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@article{197262,
        author = {Aniket bhanyari and Ravi Khatri and Mehak sharma and Yuvraj Lad},
        title = {Identifying URL based attacks using IP data},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {6090-6096},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197262},
        abstract = {This paper presents a practical implementation of a system for identifying URL-based attacks using IP intelligence data. Unlike traditional approaches that rely solely on URL structure or webpage content, the proposed system integrates backend logic to extract IP addresses using DNS lookup and analyze them using threat intelligence techniques. The system is implemented as a web-based application where users can input URLs and receive real-time threat analysis. Key features include IP extraction, DNS resolution, and basic reputation analysis. The backend is developed using modern web technologies and demonstrates how machine learning and rule-based analysis can be combined for detecting malicious URLs. The system aims to provide a lightweight, efficient, and user-friendly solution for real-time cybersecurity applications.URL-based attacks such as phishing and malware distribution are increasing rapidly and pose serious threats to users and organizations. Traditional detection methods rely mainly on URL structure or blacklisting, which can be bypassed by attackers. This project presents a hybrid system for identifying malicious URLs using a combination of heuristic analysis and IP intelligence.
The system is implemented as a web-based application using Fast API. It analyses URLs in real-time by performing multiple checks such as URL structure analysis, domain similarity detection, domain age verification using WHOIS, DNS-based IP extraction, and integration with external threat intelligence APIs like Virus Total and Abuse IPDB. A scoring-based approach is used to classify URLs as Safe, Suspicious, or Malicious.
The results show that combining multiple techniques improves detection accuracy and reduces false positives. The system is efficient, scalable, and suitable for real-time cybersecurity applications.},
        keywords = {},
        month = {April},
        }