Experimental evaluations on the UCI SMS Spam Collection dataset demonstrate that the TF-IDF-PCA-DT pipeline achieves a detection accuracy of 99% while reducing model size by 77% and inference time by 75%, demonstrating the practicality of the proposed framework for real-world deployment in resource-constrained environments.
Abstract
The proliferation of mobile communications has led to a significant increase in SMS spam, posing challenges related to security, privacy, and user experience. Although numerous machine-learning-based spam detection approaches have been proposed, developing systems that are simultaneously lightweight and resilient to adversarial manipulation remains an open problem. This paper proposes an SMS spam detection framework that incorporates multiple feature extraction methods, including bag-of-words (BoW), Term Frequency–Inverse Document Frequency (TF-IDF), and N-gram models with dimensionality reduction using principal component analysis (PCA), followed by classification using decision tree (DT) and Logistic Regression (LogReg) models. Experimental evaluations on the UCI SMS Spam Collection dataset demonstrate that the TF-IDF-PCA-DT pipeline achieves a detection accuracy of 99% while reducing model size by 77% and inference time by 75%. Robustness evaluation under adversarial text perturbations indicates minimal performance degradation, maintaining an accuracy of 96.5%. These findings demonstrate the practicality of the proposed framework for real-world deployment in resource-constrained environments.
A high-efficiency detection framework utilizing DistilBERT, a distilled knowledge representation of the BERT transformer is proposed, substantiate the viability of Knowledge Distillation as a mechanism to deploy state-of-the-art semantic security filters on edge infrastructure.
Mrinal Mrinal, Neeraj Kumar· International Journal of Cre...· 0 citations
Experimental results on English and Korean SMS datasets indicate that LLMs are highly robust for spam detection and can provide interpretable rationales by leveraging their language understanding capabilities, suggesting that LLMs are a highly effective and practical option for modern SMS spam filtering.
Minseok Hur, Sooyon Seo, Jaeho Hwang et al.· International Journal of Inf...· 0 citations
Email communication is one of the most significant modes of digital interaction, playing a central role in business communication, personal correspondence, and information exchange across the modern world. The increasing volume of unsolicited, deceptive, and malicious emails, commonly known as spam, has made accurate spam detection a challenging task for email service providers, organizations, and individual users. Traditional filtering methods primarily depend on static rule-based approaches and manually curated blacklists, which often fail to adapt to rapidly evolving spam patterns and result in inaccurate classification. Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) have enabled the development of intelligent systems capable of analyzing email content and generating accurate spam classifications using data-driven approaches digital communication. The proposed system utilizes textual features such as word frequency, message structure, and content patterns to classify emails as spam or legitimate (ham) through supervised learning techniques.
The email dataset undergoes preprocessing procedures, including text cleaning, tokenization, stop-word removal, stemming, feature extraction using TF-IDF vectorization, and train-test splitting before model training. The predictive model is developed using Python and Scikit-learn, while Flask is employed to create a web-based interface that allows users to enter email content and obtain real-time spam classification.
M. K, S. Nandhini· International Journal of Cre...· 0 citations
A hybrid browser-resident phishing detection framework that combines three complementary detection mechanisms: a locally executed Random Forest model using URL lexical features, lightweight real-time DOM structure analysis, and VirusTotal’s multi-engine reputation service is proposed.
Muhammad Arshad, Beena Sherin Kuriakose, C. W. Onn et al.· Frontiers of Computer Scienc...· 0 citations
Despite the advancements made by researchers, spam emails remain one of the biggest challenges in the field of cybersecurity. Spam emails can serve as phishing emails or carry viruses that compromise the security of an organization's system. Current detection techniques depend on supervised learning or rely on cloud-based services, which can compromise user data privacy and affect implementation flexibility. This paper evaluates the capability of five large language models (LLMs) in zero-shot spam email classification. The models used in this study include llama3.1:8b, deepseek-r1:8b, gemma3:4b, falcon3:7b, and mistral:7b. In addition to predicting whether the email is spam or not, the LLM was also asked to generate an explanation of its prediction in natural language form. The experiments were conducted on two benchmark datasets: the Ling and TREC2007 datasets. In terms of performance, llama3.1:8b outperformed other LLMs when evaluated on the TREC2007 dataset (98.78% accuracy) and deepseek-r1:8b had the best performance on the Ling dataset (98.79%). The results show that open-weight LLMs can achieve competitive spam detection performance in a local, privacy-preserving environment without any fine-tuning.
Vusal Shahbazov· 2026 7th International Confe...· 0 citations
Spam detection on SMS messaging has not received as much attention from researchers recently as the spam detection studies on emails or social media platforms. However, spam SMS messaging can be more intrusive, annoying, and harmful. Thus, detecting and filtering spam SMS messages is becoming a priority that saves human productivity. This work aims to introduce a dynamic, accurate, and efficient machine learning-based spam detection technique for SMS messaging. It aims at protecting users and businesses from spam SMS attacks. It aims to detect and identify suspicious messages that contain promotional, misleading, irrelevant, or harmful content. It primarily aims to test and evaluate the impact of feature extraction methods on the performance of machine-learning-based spam detection. Several text feature extraction techniques have been used and tested, including classical, statistical, contextual, and advanced embedding techniques. An extensive set of experiments has been presented on benchmark datasets in this field. From the comparative study, we can infer that all investigated feature extraction techniques have achieved high accuracy (90%+) on the in-domain dataset. However, their performance decreased when they were tested on the out-of-domain dataset (70%+). The advanced embedding techniques achieved the best performance across both datasets compared to the other tested feature extraction models.
A. Ababneh, Maram Bani Younes· Journal of Cybersecurity and...· 0 citations