An Effective Model for SMS Spam Detection Using Content-based Features and Averaged Neural Network

Sheikhi, Saeid; Kheirabadi, Mohammad Taghi; Bazzazi, A.

doi:10.5829/ije.2020.33.02b.06

Cited by 13 publications

(5 citation statements)

References 17 publications

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“…The performance presented by several machine learning techniques is compared. A novel machine learning system for SMS spam messages detection is proposed in [19]. This paper utilized feature extraction and decision making as a method dependent on the proposed system.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Security Schema for SMS Spam Message Based on Machine Learning Algorithms

Alshahrani

2021

Int. J. Interact. Mob. Technol.

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<p class="0abstract">SMS spam messages represent one of the most serious threats to current traditional networks. These messages have been particularly prevalent overseas and are harmful to various types of devices. The current filtering scheme employed in conventional systems is unable to expose a large number of messages. To resolve this issue, a new intelligent security system is proposed to reduce the number of spam messages. It can detect novel spam messages that have a direct and negative impact on networks. The proposed system is heavily based on machine learning to explore various types of messages. The primary achievement of our study is the increase in the accuracy ratio as well as the reduction in the number of false alarms. According to the experimental results, it is clear that our system can realize outstanding results, detecting a massive number of massages.</p>

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Section: Related Workmentioning

confidence: 99%

Intelligent Security Schema for SMS Spam Message Based on Machine Learning Algorithms

Alshahrani

2021

Int. J. Interact. Mob. Technol.

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“…Zhao et al [33] applied six classifiers in the basic module and a deep neural network in the combination module. There are also other models for SMS spam detection, such as the neural network [34], KNN [35], and negative selection algorithm (NSA) [36]. Recently, Shang [37] developed a score-based filtering mechanism in consensus of hybrid multi-agent systems with malicious nodes, which can also be applied for spam detection.…”

Section: Content-filtering Technologiesmentioning

confidence: 99%

A Discrete Hidden Markov Model for SMS Spam Detection

Xia

Chen

2020

Applied Sciences

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Many machine learning methods have been applied for short messaging service (SMS) spam detection, including traditional methods such as naïve Bayes (NB), vector space model (VSM), and support vector machine (SVM), and novel methods such as long short-term memory (LSTM) and the convolutional neural network (CNN). These methods are based on the well-known bag of words (BoW) model, which assumes documents are unordered collection of words. This assumption overlooks an important piece of information, i.e., word order. Moreover, the term frequency, which counts the number of occurrences of each word in SMS, is unable to distinguish the importance of words, due to the length limitation of SMS. This paper proposes a new method based on the discrete hidden Markov model (HMM) to use the word order information and to solve the low term frequency issue in SMS spam detection. The popularly adopted SMS spam dataset from the UCI machine learning repository is used for performance analysis of the proposed HMM method. The overall performance is compatible with deep learning by employing CNN and LSTM models. A Chinese SMS spam dataset with 2000 messages is used for further performance evaluation. Experiments show that the proposed HMM method is not language-sensitive and can identify spam with high accuracy on both datasets.

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“…The fake account detection method is based on the bagging algorithm used to the bagged decision tree. In general, parameters influence every classifier performance [23]. The Bagging method has two training parameters: BagSizePercent manages the bag size, as a percentage; and NumIterations controls the number of iterations.…”

Section: Experiments Setupmentioning

confidence: 99%

An Efficient Method for Detection of Fake Accounts on the Instagram Platform

Sheikhi¹

2020

RIA

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In recent years, social media platforms such as Instagram, Twitter, and Facebook have gradually become important ways to disseminate information. One of these social platforms that have attracted more attention in past years is Instagram. Instagram has widely used for sharing photos and videos and is profitable for celebrities, businesses, and people with a considerable number of followers. In the meantime, this high profit made this platform prone to be the potential place to be used for malicious activities. One of the essential malicious activities in the Instagram platform is fake accounts. However, in this paper, an efficient method for identifying Instagram fake accounts is proposed. In the presented model. First, a dataset of legitimate and fake accounts is created. Then, the collected dataset has been used as input of the bagging classifier to classify fake users on the dataset. Furthermore, the proposed method compared to the five well-known machine-learning classifiers in terms of classification accuracy to better evaluate effectiveness of the method. The experimental results show that the proposed method performs better than other considered algorithms and correctly classified over 98% of the accounts with a low error rate.

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An Effective Model for SMS Spam Detection Using Content-based Features and Averaged Neural Network

Cited by 13 publications

References 17 publications

Intelligent Security Schema for SMS Spam Message Based on Machine Learning Algorithms

Intelligent Security Schema for SMS Spam Message Based on Machine Learning Algorithms

A Discrete Hidden Markov Model for SMS Spam Detection

An Efficient Method for Detection of Fake Accounts on the Instagram Platform

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