Download e-book for iPad: A Machine-Learning Approach to Phishing Detection and by I. S. Amiri, O. A. Akanbi, E. Fazeldehkordi

By I. S. Amiri, O. A. Akanbi, E. Fazeldehkordi

ISBN-10: 0128029277

ISBN-13: 9780128029275

Phishing is without doubt one of the such a lot widely-perpetrated kinds of cyber assault, used to assemble delicate info equivalent to bank card numbers, checking account numbers, and consumer logins and passwords, in addition to different info entered through an internet site. The authors of A Machine-Learning method of Phishing Detetion and protection have performed examine to illustrate how a laptop studying set of rules can be utilized as an efficient and effective device in detecting phishing web content and designating them as info protection threats. this system can turn out priceless to a wide selection of companies and companies who're looking options to this long-standing chance. A Machine-Learning method of Phishing Detetion and safeguard additionally offers info safety researchers with a kick off point for leveraging the laptop set of rules strategy as an answer to different details protection threats.

Discover novel learn into the makes use of of machine-learning rules and algorithms to observe and forestall phishing attacks
Help what you are promoting or association stay away from expensive harm from phishing sources
Gain perception into machine-learning recommendations for dealing with a number of info safeguard threats
About the Author

O.A. Akanbi bought his B. Sc. (Hons, details know-how - software program Engineering) from Kuala Lumpur Metropolitan collage, Malaysia, M. Sc. in details protection from collage Teknologi Malaysia (UTM), and he's shortly a graduate pupil in computing device technology at Texas Tech college His region of study is in CyberSecurity.

E. Fazeldehkordi acquired her Associate’s measure in laptop from the collage of technology and expertise, Tehran, Iran, B. Sc (Electrical Engineering-Electronics) from Azad collage of Tafresh, Iran, and M. Sc. in details protection from Universiti Teknologi Malaysia (UTM). She at present conducts examine in details defense and has lately released her examine on cellular advert Hoc community defense utilizing CreateSpace.

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Extra resources for A Machine-Learning Approach to Phishing Detection and Defense

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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.

Lookup-based systems suffer from high false negatives whereas classifier systems suffer from high false positives. To better detect fraudulent websites, it was proposed by Fahmy and Ghoneim (2011), as an efficient hybrid system that is based on both lookup and a support vector machine classifier that checks features derived from websites URL, text, and linkage. 6). Xiang and Hong (2009) proposed a hybrid phish-detection approach based on information extraction (IE) and information retrieval (IR) techniques.

3. Linkguard analysis in various classified hyperlink. (Chen and Guo, 2006) Character-based anti-phishing technique utilizes characteristics of hyperlink in order to identify phishing links. Linkguard (Chen and Guo, 2006) is a tool that implements this technique. 3. For detection of phishing sites LinkGuard, the DNS names from the actual and the visual links will be initially extracted and then matches the actual and visual DNS names, if these names do not match, then it is phishing of category 1 and if dotted decimal IP address is directly used in actual DNS, it is then a potential phishing attack of category 2 (Chen and Guo, 2006).

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A Machine-Learning Approach to Phishing Detection and Defense by I. S. Amiri, O. A. Akanbi, E. Fazeldehkordi


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