The Impact of Deep Learning Techniques on SMS Spam Filtering

Gomaa, Wael H.

doi:10.14569/ijacsa.2020.0110167

Cited by 16 publications

(12 citation statements)

References 13 publications

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“…However, the emergence of advanced machine learning algorithms, such as support vector machines (SVMs), neural networks, and deep learning models, revolutionized spam filtering [10,11]. These algorithms enabled the automatic extraction of pertinent features directly from raw email data, learning intricate patterns and relationships within emails.…”

Section: Evolution Of Machine Learning In Spam Filteringmentioning

confidence: 99%

“…Gomaa [11] explores the impact of deep learning techniques on SMS spam filtering, emphasizing the persistence of SMS as a marketing tool and the consequent proliferation of spam. By comparing various deep neural network architectures and classical machine learning classifiers, the study achieves remarkable accuracy in spam detection, highlighting the efficacy of deep learning in combating evolving spam threats.…”

Section: Novel Techniques In Spam Detectionmentioning

confidence: 99%

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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: Evolution Of Machine Learning In Spam Filteringmentioning

confidence: 99%

Section: Novel Techniques In Spam Detectionmentioning

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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“…Barushka and Hajek [17] proposed a deep learning model (DBB-RDNNReL) for spam detection with good performance on strongly imbalanced and highly non-linear spam datasets. In Gomaa [18] Random Multimodel Deep Learning (RDML) algorithm outperforms other classical and deep learning methods. The RDML method combines the advantages of convolutional neural network, deep neural network and recurrent neural network.…”

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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“…The average person checks their phone 160 times per day. SMS can be used to promote, increase customer interaction, and update users on various activities [3]. The low cost of sending text messages, the rapid increase in mobile phone subscribers, the ease of use, and the fact that SMS does not require internet connectivity to be delivered all contribute positively to the popularity of SMS for spammers [3], [4].…”

Section: Introductionmentioning

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 Impact of Deep Learning Techniques on SMS Spam Filtering

Cited by 16 publications

References 13 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

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

Uncovering SMS Spam in Swahili Text Using Deep Learning Approaches

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