Restricted Boltzmann Machine Assisted Secure Serverless Edge System for Internet of Medical Things

Lakhan, Abdullah; Mohammed, Mazin Abed; Rashid, Ahmed Noori; Kadry, Seifedine; Abdulkareem, Karrar Hameed; Nedoma, Jan; Martínek, Radek; Razzak, Imran

doi:10.1109/jbhi.2022.3178660

Cited by 26 publications

(12 citation statements)

References 30 publications

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“…In addition, none of the methods failed to concede queue delay, mainly in fault collaboration with nearby nodes. In [31,32] a mobilityaware security dynamic service composition (MSDSC) model-based framework has been created to meet the requirements of deadline-sensitive applications through the restricted Boltzmann mechanism. The suggested probabilistic models assess the process at each phase for effective service execution.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Service Stipulation Uncertainty with Deep Reinforcement Learning for Internet Vehicle Systems

Nain¹,

Shahana²,

Chaudhry³

et al. 2023

Computers, Materials &Amp; Continua

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Fog computing brings computational services near the network edge to meet the latency constraints of cyber-physical System (CPS) applications. Edge devices enable limited computational capacity and energy availability that hamper end user performance. We designed a novel performance measurement index to gauge a device's resource capacity. This examination addresses the offloading mechanism issues, where the end user (EU) offloads a part of its workload to a nearby edge server (ES). Sometimes, the ES further offloads the workload to another ES or cloud server to achieve reliable performance because of limited resources (such as storage and computation). The manuscript aims to reduce the service offloading rate by selecting a potential device or server to accomplish a low average latency and service completion time to meet the deadline constraints of sub-divided services. In this regard, an adaptive online status predictive model design is significant for prognosticating the asset requirement of arrived services to make float decisions. Consequently, the development of a reinforcement learning-based flexible x-scheduling (RFXS) approach resolves the service offloading issues, where x = service/resource for producing the low latency and high performance of the network. Our approach to the theoretical bound and computational complexity is derived by formulating the system efficiency. A quadratic restraint mechanism is employed to formulate the service optimization issue according to a set of measurements, as well as the behavioural association rate and adulation factor. Our system managed an average 0.89% of the service offloading rate, with 39 ms of delay over complex scenarios (using three servers with a 50% service arrival rate). The simulation outcomes confirm that the proposed scheme attained a low offloading uncertainty, and is suitable for simulating heterogeneous CPS frameworks.

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Section: Related Workmentioning

confidence: 99%

Optimizing Service Stipulation Uncertainty with Deep Reinforcement Learning for Internet Vehicle Systems

Nain¹,

Shahana²,

Chaudhry³

et al. 2023

Computers, Materials &Amp; Continua

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“…Technological advancement such as IoT, CoT, and EoT in the healthcare sector has completely revolutionized the human world. Various issues and challenges have been observed in the transition from the 1.0 to 4.0 technology version of the healthcare framework [ 15 ]. A few major challenges are data security, storage, service latency, network bandwidth etc.…”

Section: Related Workmentioning

confidence: 99%

Edge computing based secure health monitoring framework for electronic healthcare system

Singh

Chatterjee

2022

Cluster Comput

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Nowadays, Smart Healthcare Systems (SHS) are frequently used by people for personal healthcare observations using various smart devices. The SHS uses IoT technology and cloud infrastructure for data capturing, transmitting it through smart devices, data storage, processing, and healthcare advice. Processing such a huge amount of data from numerous IoT devices in a short time is quite challenging. Thus, technological frameworks such as edge computing or fog computing can be used as a middle layer between cloud and user in SHS. It reduces the response time for data processing at the lower level (edge level). But, Edge of Things (EoT) also suffers from security and privacy issues. A robust healthcare monitoring framework with secure data storage and access is needed. It will provide a quick response in case of the production of abnormal data and store/access the sensitive data securely. This paper proposed a Secure Framework based on the Edge of Things (SEoT) for Smart healthcare systems. This framework is mainly designed for real-time health monitoring, maintaining the security and confidentiality of the healthcare data in a controlled manner. This paper included clustering approaches for analyzing bio-signal data for abnormality detection and Attribute-Based Encryption (ABE) for bio-signal data security and secure access. The experimental results of the proposed framework show improved performance with maintaining the accuracy of up to 98.5% and data security.

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“…Finally, one hot is encoding to convert the categorical data to unique values that assign the current category value as bit 1 and the other as 0. This will improve the DL model with better input vectors [16]. For example, the DoS label features are converted to the form of [1,0,0,0,0].…”

Section: Data Preprocessingmentioning

confidence: 99%

Decision Tree with Pearson Correlation-based Recursive Feature Elimination Model for Attack Detection in IoT Environment

Padmashree¹,

Krishnamoorthi²

2022

ITC

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The industrial revolution in recent years made massive uses of Internet of Things (IoT) applications like smart cities’ growth. This leads to automation in real-time applications to make human life easier. These IoT-enabled applications, technologies, and communications enhance the quality of life, quality of service, people’s well-being, and operational efficiency. The efficiency of these smart devices may harm the end-users, misuse their sensitive information increase cyber-attacks and threats. This smart city expansion is difficult due to cyber attacks. Consequently, it is needed to develop an efficient system model that can protect IoT devices from attacks and threats. To enhance product safety and security, the IoT-enabled applications should be monitored in real-time. This paper proposed an efficient feature selection with a feature fusion technique for the detection of intruders in IoT. The input IoT data is subjected to preprocessing to enhance the data. From the preprocessed data, the higher-order statistical features are selected using the proposed Decision tree-based Pearson Correlation Recursive Feature Elimination (DT-PCRFE) model. This method efficiently eliminates the redundant and uncorrelated features which will increase resource utilization and reduces the time complexity of the system. Then, the request from IoT devices is converted into word embedding using the feature fusion model to enhance the system robustness. Finally, a Deep Neural network (DNN) has been used to detect malicious attacks with the selected features. This proposed model experiments with the BoT-IoT dataset and the result shows the proposed model efficiency which outperforms other existing models with the accuracy of 99.2%.

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Restricted Boltzmann Machine Assisted Secure Serverless Edge System for Internet of Medical Things

Cited by 26 publications

References 30 publications

Optimizing Service Stipulation Uncertainty with Deep Reinforcement Learning for Internet Vehicle Systems

Optimizing Service Stipulation Uncertainty with Deep Reinforcement Learning for Internet Vehicle Systems

Edge computing based secure health monitoring framework for electronic healthcare system

Decision Tree with Pearson Correlation-based Recursive Feature Elimination Model for Attack Detection in IoT Environment

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