Details

Autor(en) / Beteiligte

• Sankhwar, Shweta
• Pandey, Dhirendra
• Khan, Raees Ahmad
• Mohanty, Sachi Nandan

Titel

An anti‐phishing enterprise environ model using feed‐forward backpropagation and Levenberg‐Marquardt method

Ist Teil von

• Security and privacy, 2021-01, Vol.4 (1), p.n/a

Ort / Verlag

Boston, USA: Wiley Periodicals, Inc

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Wiley Online Library - AutoHoldings Journals

Beschreibungen/Notizen

• Phishing in an enterprise is serious issue rising in wide scale and complexity, as phishers use email phishing via obfuscated, malicious or phished URLs and continuously adapt or innovate their strategies to lure victims for identity theft for financial benefits. To gain victim's trust and confidence phishers have started using visceral factors and familiarity cues. Phishing is not always money centric; phisher defame the user's goodwill and character. Defamation in enterprise could be much more traumatic than being embarrassed at a social networking site. It is a challenging task to address this issue. It is evident through literature review that single phishing detection filter approaches are insufficient to detect phishing in enterprise environ. Therefore, a novel anti‐phishing model for enterprise using artificial neural network is proposed. In addition, this model effectively identifies whether the phishing email is known phishing or unknown phishing to reduce the trust and familiarity‐based email phishing enterprise environ. The feed‐forward backpropagation and Levenberg‐Marquart methods of ANN are adopted to enhance the URL classification process and with Fuzzy Inference System to get result with imprecise data of social features. The proposed model can accurately classify the known and unknown email phishing via URLs.