دورية أكاديمية
Malicious URL Detection to Avoid Web Crime Using Machine Learning Techniques.
| العنوان: | Malicious URL Detection to Avoid Web Crime Using Machine Learning Techniques. |
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| المؤلفون: | RAJESWARI, A., S., MONIKA, G., NIVETHA, G., SANGEETHA DEVI |
| المصدر: | INFOCOMP: Journal of Computer Science; Dec2023, Vol. 22 Issue 2, p1-6, 6p |
| مصطلحات موضوعية: | UNIFORM Resource Locators, MACHINE learning, INTERNET fraud, BOOSTING algorithms, CYBERTERRORISM, CRIME |
| مستخلص: | Today the foremost necessary concern within the field of cyber security is finding the intense issues that create loss in secure data. In recent years, most offensive strategies are applied by spreading malicious and phishing URLs. An accidental visit to a malicious website will trigger pre-designed criminal activity. The phishing website has evolved as a serious cyber security threat in recent times. Phishing may be a type of online fraud wherever a spoofed website tries to gain access to user's sensitive data by tricking the user into believing that it's a benign website. ML algorithms are one of the effective techniques for malicious website detection. The proposed system is enforced with the assistance of Gradient Boosting classifier, it considers 27 major features of the URL to detect whether the URL is legitimate or malicious based on varied discriminative options and attributes of the address. The model can find whether the address is safe or unsafe. It is found that the accuracy rate of gradient boosting algorithm is 98% and the accuracy rate of other existing algorithm is 96% or 95% respectively. Comparatively the proposed system outstand the performance of the existing system. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: | Complementary Index |
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