International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 04 | Apr 2025
p-ISSN: 2395-0072
www.irjet.net
Advancing Security through Innovative Spam Detection Algorithm Akhil Thota1, Jayanth meduri2, Sadanapalli Himesh3 , Mallikarjun Yaramadhi4 1 B.Tech 4th Year, Dept. of CSE(DS), Institute of Aeronautical Engineering
2 B.Tech 4th Year, Dept. of CSE(DS), Institute of Aeronautical Engineering 3 B.Tech 4th Year, Dept. of CSE(DS), Institute of Aeronautical Engineering
4 Assistant Professor, Dept. of CSE(DS), Institute of Aeronautical Engineering, Telangana, India
---------------------------------------------------------------------***--------------------------------------------------------------------The results demonstrate that machine learning models can Abstract - Spam messages and malicious URLs are major
effectively distinguish spam from legitimate content, offering a reliable defense mechanism against cyber threats. This research contributes to the advancement of automated spam detection and lays the groundwork for future improvements incorporating deep learning and real-time web monitoring techniques.
threats in digital communication, impacting email, social media, and various platforms. Detecting spam content is essential for protecting users and systems from potential cyberattacks. This study presents a machine learning-based approach for classifying SMS and URLs as spam or legitimate. SMS classification leverages Natural Language Processing (NLP) techniques with TF-IDF vectorization, while URL classification extracts key features like length, entropy, subdomains, and HTTPS usage, utilizing a Voting Classifier for improved accuracy. The system is implemented as a Streamlit-based web application for real-time detection. Experimental results show high accuracy, highlighting the effectiveness of the approach. Future work aims to integrate deep learning and domain reputation analysis for enhanced performance.
1.1 EXISTING SYSTEMS
i. Rule-Based Filtering Systems • Traditional spam detection methods rely on predefined rules and heuristics to filter out spam messages and malicious URLs. • Examples include blacklists, keyword-based filtering, and regex-based pattern matching.
Keywords— Spam detection, malicious URLs, cybersecurity, phishing detection, machine learning, naive bayes, natural language processing (NLP), URL Filtering
• While effective against simple spam patterns, these methods fail to adapt to evolving spam techniques, leading to high false negativesand poor scalability.
1. INTRODUCTION Malicious URLs and spam messages have grown to be a serious cybersecurity risk as our reliance on digital communication grows. Phishing attempts, virus dissemination, and fraudulent links are common in spam messages, endangering individuals as well as organizations. Machine learning (ML) approaches are necessary for efficient spam detection since traditional rule- based filtering methods are unable to keep up with the constantly changing tactics of spammers.
ii. Machine Learning-Based Systems
This study presents a machine learning-driven approach to classify SMS messages and URLs as spam or legitimate. The SMS classification process utilizes Natural Language Processing (NLP) techniques, including TF-IDF vectorization and stemming, to extract key textual patterns for analysis. Meanwhile, the URL classification module derives multiple structural and statistical features, such as URL length, entropy, presence of subdomains, HTTPS usage, and special characters, leveraging a Voting Classifier for enhanced accuracy.
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Impact Factor value: 8.315
Support Vector Machines (SVM) – Effective in distinguishing spam and non-spam messages based on feature vectors.
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Random Forest and Decision Trees – Applied to URL classification by analyzing structural and lexical features
1.2 DEMERITS OF EXISTING SYSTEM
To make the system accessible and user-friendly, the solution is deployed as a Stream lit-based web application, enabling real- time spam detection for both SMS and URLs.
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Rule-based approaches are static and ineffective against evolving spam techniques.
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Machine learning models may suffer from overfitting, high false positives, and reliance on feature engineering.
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Deep learning models require large datasets, high computational resources, and may lack interpretability.
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