By I. S. Amiri, O. A. Akanbi, E. Fazeldehkordi
Phishing is among the such a lot widely-perpetrated sorts of cyber assault, used to assemble delicate info equivalent to bank card numbers, checking account numbers, and person logins and passwords, in addition to different info entered through a website. The authors of A Machine-Learning method of Phishing Detetion and safety have performed study to illustrate how a computer studying set of rules can be utilized as an efficient and effective device in detecting phishing web pages and designating them as info defense threats. this technique can end up worthwhile to a wide selection of companies and companies who're looking suggestions to this long-standing possibility. A Machine-Learning method of Phishing Detetion and safeguard additionally presents details protection researchers with a kick off point for leveraging the desktop set of rules procedure as an answer to different info safety threats.
Discover novel study into the makes use of of machine-learning rules and algorithms to notice and stop phishing attacks
Help your corporation or association steer clear of high priced harm from phishing sources
Gain perception into machine-learning thoughts for dealing with quite a few details defense threats
About the Author
O.A. Akanbi bought his B. Sc. (Hons, info expertise - software program Engineering) from Kuala Lumpur Metropolitan college, Malaysia, M. Sc. in details safety from collage Teknologi Malaysia (UTM), and he's shortly a graduate scholar in desktop technology at Texas Tech collage His zone of analysis is in CyberSecurity.
E. Fazeldehkordi got her Associate’s measure in laptop from the college of technology and know-how, Tehran, Iran, B. Sc (Electrical Engineering-Electronics) from Azad college of Tafresh, Iran, and M. Sc. in details safety from Universiti Teknologi Malaysia (UTM). She presently conducts study in details safety and has lately released her study on cellular advert Hoc community protection utilizing CreateSpace.
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Extra resources for A Machine-Learning Approach to Phishing Detection and Defense
1% false positive. Chen et al. (2009) claimed that screenshot analysis lack efficiency in proper detection of online phishing. Fu et al. (2006) utilized Earth Mover’s Distance (EMD) to associate low-resolution screen capture of a web page. Images of web pages are denoted through the aid of image pixel color (alpha, red, green, and blue) and the centroid of its position distribution in the image. They used machine learning to select different threshold appropriate for different web pages. , 2010).
1997) presented a system having three voting nonlinear classifiers: two of them based on the multilayer perceptron (MLP), and one using the moments method. Parker (1995) has reported voting methods for multiple autonomous agents. Ji and Ma (1997) have reported a learning method to combine weak classifiers, where weak classifiers are linear classifiers (perceptron) which can do little better than making random guesses. The authors have demonstrated, both theoretically and experimentally, that if the weak classifiers are properly chosen, their combinations can achieve a good generalization performance with polynomial space-and time-complexity.
The authors have demonstrated, both theoretically and experimentally, that if the weak classifiers are properly chosen, their combinations can achieve a good generalization performance with polynomial space-and time-complexity. 6 NORMALIZATION As proposed by Al Shalabi and Shaaban (2006), data usually collected from multiple resources and stored in data warehouse may include multiple databases, data cubes, or flat files and as such could result to different issues arising during integration of data needed for mining and discovery.