Affiliations: [a]
Vellore Institute of Technology, Chennai, Tamil Nadu, India
| [b]
Mother Teresa Women’s University, Kodaikanal, Tamil Nadu, India
| [c]
Madras Institue of Technology, Anna University, Chennai, Tamil Nadu, India
Correspondence:
[*]
Corresponding author. T. Nathezhtha, Vellore Institute of Technology, Chennai, Tamil Nadu, India. E-mail: [email protected].
Abstract: In recent days, malicious users try to captivate the consumers using their fraudulent marketing URL post in social networking sites. Such malicious URL posted by fake users in Social Networking Services (SNS) is hard to identify. Therefore, there occurs a need to detect such fraudulent URLs in SNS. In order to detect such URLS, this paper proposes a SNS Fraudulent Detection (SFD) scheme. The proposed SFD scheme includes a Deterministic Finite Automata Tokenization (DFA-T) and Web Crawler (WC) based Neuro Fuzzy System (WC-NFS). DFA-T extracts the URL features and calculates a Penalty Score (PS) based on the malicious words in the extracted URL. The DFA extracted URL features with PS are fed into WC-NFS. Subsequently, the WC fetches the numeric WC-Index (WCI) value from the URLs which are added to the WC-NFS. The existing URL data set is used to identify the malicious web links and suitable machine learning techniques are used to identify the malicious URLs. From the experimental results, it is found that the proposed SFD provides 92.6 % accuracy in classifying the benign from malicious URLs when compared with the existing methods.
Keywords: Consumer electronics, fraudulent, web crawler, social networking service, malicious users
