D. Ruby

Bio: D. Ruby is an academic researcher from VIT University. The author has contributed to research in topics: Edge device & Enhanced Data Rates for GSM Evolution. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

Deep Learning Enabled Data Offloading With Cyber Attack Detection Model in Mobile Edge Computing Systems

Abstract: Mobile edge computing (MEC) becomes popular as it offers cloud services and functionalities to the edge devices, to enhance the quality of service (QoS) of end-users by offloading their computationally intensive tasks. At the same time, the rise in the number of internet of things (IoT) objectives poses considerable cybersecurity issues owing to the latest rise in the existence of attacks. Presently, the development of deep learning and hardware technologies offers a way to detect the present traffic condition, data offloading, and cyber-attacks in edge networks. The utilization of DL models finds helpful in several domains in which the MEC provides the decisive beneficiary of the approach for traffic prediction and attack detection since a large quantity of data generated by IoT devices enables deep models to learn better than shallow approaches. In this view, this paper presents a new DL based traffic prediction with a data offloading mechanism with cyber-attack detection (DLTPDO-CD) technique. The proposed model involves three major processes traffic prediction, data offloading, and attack detection. Initially, bidirectional long short term memory (BiLSTM) based traffic prediction to enable the proficient data offloading process. Then, the adaptive sampling cross entropy (ASCE) technique is executed to maximize the network throughput by making decisions related to offloading users to the WiFi system. Finally, a deep belief network (DBN) optimized by a barnacles mating optimizer (BMO) algorithm called BMO-DBN is applied as a detection tool for cyberattacks in MEC. Extensive simulation is carried out to ensure the proficient performance of the DLTPDO-CD model. The experimental outcome stated the superiority of the presented model over the compared methods under different dimensions.

Cyber-attack detection in healthcare using cyber-physical system and machine learning techniques

Abstract: Cyber-physical systems have been extensively utilized in healthcare domains to deliver high-quality patient treatment in multifaceted clinical scenarios. The medical device’ heterogeneity involved in these systems (mobile devices and body sensor nodes) introduces enormous attack surfaces and therefore necessitates effective security solutions for these complex environments. Hence, in this study, the cognitive machine learning assisted Attack Detection Framework has been proposed to share healthcare data securely. The Healthcare Cyber-Physical Systems will be proficient in spreading the collected data to cloud storage. Machine learning models predict cyber-attack behavior, and processing this data can offer healthcare specialists decision support. This proposed approach is based on a patient-centric design that safeguards the information on a trusted device like the end-users mobile phones and end-user control data sharing access. Experimental results demonstrate that our suggested model achieves an attack prediction ratio of 96.5%, an accuracy ratio of 98.2%, an efficiency ratio of 97.8%, less delay of 21.3%, and a communication cost of 18.9% to other existing models.

Artificial Intelligence Based Data Offloading Technique for Secure MEC Systems

Abstract: Mobile edge computing (MEC) provides effective cloud services and functionality at the edge device, to improve the quality of service (QoS) of end users by offloading the high computation tasks. Currently, the introduction of deep learning (DL) and hardware technologies paves a method in detecting the current traffic status, data offloading, and cyberattacks in MEC. This study introduces an artificial intelligence with metaheuristic based data offloading technique for Secure MEC (AIMDO-SMEC) systems. The proposed AIMDO-SMEC technique incorporates an effective traffic prediction module using Siamese Neural Networks (SNN) to determine the traffic status in the MEC system. Also, an adaptive sampling cross entropy (ASCE) technique is utilized for data offloading in MEC systems. Moreover, the modified salp swarm algorithm (MSSA) with extreme gradient boosting (XGBoost) technique was implemented to identification and classification of cyberattack that exist in the MEC systems. For examining the enhanced outcomes of the AIMDO-SMEC technique, a comprehensive experimental analysis is carried out and the results demonstrated the enhanced outcomes of the AIMDO-SMEC technique with the minimal completion time of tasks (CTT) of 0.680.

Enhancing quality of experience in mobile edge computing using deep learning based data offloading and cyberattack detection technique

Abstract: Due to the advancements of high-speed networks, mobile edge computing (MEC) has received significant attention to bring processing and storage resources in client’s proximity. The MEC is also a form of Edge Network or In-network computing where the resources are brought closer to the user end (edge) of the network while increasing QoE. On the other hand, the increase in the utilization of the internet of things (IoT) gadgets results in the generation of cybersecurity issues. In recent times, the advent of machine learning (ML) and deep learning (DL) techniques paves way in the detection of existing traffic conditions, data offloading, and cyberattacks in MEC. With this motivation, this study designs an effective deep learning based data offloading and cyberattack detection (DL-DOCAD) technique for MEC. The goal of the DL-DOCAD technique is to enhance the QoE in MEC systems. The proposed DL-DOCAD technique comprises traffic prediction, data offloading, and attack detection. The DL-DOCAD model applies a gated recurrent unit (GRU) based predictive model for traffic detection. In addition, an adaptive sampling cross entropy (ASCE) approach is employed for the maximization of throughput and decision making for offloading users. Moreover, the birds swarm algorithm based feed forward neural network (BSA-FFNN) model is used as a detector for cyberattacks in MEC. The utilization of BSA to appropriately tune the parameters of the FFNN helps to boost the classification performance to a maximum extent. A comprehensive set of simulations are performed and the resultant experimental values highlight the improved performance of the DL-DOCAD technique with the maximum detection accuracy of 0.992.

Class imbalance data handling with deep learning–based ubiquitous healthcare monitoring system using wearable devices

Abstract: Rapid development in the production of wearable devices has taken place for healthcare monitoring system. At the same time, the existence of imbalanced data poses a major influence on the prediction model, and many of the under sampling models require maximum time and decreased performance. In this view, this chapter develops a new class imbalance data handling (CIH) with optimal deep belief network (ODBN) model, named CIH-ODBN for ubiquitous healthcare monitoring system. To handle the class imbalance problem in healthcare data, adaptive synthetic sampling (ADASYN) technique is employed. Besides, the ODBN model is applied to determine the presence of diseases. In addition, rainfall optimization algorithm (ROA) is introduced to tune the hyperparameters of the deep belief network (DBN) model. An extensive implementation analysis was performed to signify the effectual outcome of the CIH-ODBN model. The outcome for the experimental validation verified the effective classification outcome of the CIH-ODBN model with the accuracy of 0.916 and 0.932 on the test diabetes and heart disease dataset.