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

Gopalakrishnan, T.; Ruby, David; Al‐Turjman, Fadi; Gupta, Deepak; Pustokhina, Irina V.; Pustokhin, Denis A.; Shankar, K.

doi:10.1109/access.2020.3030726

Cited by 29 publications

(13 citation statements)

References 23 publications

(35 reference statements)

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“…r 1 , j , r j < pr s or j = j 1 soc p,t r 2 j , r j ≥ pr s + pr a or j = j 2 σ j , O.W . (7) where r j ∈ [0, 1] denotes arbitrary values and r 2 indicates an arbitrary coyote in the group p, σ j defines an arbitrary value in the design variable limits, j 1 and j 2 are arbitrary design variables, and pr a and pr s signify the association and scatter probability which declares the coyote cultural diversity from the group. The pr a and pr s can be mathematically defined here's:…”

Section: Feature Selection Using Ocoa Techniquementioning

confidence: 99%

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Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

Shoab¹,

Alotaibi²

2022

Computers, Materials &Amp; Continua

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With the incorporation of distributed energy systems in the electric grid, transactive energy market (TEM) has become popular in balancing the demand as well as supply adaptively over the grid. The classical grid can be updated to the smart grid by the integration of Information and Communication Technology (ICT) over the grids. The TEM allows the Peerto-Peer (P2P) energy trading in the grid that effectually connects the consumer and prosumer to trade energy among them. At the same time, there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of machine learning (DML) models. Though some of the short term load prediction techniques have existed in the literature, there is still essential to consider the intrinsic features, parameter optimization, etc. into account. In this aspect, this study devises new deep learning enabled short term load forecasting model for P2P energy trading (DLSTLF-P2P) in TEM. The proposed model involves the design of oppositional coyote optimization algorithm (OCOA) based feature selection technique in which the OCOA is derived by the integration of oppositional based learning (OBL) concept with COA for improved convergence rate. Moreover, deep belief networks (DBN) are employed for the prediction of load in the P2P energy trading systems. In order to additional improve the predictive performance of the DBN model, a hyperparameter optimizer is introduced using chicken swarm optimization (CSO) algorithm is applied for the optimal choice of DBN parameters to improve the predictive outcome. The simulation analysis of the proposed DLSTLF-P2P is validated using the UK Smart Meter dataset and the obtained outcomes demonstrate the superiority of the DLSTLF-P2P technique with the maximum training, testing, and validation accuracy of 90.17%, 87.39%, and 87.86%.

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Section: Feature Selection Using Ocoa Techniquementioning

confidence: 99%

“…A P2P electricity trading's among electric vehicles (EVs) are presented for reducing the impacts of EV charging on peak power. A complete P2P market framework for bilateral electricity trading amongst market performers according to their preference [7].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

Shoab¹,

Alotaibi²

2022

Computers, Materials &Amp; Continua

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“…W (1) represents the hidden output weight matrix. Mainly, RNN is utilized in handling the problems related to order input and output.…”

Section: Phase Ii: Data Classificationmentioning

confidence: 99%

“…Cybersecurity is a phenomenon comprised of a group of processes, methodologies, policies, and technologies which collaborate together to protect the availability, confidentiality, and integrity of software programs, computing resources, network, and data from different types of attacks [1]. Cyber defense mechanism exists at network, data, application and host level.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Empowered Cybersecurity Spam Bot Detection for Online Social Networks

Duhayyim¹,

Alshahrani²,

Al‐Wesabi³

et al. 2022

Computers, Materials &Amp; Continua

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Cybersecurity encompasses various elements such as strategies, policies, processes, and techniques to accomplish availability, confidentiality, and integrity of resource processing, network, software, and data from attacks. In this scenario, the rising popularity of Online Social Networks (OSN) is under threat from spammers for which effective spam bot detection approaches should be developed. Earlier studies have developed different approaches for the detection of spam bots in OSN. But those techniques primarily concentrated on hand-crafted features to capture the features of malicious users while the application of Deep Learning (DL) models needs to be explored. With this motivation, the current research article proposes a Spam Bot Detection technique using Hybrid DL model abbreviated as SBD-HDL. The proposed SBD-HDL technique focuses on the detection of spam bots that exist in OSNs. The technique has different stages of operations such as pre-processing, classification, and parameter optimization. Besides, SBD-HDL technique hybridizes Graph Convolutional Network (GCN) with Recurrent Neural Network (RNN) model for spam bot classification process. In order to enhance the detection performance of GCN-RNN model, hyperparameters are tuned using Lion Optimization Algorithm (LOA). Both hybridization of GCN-RNN and LOA-based hyperparameter tuning process make the current work, a first-of-its-kind in this domain. The experimental validation of the proposed SBD-HDL technique, conducted upon benchmark dataset, established the supremacy of the technique since it was validated under different measures.

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“…However, existing machine learning-based security systems inevitably have to learn the data, which is directly related to the problems of the size and complexity of data. An attack on the network essentially requires an immediate or preemptive response in terms of availability [10]. Nowadays, the computing environment and the amount of data collected are beyond what can be processed in realtime.…”

Section: Introductionmentioning

confidence: 99%

A Study on High-Speed Outlier Detection Method of Network Abnormal Behavior Data Using Heterogeneous Multiple Classifiers

2022

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As the complexity and scale of the network environment increase continuously, various methods to detect attacks and intrusions from network traffic by classifying normal and abnormal network behaviors show their limitations. The number of network traffic signatures is increasing exponentially to the extent that semi-realtime detection is not possible. However, machine learning-based intrusion detection only gives simple guidelines as simple contents of security events. This is why security data for a specific environment cannot be configured due to data noise, diversification, and continuous alteration of a system and network environments. Although machine learning is performed and evaluated using a generalized data set, its performance is expected to be similar in that specific network environment only. In this study, we propose a high-speed outlier detection method for a network dataset to customize the dataset in real-time for a continuously changing network environment. The proposed method uses an ensemble-based noise data filtering model using the voting results of 6 classifiers (decision tree, random forest, support vector machine, naive Bayes, k-nearest neighbors, and logistic regression) to reflect the distribution and various environmental characteristics of datasets. Moreover, to prove the performance of the proposed method, we experimented with the accuracy of attack detection by gradually reducing the noise data in the time series dataset. As a result of the experiment, the proposed method maintains a training dataset of a size capable of semi-real-time learning, which is 10% of the total training dataset, and at the same time, shows the same level of accuracy as a detection model using a large training dataset. The improved research results would be the basis for automatic tuning of network datasets and machine learning that can be applied to special-purpose environments and devices such as ICS environments.

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

Cited by 29 publications

References 23 publications

Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

Deep Learning Empowered Cybersecurity Spam Bot Detection for Online Social Networks

A Study on High-Speed Outlier Detection Method of Network Abnormal Behavior Data Using Heterogeneous Multiple Classifiers

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