AN ADAPTIVE MACHINE LEARNING BASED APPROACH FOR PHISHING DETECTION USING HYBRID FEATURES

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YADOLLAHI MOHAMMAD MEHDI; Shoeleh Farzaneh; Serkani Elham; MADANI AFSANEH; GHARAEE HOSSEIN

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2019 | Issue:5

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Title

AN ADAPTIVE MACHINE LEARNING BASED APPROACH FOR PHISHING DETECTION USING HYBRID FEATURES

Author(s)

YADOLLAHI MOHAMMAD MEHDI | Shoeleh Farzaneh | Serkani Elham | MADANI AFSANEH | GHARAEE HOSSEIN

Keywords

CYBER SECURITY

PHISHING DETECTION

WEB SECURITY

LEARNING CLASSIFIER SYSTEM

Abstract

NOWADAYS, PHISHING IS ONE OF THE MOST USUAL WEB THREATS WITH REGARDS TO THE SIGNIFICANT GROWTH OF THE WORLD WIDE WEB IN VOLUME OVER TIME. PHISHING ATTACKERS ALWAYS USE NEW (ZERO-DAY) AND SOPHISTICATED TECHNIQUES TO DECEIVE ONLINE CUSTOMERS. HENCE, IT IS NECESSARY THAT THE ANTI-PHISHING SYSTEM BE REAL-TIME AND FAST AND ALSO LEVERAGES FROM AN INTELLIGENT PHISHING DETECTION SOLUTION. HERE, WE DEVELOP A RELIABLE DETECTION SYSTEM WHICH CAN ADAPTIVELY MATCH THE CHANGING ENVIRONMENT AND PHISHING WEBSITES. OUR METHOD IS AN ONLINE AND FEATURE-RICH MACHINE LEARNING TECHNIQUE TO DISCRIMINATE THE PHISHING AND LEGITIMATE WEBSITES. SINCE THE PROPOSED APPROACH EXTRACTS DIFFERENT TYPES OF DISCRIMINATIVE FEATURES FROM URLS AND WEBPAGES SOURCE CODE, IT IS AN ENTIRELY CLIENT-SIDE SOLUTION AND DOES NOT REQUIRE ANY SERVICE FROM THE THIRD-PARTY. THE EXPERIMENTAL RESULTS HIGHLIGHT THE ROBUSTNESS AND COMPETITIVENESS OF OUR ANTI-PHISHING SYSTEM TO DISTINGUISH THE PHISHING AND LEGITIMATE WEBSITES.

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APA: Copy

YADOLLAHI, MOHAMMAD MEHDI, Shoeleh, Farzaneh, Serkani, Elham, MADANI, AFSANEH, & GHARAEE, HOSSEIN. (2019). AN ADAPTIVE MACHINE LEARNING BASED APPROACH FOR PHISHING DETECTION USING HYBRID FEATURES. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/949092/en

Vancouver: Copy

YADOLLAHI MOHAMMAD MEHDI, Shoeleh Farzaneh, Serkani Elham, MADANI AFSANEH, GHARAEE HOSSEIN. AN ADAPTIVE MACHINE LEARNING BASED APPROACH FOR PHISHING DETECTION USING HYBRID FEATURES. 2019. Available from: https://sid.ir/paper/949092/en

IEEE: Copy

MOHAMMAD MEHDI YADOLLAHI, Farzaneh Shoeleh, Elham Serkani, AFSANEH MADANI, and HOSSEIN GHARAEE, “AN ADAPTIVE MACHINE LEARNING BASED APPROACH FOR PHISHING DETECTION USING HYBRID FEATURES,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2019, [Online]. Available: https://sid.ir/paper/949092/en

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