International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | CyberShield.AI: A Web-Based Multi-Category Cyber Threat Detection and Vulnerability Assessment Framework |
|---|---|
| ABSTRACT | The rapid growth of digital communication and internet-based services has increased the risk of cyber threats such as phishing attacks, malicious URLs, fraudulent emails, suspicious APK files, insecure APIs, fake links, and deceptive SMS messages. These threats can compromise sensitive information, affect system security, and expose users to financial and privacy-related risks. Traditional security approaches may require users to rely on multiple tools to examine different types of potential threats, making the detection process difficult and time-consuming. To address these challenges, CyberShield.AI is proposed as a web-based cybersecurity threat scanning and analysis system that provides a centralized platform for identifying and assessing potentially harmful digital content. The system supports the scanning of six major categories of digital threats, including URLs, emails, APK files, APIs, fake links, and SMS messages. It analyses the submitted inputs and presents a vulnerability percentage along with a security assessment to help users understand the potential risks associated with the scanned content. The proposed system incorporates separate user and administrator interfaces to support secure access and centralized monitoring of scanning activities. Users can examine scan results, maintain their scanning history, search previous records, and download individual scan reports in PDF format for documentation and future reference. When potentially harmful content is identified, users can report the suspicious scan to the administrators for further review, whereas inputs assessed as safe are presented without raising unnecessary threat alerts. The administrator dashboard enables authorized administrators to monitor reported threats and manage relevant scanning activities. By integrating multiple scanning categories, report generation, scan history management, and threat reporting within a single platform, CyberShield.AI aims to improve the accessibility and organization of basic cybersecurity assessment. The system provides a practical approach to helping users recognize potential digital threats and make more informed decisions before interacting with suspicious online content. |
| AUTHOR | VISHAL V P, POOJA N ANNIGERE, KEERTHI MANJUNATH GEDDAPPANAVAR, PROF. CHAITHRA B M UG Students, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India Assistant Professor, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India |
| VOLUME | 190 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1411013 |
| pdf/13_CyberShield.AI A Web-Based Multi-Category Cyber Threat Detection and Vulnerability Assessment Framework.pdf | |
| KEYWORDS | |
| References |
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