SmiDCA: An Anti-Smishing Model with Machine Learning Approach

Sonowal, Gunikhan; Kuppusamy, K.

doi:10.1093/comjnl/bxy039

Cited by 54 publications

(36 citation statements)

References 22 publications

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“…There are several different machine learning based classification applications proposed in the last few decades [6] [7] [8] [9]. In the field of SMS spam detection, a great number of these approaches are based on traditional machine learning techniques, such as Logistic Regression (LR), Random Forest (RF) [10], Support Vector Machine (SVM) [11], Naïve Bayes (NB), and Decision Trees (DT).…”

Section: A Motivation and Objectivementioning

confidence: 99%

A Spam Transformer Model for SMS Spam Detection

Liu

Nayak

2021

IEEE Access

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In this paper, we aim to explore the possibility of the Transformer model in detecting the spam Short Message Service (SMS) messages by proposing a modified Transformer model that is designed for detecting SMS spam messages. The evaluation of our proposed spam Transformer is performed on SMS Spam Collection v.1 dataset and UtkMl's Twitter Spam Detection Competition dataset, with the benchmark of multiple established machine learning classifiers and state-of-the-art SMS spam detection approaches. In comparison to all other candidates, our experiments on SMS spam detection show that the proposed modified spam Transformer has the optimal results on the accuracy, recall, and F1-Score with the values of 98.92%, 0.9451, and 0.9613, respectively. Besides, the proposed model also achieves good performance on the UtkMl's Twitter dataset, which indicates a promising possibility of adapting the model to other similar problems.

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Section: A Motivation and Objectivementioning

confidence: 99%

A Spam Transformer Model for SMS Spam Detection

Liu

Nayak

2021

IEEE Access

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“…Their evaluation results showed an accuracy of 98.74%. SmiDCA [ 7 ], and a smishing detection technique proposed by Sonowal et al showed an accuracy of 96.4%. The authors selected 39 features of smishing messages and then, used dimensionality reduction to reduce the number of features and to select the 20 best features.…”

Section: Background Studymentioning

confidence: 99%

DSmishSMS-A System to Detect Smishing SMS

Mishra

Soni

2021

Neural Comput & Applic

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With the origin of smart homes, smart cities, and smart everything, smart phones came up as an area of magnificent growth and development. These devices became a part of daily activities of human life. This impact and growth have made these devices more vulnerable to attacks than other devices such as desktops or laptops. Text messages or SMS (Short Text Messages) are a part of smartphones through which attackers target the users. Smishing (SMS Phishing) is an attack targeting smartphone users through the medium of text messages. Though smishing is a type of phishing, it is different from phishing in many aspects like the amount of information available in the SMS, the strategy of attack, etc. Thus, detection of smishing is a challenge in the context of the minimum amount of information shared by the attacker. In the case of smishing, we have short text messages which are often in short forms or in symbolic forms. A single text message contains very few smishing-related features, and it consists of abbreviations and idioms which makes smishing detection more difficult. Detection of smishing is a challenge not only because of features constraint but also due to the scarcity of real smishing datasets. To differentiate spam messages from smishing messages, we are evaluating the legitimacy of the URL (Uniform Resource Locator) in the message. We have extracted the five most efficient features from the text messages to enable the machine learning classification using a limited number of features. In this paper, we have presented a smishing detection model comprising of two phases, Domain Checking Phase and SMS Classification Phase. We have examined the authenticity of the URL in the SMS which is a crucial part of SMS phishing detection. In our system, Domain Checking Phase scrutinizes the authenticity of the URL. SMS Classification Phase examines the text contents of the messages and extracts some efficient features. Finally, the system classifies the messages using Backpropagation Algorithm and compares results with three traditional classifiers. A prototype of the system has been developed and evaluated using SMS datasets. The results of the evaluation achieved an accuracy of 97.93% which shows the proposed method is very efficient for the detection of smishing messages.

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“…Some researchers have proposed the use of naive bayes algorithm to classify text messages as spam or ham [7,20,21]. Other researchers have tried other classifiers, such as Random Forest, Decision Tree, Support Vector Machine, and AdaBoost [22,23], or even a rule-based classification [24]. Other researchers have been interested in standardizing and expanding the content of the messages to improve the classifiers performance [25].…”

Section: Related Workmentioning

confidence: 99%

“…In [22], Sonowal et al proposed a model, called "SmiDCA", for the detection of smishing messages based on machine learning algorithms. In the model, the authors chose to use correlation algorithms to extract the 39 most relevant features from smishing messages.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid CNN-LSTM Model for SMS Spam Detection in Arabic and English Messages

2020

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Despite the rapid evolution of Internet protocol-based messaging services, SMS still remains an indisputable communication service in our lives until today. For example, several businesses consider that text messages are more effective than e-mails. This is because 82% of SMSs are read within 5 min., but consumers only open one in four e-mails they receive. The importance of SMS for mobile phone users has attracted the attention of spammers. In fact, the volume of SMS spam has increased considerably in recent years with the emergence of new security threats, such as SMiShing. In this paper, we propose a hybrid deep learning model for detecting SMS spam messages. This detection model is based on the combination of two deep learning methods CNN and LSTM. It is intended to deal with mixed text messages that are written in Arabic or English. For the comparative evaluation, we also tested other well-known machine learning algorithms. The experimental results that we present in this paper show that our CNN-LSTM model outperforms the other algorithms. It achieved a very good accuracy of 98.37%.

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SmiDCA: An Anti-Smishing Model with Machine Learning Approach

Cited by 54 publications

References 22 publications

A Spam Transformer Model for SMS Spam Detection

A Spam Transformer Model for SMS Spam Detection

DSmishSMS-A System to Detect Smishing SMS

A Hybrid CNN-LSTM Model for SMS Spam Detection in Arabic and English Messages

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