Suspicious Link Analyzer Using Artificial Intelligence

Abstract: With the rapid expansion of internet usage and the increased sharing of hyperlinks through emails, messages, and social media platforms, the spread of malicious or suspicious links has become a major cybersecurity concern. These links can lead to phishing websites, malware downloads, or data theft. This paper proposes an AI-driven Suspicious Link Analyzer (SLA) system that can intelligently classify and detect suspicious or harmful URLs based on their features. The proposed model uses machine learning techniques to identify patterns in URLs and predict potential threats. This paper discusses the architecture, dataset, feature extraction, and the performance of various classification algorithms in detecting suspicious links.

Authors

Publication Details

Journal
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Published
2025-05-04
DOI
https://doi.org/10.55041/ijsrem46882
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

Suspicious Link Analyzer Using Artificial Intelligence

M. Mamatha
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Network Security and Intrusion Detection
article

Suspicious Link Analyzer Using Artificial Intelligence

M. Mamatha
article en

Abstract

Abstract: With the rapid expansion of internet usage and the increased sharing of hyperlinks through emails, messages, and social media platforms, the spread of malicious or suspicious links has become a major cybersecurity concern. These links can lead to phishing websites, malware downloads, or data theft. This paper proposes an AI-driven Suspicious Link Analyzer (SLA) system that can intelligently classify and detect suspicious or harmful URLs based on their features. The proposed model uses machine learning techniques to identify patterns in URLs and predict potential threats. This paper discusses the architecture, dataset, feature extraction, and the performance of various classification algorithms in detecting suspicious links.

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTVol. 09(05)
Openalex Percentile: Top 91%
Network Security and Intrusion Detection
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Suspicious Link Analyzer Using Artificial Intelligence — M. Mamatha · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (2025) | TGRS Research Map | TGRS