Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions

Sarker, Iqbal H.; Hoque, Mohammed Moshiul; Uddin, Kafil; Alsanoosy, Tawfeeq

doi:10.1007/s11036-020-01650-z

Cited by 111 publications

(92 citation statements)

References 117 publications

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“…The reason is that the AE-based feature learning model in cybersecurity typically uses the minimum number of security features compared to other state-of-the-art algorithms. The resulting rich and tiny latent representation of the security features makes the model more effective and efficient, even in small devices such as smartphones, known as the internet of things (IoT) devices [91]. For example, the authors [92] present an AE-based feature learning model for cybersecurity applications, where they have demonstrated the model efficacy for malware classification and detection of network-based anomalies.…”

Section: Auto-encoder (Ae)mentioning

confidence: 99%

Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective

Sarker

2021

SN COMPUT. SCI.

Self Cite

115

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Deep learning, which is originated from an artificial neural network (ANN), is one of the major technologies of today's smart cybersecurity systems or policies to function in an intelligent manner. Popular deep learning techniques, such as multi-layer perceptron, convolutional neural network, recurrent neural network or long short-term memory, self-organizing map, autoencoder, restricted Boltzmann machine, deep belief networks, generative adversarial network, deep transfer learning, as well as deep reinforcement learning, or their ensembles and hybrid approaches can be used to intelligently tackle the diverse cybersecurity issues. In this paper, we aim to present a comprehensive overview from the perspective of these neural networks and deep learning techniques according to today's diverse needs. We also discuss the applicability of these techniques in various cybersecurity tasks such as intrusion detection, identification of malware or botnets, phishing, predicting cyberattacks, e.g. denial of service, fraud detection or cyberanomalies, etc. Finally, we highlight several research issues and future directions within the scope of our study in the field. Overall, the ultimate goal of this paper is to serve as a reference point and guidelines for the academia and professionals in the cyber industries, especially from the deep learning point of view.

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Section: Auto-encoder (Ae)mentioning

confidence: 99%

Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective

Sarker

2021

SN COMPUT. SCI.

Self Cite

115

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“…Sarker et al [25] recently used a random forest ensemble learning method consisting of many decision trees to predict the usage of context-aware mobile apps. In [22], Sarker et al also discussed various AI techniques as well as machine learning models based on contextual information. The models focused on machine learning approaches typically work well in predicting smartphone usage.…”

Section: Background and Related Workmentioning

confidence: 99%

“…It could be anything, according to the general definition of context, to describe an entity's condition [4,22]. A smartphone user can be represented as an entity while defining the context in this work.…”

Section: User-centric Contextsmentioning

confidence: 99%

“…Correlation is a well-known indicator between two contextual variables, which tests the strength between the contexts and the class of users' behavioral activity. A contextual dataset may contain high-dimensional data [22], and some may not contain significant information for the construction of a deep learning-based classification model. Besides, further processing using deep learning techniques with all the provided features or attributes may offer poor prediction results because of the over-fitting problem [20,27].…”

Section: Correlation Analysismentioning

confidence: 99%

“…In addition to voice contact, smartphones are used by people in different types of applications such as social networking systems, tour guides, recommendation systems, shopping suggestions, transportation, messaging, medical appointment, etc. [22]. The behavioral activities of users with these apps may vary from user to user in different contexts, such as temporal context, workday or vacation status, spatial context, user emotional state, WiFi status, device-related status, etc.…”

Section: Introductionmentioning

confidence: 99%

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Mobile Deep Learning: Exploring Deep Neural Network for Predicting Context-Aware Smartphone Usage

et al. 2021

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In this paper, we mainly formulate the problem of predicting smartphone usage based on contextual information, which involves both the user-centric and device-centric contexts. In the area of mobile analytics, traditional machine learning techniques, such as Decision Trees, Random Forests, Support Vector Machines, etc. are popular for building context-aware prediction models. However, real-life smartphone usage data may contain higher dimensions of contexts and can be huge in size considering the daily behavioral data of the users. Thus, the traditional machine learning models may not be effective to build the context-aware model. In this paper, we explore "Mobile Deep Learning", an artificial neural network learningbased model considering multiple hidden layers for predicting context-aware smartphone usage. Our model first takes into account context correlation analysis to reduce the neurons as well as to simplify the network model through filtering the irrelevant or less significant contexts, and then build the deep learning model with the selected contexts. The experimental results on smartphone usage datasets show the effectiveness of the model.

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Performance analysis of regression‐based machine learning models towards intelligent selection of MIMO configurations

Beeharry

Calchand

2022

Concurrency and Computation

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Fifth Generation (5G) fosters the integration of several technologies for achieving the requirements of rapid and reliable communications. Multiple Input Multiple Output (MIMO) is one such technology implemented in communication systems with the aim of delivering better services to users. Machine Learning (ML) technologies are also being used alongside MIMO since they can aid with the selection of the most appropriate MIMO configuration based on antenna, data, and channel parameters. These parameters can be further used as input features to the models for obtaining the best data rate and throughput. In this paper, three different MIMO configurations, 2 × 2, 3 × 3, and 4 × 4 MIMO using directional and omnidirectional antennas have been simulated. Power propagation from each antenna, MIMO power, channel capacity, data rate, and throughput have been used as response variables. The data obtained from these simulations are used to train regression models for prediction of new data. Frankfurt city is used as scenario in this simulation and the models are analyzed based on the root mean squared error (RMSE) values obtained. The results demonstrate that the interactions linear regression, fine tree and medium tree models provide the lowest RMSE values when predicting the variables for different MIMO configurations using directional and omnidirectional antenna.

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Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions

Cited by 111 publications

References 117 publications

Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective

Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective

Mobile Deep Learning: Exploring Deep Neural Network for Predicting Context-Aware Smartphone Usage

Performance analysis of regression‐based machine learning models towards intelligent selection of MIMO configurations

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