Hybrid SMS Spam Filtering System Using Machine Learning Techniques

Baaqeel, Hind; Zagrouba, Rachid

doi:10.1109/acit50332.2020.9300071

Cited by 22 publications

(14 citation statements)

References 10 publications

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“…Baaqeel et al [22] address the proliferation of SMS spam, proposing a hybrid spam filtering system using supervised and unsupervised machine learning techniques. By leveraging the strengths of both approaches, their hybrid system aims to enhance spam filtration accuracy and F-measures, thereby providing a robust solution to combat spam SMS threats.…”

Section: Advanced Techniques In Spam Filteringmentioning

confidence: 99%

Next-Generation Spam Filtering: Comparative Fine-Tuning of LLMs, NLPs, and CNN Models for Email Spam Classification

Roumeliotis,

Tselikas,

Nasiopoulos

2024

Electronics

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Spam emails and phishing attacks continue to pose significant challenges to email users worldwide, necessitating advanced techniques for their efficient detection and classification. In this paper, we address the persistent challenges of spam emails and phishing attacks by introducing a cutting-edge approach to email filtering. Our methodology revolves around harnessing the capabilities of advanced language models, particularly the state-of-the-art GPT-4 Large Language Model (LLM), along with BERT and RoBERTa Natural Language Processing (NLP) models. Through meticulous fine-tuning tailored for spam classification tasks, we aim to surpass the limitations of traditional spam detection systems, such as Convolutional Neural Networks (CNNs). Through an extensive literature review, experimentation, and evaluation, we demonstrate the effectiveness of our approach in accurately identifying spam and phishing emails while minimizing false positives. Our methodology showcases the potential of fine-tuning LLMs for specialized tasks like spam classification, offering enhanced protection against evolving spam and phishing attacks. This research contributes to the advancement of spam filtering techniques and lays the groundwork for robust email security systems in the face of increasingly sophisticated threats.

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Section: Advanced Techniques In Spam Filteringmentioning

confidence: 99%

Next-Generation Spam Filtering: Comparative Fine-Tuning of LLMs, NLPs, and CNN Models for Email Spam Classification

Roumeliotis,

Tselikas,

Nasiopoulos

2024

Electronics

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“…Recently, researchers proposed hybrid models to improve the detection of SMS phishing attacks. To filter SMS spam, Baaqeel & Zagrouba suggested a hybrid machine-learning model of SVM and K-Means [28]. To achieve the best results, the implementation was head-tohead, with an unlabeled dataset fed into the K-Means clustering model, and the results were fed into an SVM classifier.…”

Section: Related Workmentioning

confidence: 99%

Uncovering SMS Spam in Swahili Text Using Deep Learning Approaches

Mambina,

Ndibwile,

Uwimpuhwe

et al. 2024

IEEE Access

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In today's communications, Short Message Service (SMS) and Internet protocol-based messaging systems are the most widely used channels. These services are currently the target of an unprecedented number of threats due to their rising appeal. Some spammers have more nefarious motives, even though most spam stems from businesses seeking to promote their products. In recent years, as new security threats such as Smishing have emerged, the amount of SMS spam has experienced substantial growth. The scientific community has paid less attention to SMS spam than email spam despite the fact that both are extensively utilized. SMS spam presents extra processing hurdles. These include the use of lexical variants, SMS-like contractions, or sophisticated obfuscations, which degrade the performance of conventional filtering solutions. In this study, we investigate the efficacy of deep-learning models for filtering Swahili SMS spam based on linguistics and behavioral patterns using a real-world dataset from telecommunications companies in Tanzania. To validate the effectiveness of our models we subjected them to the UCI dataset of spam messages written in English. The models were trained and tested with 10 k-fold cross validation. The experimental results show that CNN-LSTM-LSTM hybrid model attained the highest accuracy of 99.98 on Swahili dataset while CNN-BiLSTM performed better on UCI dataset with an accuracy of 98.38.

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“…The result shows improvement over SVM and Naï ve Bayes. The hybrid technique in Baaqeel and Zagrouba [20] filter spam messages with Support Vector Machine (SVM) supervised classifier and k-means clustering unsupervised classifier.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid spam message detection using convolutional neural network and long short-term memory techniques

Olotu

Daramola

2023

ACE

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Short Message Service (SMS) is a feature of a mobile phone that enable convenient and instant way of sending electronic messages between users. As SMS usage increases fraudulent text messages, known as spam, are becoming more common. Spam SMS may result in leaking personal information, invasion of privacy or accessing unauthorized data from mobile devices. Users of mobile phones can mistakingly give away personal information with the assumption that they are sharing it with the right recipients. This work propose a SMS spam detection method that combines convolutional neural network (CNN) and long short term memory (LSTM) deep learning algorithms. The CNN is used for feature extraction while the LSTM classifies the message. The SMS spam dataset, collected from online repository, is used to train the model. Word embeddings is used to vectorize the words in the message to make it suitable for the model. The result obtained from the implementation outperforms other machine learning algorithms with an accuracy of 99.77%.

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Hybrid SMS Spam Filtering System Using Machine Learning Techniques

Cited by 22 publications

References 10 publications

Next-Generation Spam Filtering: Comparative Fine-Tuning of LLMs, NLPs, and CNN Models for Email Spam Classification

Next-Generation Spam Filtering: Comparative Fine-Tuning of LLMs, NLPs, and CNN Models for Email Spam Classification

Uncovering SMS Spam in Swahili Text Using Deep Learning Approaches

Hybrid spam message detection using convolutional neural network and long short-term memory techniques

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