A Fog-Augmented Machine Learning based SMS Spam Detection and Classification System

Bosaeed, Sahar; Katib, Iyad; Mehmood, Rashid

doi:10.1109/fmec49853.2020.9144833

Cited by 36 publications

(17 citation statements)

References 15 publications

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“…Secondly, existing approaches assume a fixed network configuration and attack models, and use fixed strategies in devising solutions. The ultimate aim is to optimally migrate and place data, analytics, and other computations in these environments such that security, privacy, energy efficiency and QoS demands of these applications and the environments are best satisfied [8,[70][71][72][73].…”

Section: Discussionmentioning

confidence: 99%

“…Cyber-physical systems (CPSs) such as smart cities and societies are examples of such environments [1,2]. The vision is to make systems and applications coexist in these environments, interacting with each other, producing and consuming historical and real-time information emanating from humans, sensors, and machines, and improving quality of life in many areas, for example, transportation [3][4][5][6], healthcare [7], and others [8].…”

Section: Introductionmentioning

confidence: 99%

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UbiPriSEQ—Deep Reinforcement Learning to Manage Privacy, Security, Energy, and QoS in 5G IoT HetNets

et al. 2020

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5G networks and Internet of Things (IoT) offer a powerful platform for ubiquitous environments with their ubiquitous sensing, high speeds and other benefits. The data, analytics, and other computations need to be optimally moved and placed in these environments, dynamically, such that energy-efficiency and QoS demands are best satisfied. A particular challenge in this context is to preserve privacy and security while delivering quality of service (QoS) and energy-efficiency. Many works have tried to address these challenges but without a focus on optimizing all of them and assuming fixed models of environments and security threats. This paper proposes the UbiPriSEQ framework that uses Deep Reinforcement Learning (DRL) to adaptively, dynamically, and holistically optimize QoS, energy-efficiency, security, and privacy. UbiPriSEQ is built on a three-layered model and comprises two modules. UbiPriSEQ devises policies and makes decisions related to important parameters including local processing and offloading rates for data and computations, radio channel states, transmit power, task priority, and selection of fog nodes for offloading, data migration, and so forth. UbiPriSEQ is implemented in Python over the TensorFlow platform and is evaluated using a real-life application in terms of SINR, privacy metric, latency, and utility function, manifesting great promise.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

UbiPriSEQ—Deep Reinforcement Learning to Manage Privacy, Security, Energy, and QoS in 5G IoT HetNets

et al. 2020

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“…Utilizing machine learning methods, multiple researchers have suggested novel and state-of-the-art smishing classifiers [11,28]. Sahar et al [6] have developed a system that includes numerous machine learning (ML) based classifiers that are built to utilize three classification methods -Naive Bayes (NB), Support Vector Machine (SVM), and Naive Bayes Multinomial (NBM) -as well as five preprocessing and feature extraction methods.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid CNN-GRU Framework with Integrated Pre-trained Language Transformer for SMS Phishing Detection

Ulfath¹,

Alqahtani²,

Hammoudeh³

et al. 2022

Preprint

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Smartphones are prone to SMS phishing due to the rapid growth in the availability of smart mobile technologies driven by Internet connections. Also, detecting phishing SMS is a challenging task due to the unstructured nature of SMS text data with non-linear complex correlations. In this concern, considering the recent advancements in the domain of cybersecurity, we have proposed a hybrid deep learning framework that extracts robust features from SMS texts followed by an automatic detection of Phishing SMS. Due to combining the potential capability of individual models into one hybrid framework, it has outperformed various other individual machine learning and deep learning models. The proposed Phishing Detection framework is an effective hybrid combination of pretrained transformer model, MPNet (Masked and Permuted Language Modeling), with supervised ConvNets (CNN) and Bi-directional Gated Recurrent Units (GRU). It is intended to successfully detect unstructured short phishing text messages that contain complex patterns.

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“…Usually, works conducted on ML involved actuators, sensors, and low-level fog nodes. However, at a higher level, fog nodes can handle frameworks like Weka [31] and Scikit-learn to implement many AI applications. ML is used to execute, optimize, assign, or monitor functional tasks such as clustering, routing, duty-cycle management, data aggregation, and medium access control [32].…”

Section: Introductionmentioning

confidence: 99%

Fog Computing Based on Machine Learning: A Review

Samann

Abdulazeez

Askar

2021

Int. J. Interact. Mob. Technol.

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<p>Internet of Things (IoT) systems usually produce massive amounts of data, while the number of devices connected to the internet might reach billions by now. Sending all this data over the internet will overhead the cloud and consume bandwidth. Fog computing's (FC) promising technology can solve the issue of computing and networking bottlenecks in large-scale IoT applications. This technology complements the cloud computing by providing processing power and storage to the edge of the network. However, it still suffers from performance and security issues. Thus, machine learning (ML) attracts attention for enabling FC to settle its issues. Lately, there has been a growing trend in utilizing ML to improve FC applications, like resource management, security, lessen latency and power usage. Also, intelligent FC was studied to address issues in industry 4.0, bioinformatics, blockchain and vehicular communication system. Due to the ML vital role in the FC paradigm, this work will shed light on recent studies utilized ML in a FC environment. Background knowledge about ML and FC also presented. This paper categorized the surveyed studies into three groups according to the aim of ML implementation. These studies were thoroughly reviewed and compared using sum-up tables. The results showed that not all studies used the same performance metric except those worked on security issues. In conclusion, the simulations of proposed ML models are not sufficient due to the heterogeneous nature of the FC paradigm.</p>

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A Fog-Augmented Machine Learning based SMS Spam Detection and Classification System

Cited by 36 publications

References 15 publications

UbiPriSEQ—Deep Reinforcement Learning to Manage Privacy, Security, Energy, and QoS in 5G IoT HetNets

UbiPriSEQ—Deep Reinforcement Learning to Manage Privacy, Security, Energy, and QoS in 5G IoT HetNets

Hybrid CNN-GRU Framework with Integrated Pre-trained Language Transformer for SMS Phishing Detection

Fog Computing Based on Machine Learning: A Review

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