Enhancing Network Security Via Machine Learning: Opportunities and Challenges

Amrollahi, Mahdi; Hadayeghparast, Shahrzad; Karimipour, Hadis; Derakhshan, Farnaz; Srivastava, Gautam

doi:10.1007/978-3-030-38557-6_8

Cited by 14 publications

(6 citation statements)

References 45 publications

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“…In the literature, many areas extended the models of machine learning. These areas include Solar energy [23], Telecommunication [24], Network security [25], Disease classification [26], and Computer vision [27]. In addition, machine-learning models can be used for either classification or prediction of data points without explicit programming of these tasks.…”

Section: Literature Reviewmentioning

confidence: 99%

Robust Prediction of the Bandwidth of Metamaterial Antenna Using Deep Learning

Abdelhamid¹,

Alotaibi²

2022

Computers, Materials &Amp; Continua

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The design of microstrip antennas is a complex and time-consuming process, especially the step of searching for the best design parameters. Meanwhile, the performance of microstrip antennas can be improved using metamaterial, which results in a new class of antennas called metamaterial antenna. Several parameters affect the radiation loss and quality factor of this class of antennas, such as the antenna size. Recently, the optimal values of the design parameters of metamaterial antennas can be predicted using machine learning, which presents a better alternative to simulation tools and trialand-error processes. However, the prediction accuracy depends heavily on the quality of the machine learning model. In this paper, and benefiting from the current advances in deep learning, we propose a deep network architecture to predict the bandwidth of metamaterial antenna. Experimental results show that the proposed deep network could accurately predict the optimal values of the antenna bandwidth with a tiny value of mean-square error (MSE). In addition, the proposed model is compared with current competing approaches that are based on support vector machines, multi-layer perceptron, K-nearest neighbors, and ensemble models. The results show that the proposed model is better than the other approaches and can predict antenna bandwidth more accurately.

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Section: Literature Reviewmentioning

confidence: 99%

Robust Prediction of the Bandwidth of Metamaterial Antenna Using Deep Learning

Abdelhamid¹,

Alotaibi²

2022

Computers, Materials &Amp; Continua

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“…Machine learning models have been extended in several areas such as telecommunication [23], Computer Vision [24], Disease Classification [25], Solar Energy [26], and Network Security [27]. These models learn from data.…”

Section: Literature Reviewmentioning

confidence: 99%

An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

Shahbazi¹,

Byun²

2022

Computers, Materials &Amp; Continua

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Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their performance. Antenna size affects the quality factor and the radiation loss of the antenna. Metamaterial antennas can overcome the limitation of bandwidth for small antennas. Machine learning (ML) model is recently applied to predict antenna parameters. ML can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated antenna. The accuracy of the prediction depends mainly on the selected model. Ensemble models combine two or more base models to produce a better-enhanced model. In this paper, a weighted average ensemble model is proposed to predict the bandwidth of the Metamaterial Antenna. Two base models are used namely: Multilayer Perceptron (MLP) and Support Vector Machines (SVM). To calculate the weights for each model, an optimization algorithm is used to find the optimal weights of the ensemble. Dynamic Group-Based Cooperative Optimizer (DGCO) is employed to search for optimal weight for the base models. The proposed model is compared with three based models and the average ensemble model. The results show that the proposed model is better than other models and can predict antenna bandwidth efficiently.

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“…Most of the research uses default classification settings. Default settings mostly result in under performance of the machine learning classification model (Ahmed et al 2019a;Ahmed et al 2020, Amrollahi et al 2020: the feature extraction, selection, model selection, hyper-parameter tuning and evaluation need to be optimized. Most research avenues do not consider multi-node application splitting or merger technique and the load balancing issue in the cluster of a heterogeneous environment.…”

Section: Critical Analysismentioning

confidence: 99%

A load balance multi-scheduling model for OpenCL kernel tasks in an integrated cluster

et al. 2020

Self Cite

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Nowadays, embedded systems are comprised of heterogeneous multi-core architectures, i.e., CPUs and GPUs. If the application is mapped to an appropriate processing core, then these architectures provide many performance benefits to applications. Typically, programmers map sequential applications to CPU and parallel applications to GPU. The task mapping becomes challenging because of the usage of evolving and complex CPU-and GPU-based architectures. This paper presents an approach to map the OpenCL application to heterogeneous multi-core architecture by determining the application suitability and processing capability. The classification is achieved by developing a machine learning-based device suitability classifier that predicts which processor has the highest computational compatibility to run OpenCL applications. In this paper, 20 distinct features are proposed that are extracted by using the developed LLVM-based static analyzer. In order to select the best subset of features, feature selection is performed by using both correlation analysis and the feature importance method. For the class imbalance problem, we use and compare synthetic minority over-sampling method with and without feature selection. Instead of hand-tuning the machine learning classifier, we use the tree-based pipeline optimization method to select the best classifier and its hyper-parameter. We then compare the optimized selected method with traditional algorithms, i.e., random forest, decision tree, Naïve Bayes and KNN. We apply our novel approach on extensively used OpenCL benchmarks, i.e., AMD and Polybench. The dataset contains 653 training and 277 testing applications. We test the classification results using four performance metrics, i.e., F-measure, precision, recall and R 2. The optimized and reduced feature subset model achieved a high F-measure of 0.91 and R 2 of 0.76. The proposed framework automatically distributes the workload based on the application requirement and processor compatibility.

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Enhancing Network Security Via Machine Learning: Opportunities and Challenges

Cited by 14 publications

References 45 publications

Robust Prediction of the Bandwidth of Metamaterial Antenna Using Deep Learning

Robust Prediction of the Bandwidth of Metamaterial Antenna Using Deep Learning

An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

A load balance multi-scheduling model for OpenCL kernel tasks in an integrated cluster

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