Knowledge-Based Approach to Detect Potentially Risky Websites

• Published in: IEEE access Vol. 9; pp. 11633 - 11643
• Main Authors: Prieto, Juan Carlos, Fernandez-Isabel, Alberto, De Diego, Isaac Martin, Ortega, Felipe, Moguerza, Javier M.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Classifiers; combination of information; Domains; Feature extraction; Fraud; Information sources; Internet; Knowledge based systems; Knowledge bases (artificial intelligence); Knowledge engineering; Machine learning; Malware; malware alerts; Optimization; Predictive models; Quality of service; Risky website detection; similarity metrics; Websites
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3051374

Nowadays, fraudulent and malicious websites are emerging as a harmful and very common problem on the Internet. It causes huge money losses and irreparable damage for both companies and particulars. To face this situation, governments have approved multiple law projects. This way, the legality on the Internet is being enforced and sanctions to those offenders who develop illegal or malicious activities are being imposed. However, governments still need a way to simplify the classification of websites into risky or non-risky, since most of this work is manual. This paper presents the DOmains Classifier based on RIsky Websites ( DOCRIW ) framework to detect domains that contain possible fraud or malicious content. It is based on two main components. The first component is a previously built knowledge base containing information from risky websites. The second one complements the system with a binary classifier able to label a website (as risky or not) considering just its domain. The system makes use of web information sources and includes host-based variables. It also applies similarity measures, supervised learning algorithms and optimization methods to enhance its performance. The presented work is experimental, rendering promising outcomes.