A Machine-Learning Approach to Phishing Detection and by I. S. Amiri, O. A. Akanbi, E. Fazeldehkordi PDF
By I. S. Amiri, O. A. Akanbi, E. Fazeldehkordi
Phishing is likely one of the so much widely-perpetrated different types of cyber assault, used to collect delicate info comparable to bank card numbers, checking account numbers, and consumer 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 examine to illustrate how a computing device studying set of rules can be utilized as an efficient and effective device in detecting phishing web content and designating them as details defense threats. this technique can turn out important to a large choice of companies and agencies who're looking recommendations to this long-standing possibility. A Machine-Learning method of Phishing Detetion and safeguard additionally presents info defense researchers with a place to begin for leveraging the computing device set of rules method as an answer to different details protection threats.
Discover novel study into the makes use of of machine-learning rules and algorithms to notice and forestall phishing attacks
Help your corporation or association stay away from high priced harm from phishing sources
Gain perception into machine-learning suggestions for dealing with numerous details defense threats
About the Author
O.A. Akanbi obtained his B. Sc. (Hons, info know-how - software program Engineering) from Kuala Lumpur Metropolitan collage, Malaysia, M. Sc. in details safety from college Teknologi Malaysia (UTM), and he's shortly a graduate pupil in computing device technological know-how at Texas Tech collage His region of analysis is in CyberSecurity.
E. Fazeldehkordi acquired her Associate’s measure in desktop from the collage of technology and know-how, Tehran, Iran, B. Sc (Electrical Engineering-Electronics) from Azad college of Tafresh, Iran, and M. Sc. in details safeguard from Universiti Teknologi Malaysia (UTM). She presently conducts study in info safety and has lately released her examine on cellular advert Hoc community protection utilizing CreateSpace.
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Extra info for A Machine-Learning Approach to Phishing Detection and Defense
Y! (Pc)y(Pe)x. 3) δk is always positive. Thus when x and y δ Pc are given, as Pc increases k increases continuously from zero to unity. This demonstrates that the success of the majority voting scheme (like most decision combination schemes) directly depends on the reliability of the decision confidences delivered by the participating experts. It is also clear that as the confidences of the delivered decisions increase, the quality of the combined decision increases. Since (x − y − 1 ≥ 0), Recently, it has been demonstrated that although majority vote is by far the simplest of the variety of strategies used to combine multiple experts, if properly applied it can also be very effective.
Artificial neural network (ANN) consists of a collection of processing elements that are highly interconnected and transform a set of inputs to a set of desired outputs. The result of the transformation is determined by the characteristics of the elements and the weights associated with the interconnections among them. Since neural network gains experience over a period as it is being trained on the data related to the problem, the major disadvantage is in the time it takes for parameter selection and network learning.
The major disadvantage of this classifier is that the accuracy falls with increase in the size of the training set. In addition, previous researches have shown that KNN can achieve very accurate results, that are sometimes more accurate than those of the symbolic classifiers. It was shown in a study carried out by Kim and Huh, 2011 that KNN classifier achieved the best result compared to other classifier such as linear discriminate analysis (LDA), naıve Bayesian (NB), and support vector machine (SVM).
A Machine-Learning Approach to Phishing Detection and Defense by I. S. Amiri, O. A. Akanbi, E. Fazeldehkordi