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Stream/Area:
-
Subject:
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Paper ID:
10.55041/IJSREM.RACCES074
Volume:
10 |
4 |
April 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Computer Science & Engineering
Bharathiyar Institute of Engineering for women (Autonomous) Salem, TamilNadu, India
Computer Science & Engineering
Bharathiyar Institute of Engineering for women (Autonomous) Salem, TamilNadu, India
Computer Science & Engineering
Bharathiyar Institute of Engineering for women (Autonomous) Salem, TamilNadu, India
Computer Science & Engineering
Bharathiyar Institute of Engineering for women (Autonomous) Salem, TamilNadu, India
Computer Science & Engineering
Bharathiyar Institute of Engineering for women (Autonomous) Salem, TamilNadu, India
With the rapid expansion of internet technologies and online services, cybersecurity threats have become a major concern for individuals, organizations, and governments. Among these threats, malicious URLs play a significant role in cyber-attacks such as phishing, malware distribution, identity theft, and financial fraud. Attackers frequently create deceptive web links that appear legitimate to trick users into revealing sensitive information ordownloadingharmful software. Traditional security systems, including blacklist-based detection methods, often fail to identify newly generated or dynamically changing malicious URLs. This research proposes is designed to identify and prevent harmful online activities in real time. The proposed system integrates advanced machine learning techniques with intelligent data analysis to detect suspicious URLs based on multiple characteristics such as lexical features, domain information, URL length, special characters, host-based attributes, and behavioural patterns. The SMART AI framework includes several stages such as data collection, data preprocessing, feature extraction, model training, and real-time threat classification.
KEYWORDS
In conclusion, the development of a Smart AI System for Malicious URL and Cyber Threat Detection plays a significant role in enhancing cybersecurity and protecting users from online threats. Malicious URLs are widely used in phishing attacks, malware distribution, and other cyber crimes, making it essential to detect and prevent them effectively. Traditional detection methods such as blacklist and signature-based techniques are limited in their ability to identify newly created or unknown malicious URLs. The use of Artificial Intelligence and Machine Learning techniques provides a more advanced and efficient solution for detecting cyber threats. By analyzing various features of URLs and learning patterns from large datasets, AI-based systems can accurately classify URLs as malicious or legitimate. Machine learning algorithms such as Random Forest, Support Vector Machine, and Decision Tree improve detection accuracy and help identify previously unseen threats.
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Publication Date
5/2/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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