaptive boosting with SMOTE

2020 ◽  
Vol 3 (5) ◽  
Author(s):  
Pandit Byomakesha Dash ◽  
Janmenjoy Nayak ◽  
Bighnaraj Naik ◽  
Etuari Oram ◽  
SK Hafizul Islam
Keyword(s):  
Security Framework ◽  
Adaptive Boosting ◽  
Iot Security ◽  
Model Based

scholarly journals POSSIBILITIES FOR PROVIDING AN INFORMATION SECURITY DURING RETRIEVAL OF NETWORK DATA

2019 ◽  
pp. 689-693
Author(s):  
Veselka Stoyanova
Keyword(s):  
Data Centers ◽  
Cyber Attacks ◽  
Security Framework ◽  
Iot Security ◽  
Computing Power ◽  
Smart Buildings ◽  
Electrical Grids ◽  
Wide Range ◽  
The Internet Of Things ◽  
Require Data

The Internet of Things (IoT) will connect not only computers and mobile devices, but it will also interconnect smart buildings, homes, and cities, as well as electrical grids, gas, and water networks, automobiles, airplanes, etc. IoT will lead to the development of a wide range of advanced information services that need to be processed in real-time and require data centers with large storage and computing power. In this paper, we present an IoT security framework for smart infrastructures such as Smart Homes (SH) and smart buildings (SB). I also present a general threat model that can be used to develop a security protection methodology for IoT services against cyber-attacks (known or unknown).

scholarly journals Multi-Layer IoT Security Framework for Ambient Intelligence Environments

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4038 ◽  
Author(s):  
Ion Bica ◽  
Bogdan-Cosmin Chifor ◽  
Ștefan-Ciprian Arseni ◽  
Ioana Matei
Keyword(s):  
Ambient Intelligence ◽  
Social Care ◽  
Security Framework ◽  
New Paradigm ◽  
Sensitive Data ◽  
Iot Security ◽  
Human Needs ◽  
Health And Social Care ◽  
The Internet Of Things

Ambient intelligence is a new paradigm in the Internet of Things (IoT) world that brings smartness to living environments to make them more sensitive; adaptive; and personalized to human needs. A critical area where ambient intelligence can be used is health and social care; where it can improve and sustain the quality of life without increasing financial costs. The adoption of this new paradigm for health and social care largely depends on the technology deployed (sensors and wireless networks), the software used for decision-making and the security, privacy and reliability of the information. IoT sensors and wearables collect sensitive data and must respond in a near real-time manner to input changes. An IoT security framework is meant to offer the versatility and modularization needed to sustain such applications. Our framework was designed to easily integrate with different health and social care applications, separating security tasks from functional ones and being designed with independent modules for each layer (Cloud, gateway and IoT device), that offer functionalities relative to that layer.

scholarly journals Structural disconnectivity from quantitative susceptibility mapping rim+ lesions is related to disability in people with multiple sclerosis

2020 ◽  
Author(s):  
Ceren Tozlu ◽  
Keith Jamison ◽  
Thanh Nguyen ◽  
Nicole Zinger ◽  
Ulrike Kaunzner ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Prediction Models ◽  
Structural Connectivity ◽  
Susceptibility Mapping ◽  
Quantitative Susceptibility Mapping ◽  
Adaptive Boosting ◽  
Lesion Location ◽  
Model Based ◽  
Structural Connectome ◽  
Left Thalamus

Background: Multiple Sclerosis (MS) is a disease characterized by inflammation, demyelination, and/or axonal loss that disrupts white matter pathways that constitute the brain's structural connectivity network. Individual disease burden and disability in patients with MS (pwMS) varies widely across the population, possibly due to heterogeneity of lesion location, size and subsequent disruption of the structural connectome. Chronic active MS lesions, which have a hyperintense rim (rim+) on Quantitative Susceptibility Mapping (QSM) and a rim of iron-laden inflammatory cells, have been shown to be particularly detrimental to tissue concentration causing greater myelin damage compared to chronic silent MS lesions. How these rim+ lesions differentially impact structural connectivity and subsequently influence disability has not yet been explored. Objective: We characterize differences in the spatial location and structural disconnectivity patterns of rim+ lesions compared to rim- lesions. We test the hypothesis that rim+ lesions' disruption to the structural connectome are more predictive of disability compared to rim- lesions' disruption to the structural connectome. Finally, we quantify the most important regional structural connectome disruptions for disability prediction in pwMS. Methods: Ninety-six pwMS were included in our study (age: 40.25 ± 10.14, 67% female). Disability was assessed using Extended Disability Status Score (EDSS); thirty-seven pwMS had disability (EDSS ≥ 2). Regional structural disconnectivity patterns due to rim- and rim+ lesions were estimated using the Network Modification (NeMo) Tool. For each gray matter region, the NeMo Tool calculates a Change in Connectivity (ChaCo) score, i.e. the percent of connecting streamlines also passing through a lesion. Adaptive Boosting (ADA) classifiers were constructed based on demographics and the two sets of ChaCo scores (from rim+ and rim- lesions); performance was compared across the two models using the area under ROC curve (AUC). Finally, the importance of structural disconnectivity in each brain region in the disability prediction models was determined. Results: Rim+ lesions were much larger and tended to be more periventricular than rim- lesions. The model based on rim+ lesion structural disconnectivity measures had better disability classification performance (AUC = 0.67) than the model based on rim- lesion structural disconnectivity (AUC = 0.63). Structural disconnectivity, from both rim+ and rim- lesions, in the left thalamus and left cerebellum were most important for classifying pwMS into disability categories. Conclusion: Our findings suggest that, independent of the evidence that they have more damaging pathology, rim+ lesions also may be more influential on disability through their disruptions to the structural connectome. Furthermore, lesions of any type in the left cerebellum and left thalamus were especially important in classifying disability in pwMS. This analysis provides a deeper understanding of how lesion location/size and resulting disruption to the structural connectome can contribute to MS-related disability.

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