Is this URL Safe: Detection of Malicious URLs Using Global Vector for Word Representation

Abstract

Users are frequently exposed to many unknown links through advertisements and emails. These links may contain URLs to mount targeted attacks like spamming, phishing, and malware installation. Using blacklist of URLs is the most widely used defense mechanism to detect a malicious URLs. However, automatically generating such a list for fresh malicious URLs is challenging. Detecting a URL as malicious using the lexico- graphical approach is an important research problem. This paper proposes a malicious URL detection mechanism using natural language processing. We use features including word vector representation obtained through GloVe along with statistical cues and n-gram on blacklist words. The proposed approach is efficient, and it does not require inputs from external servers to identify malicious URLs. Experiments are performed on 227,909 size database containing 80,128 benign and 147,781 malicious URLs. Proposed system has achieved an accuracy of 89% for ANN model with GloVe based features.