Robust multimedia spam filtering based on visual, textual, and audio deep features and random forest

Kihal, Marouane; Hamza, Lamia

doi:10.1007/s11042-023-15170-x

Cited by 9 publications

(3 citation statements)

References 40 publications

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“…Kihal et al [25] present a robust multimedia spam filtering system, VTA-CNN-RF, capable of detecting spam across text, image, audio, and video modalities. Leveraging Convolutional Neural Networks (CNNs) for feature extraction and Random Forest (RF) for classification, the proposed model outperforms existing methods in spam identification.…”

Section: Advanced Models and Approachesmentioning

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 Models and Approachesmentioning

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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“…The performance results of the proposed method were obtained using the Dredze dataset and the ISH dataset. Kihal et al (Kihal & Hamza, 2023), proposed a CNN-based hybrid model for voice, image, and text spam mail classification. Feature extraction was done with CNN using audio files, video-image files, and text files.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, spam emails cause harmful content to enter their inboxes, and therefore, a lot of work has been done for spam detection. Spam emails are often text-based, but in some cases, spam can be sent with images, video, and audio files (Kihal & Hamza, 2023). This can fill their inboxes and cause users to be unable to use e-mail services.…”

Section: Introductionmentioning

confidence: 99%

LBP Özellik Çıkarma ve İstatistiksel Havuzlama Tabanlı Görüntü Spam Tespit Modeli

Yaman

2023

JCS

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Email, which stands for electronic mail, is a form of digital communication between two or more individuals. These technological instruments that facilitate communication can have a positive and negative impact on our lives due to junk e-mails, widely known as spam mail. These spam messages, which are typically delivered for commercial purposes by organizations/individuals for indirect or direct benefits, not only distract people but also consume a significant amount of system resources such as processing power, memory, and network bandwidth. In this study, a method based on LBP (Local Binary Patterns) feature extraction and statistical pooling is proposed to classify spam or raw (non-spam) images. Two datasets are used to test the proposed method. The ISH dataset is widely used in the literature and contains 1738 images. In addition to this dataset, the dataset our collect consists of 1015 images in total. Feature extraction was performed on these images. Obtained features were classified by SVM (Support Vector Machine) algorithm. In the proposed method, 98.56% and 79.01% accuracy were calculated for the ISH dataset and our collected dataset, respectively. The results obtained were compared with the studies in the literature.

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OEC Net: Optimal feature selection-based email classification network using unsupervised learning with deep CNN model

Borra,

Yukthika,

Bhargavi

et al. 2024

e-Prime - Advances in Electrical Engineering, Electronics and E

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Robust multimedia spam filtering based on visual, textual, and audio deep features and random forest

Cited by 9 publications

References 40 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

LBP Özellik Çıkarma ve İstatistiksel Havuzlama Tabanlı Görüntü Spam Tespit Modeli

OEC Net: Optimal feature selection-based email classification network using unsupervised learning with deep CNN model

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