Download 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 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 details entered through a website. The authors of A Machine-Learning method of Phishing Detetion and protection have performed examine to illustrate how a desktop studying set of rules can be utilized as an efficient and effective device in detecting phishing web content and designating them as details safety threats. this system can turn out invaluable to a wide selection of companies and firms who're looking recommendations to this long-standing possibility. A Machine-Learning method of Phishing Detetion and protection additionally offers info safeguard researchers with a place to begin for leveraging the computing device set of rules technique as an answer to different details defense threats.
Discover novel learn into the makes use of of machine-learning ideas and algorithms to become aware of and stop phishing attacks
Help what you are promoting or association steer clear of high priced harm from phishing sources
Gain perception into machine-learning thoughts for dealing with quite a few info safety threats
About the Author
O.A. Akanbi bought his B. Sc. (Hons, details expertise - software program Engineering) from Kuala Lumpur Metropolitan college, Malaysia, M. Sc. in details protection from collage Teknologi Malaysia (UTM), and he's almost immediately a graduate pupil in laptop technological know-how at Texas Tech collage His quarter of analysis is in CyberSecurity.
E. Fazeldehkordi bought her Associate’s measure in laptop from the college of technological know-how and expertise, Tehran, Iran, B. Sc (Electrical Engineering-Electronics) from Azad college of Tafresh, Iran, and M. Sc. in info safeguard from Universiti Teknologi Malaysia (UTM). She at the moment conducts study in details safety and has lately released her study on cellular advert Hoc community safety utilizing CreateSpace.
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Extra info for A Machine-Learning Approach to Phishing Detection and Defense
Sample text
5% Abbasi and Chen, 2009b. , 2005). While6this is a simple technique to protect6against password phishing; it is not secure against6offline dictionary attacks, key6logger attacks, DNS6cache poisoning attacks, and cannot be6securely applied when the6user does not have6the privileges to install the tool on the6computer. , 2006). 2. 2 Anti-Phishing Tools Tools Prime feature Limitations Google Safe Browsing Uses a blacklist of phishing URLs to identify a phishing site Might not recognize phishing sites not present in the blacklist NetCraft Tool Bar Risk rating system used.
The result demonstrated mat these application paths may be used as a basis for further investigation to expose and document the primary exploits and tools used by hackers to compromise web servers, which could lead to the revelation of the aliases or identities of me criminals. , 2010) The research proposed an intelligent, resilient aid effective model that is based on using association and classification data mining algorithms. They used a number of different listing data mining association and classification techniques The experimental results demonstrated me feasibility of using associative classification techniques in real applications and its better performance as compared to other traditional classifications algorithms.
2010). The design approach can be summarized into three key steps. (1) an image capture of the current website in the user’s web browser (2) the conversion of captured image into computer readable text using optical character recognition, and (3) input the converted text into a search engine to retrieve results and evaluate the page rank. , 2010). The drawback of GoldPhish is the time it takes in the rendering of a webpage. , 2010). 3 Visual Similarity-Based Approach Chen et al. (2009) used screenshot of web pages to identify phishing sites.