Mitigation of Phishing Attacks

الموضوعات

علوم تطبيقية وتكنولوجية

الملخص

Phishing is a semantic attack that takes advantage of weaknesses caused by the human factor, which are exploited through electronic communication channels. Through social engineering, an attacker may craft a message that lures the victim into performing certain actions for attacker's benefit. The phishing problem is broad and enhancing the mitigation process often requires multiple solutions, such as: user education, user interface enhancements, and automated classification of phishing messages. The contributions of this thesis can be summarized in the following points: * A review of the phishing literature and state of the art phishing mitigation techniques. This incorporates both: the user training approaches as well as software enhancement techniques. * The construction of a highly accurate anti-phishing email classifier via the use of Machine Learning algorithms. This phishing email classifier is the outcome of an empirical evaluation of a number of feature selection methods, which resulted in finding a highly effective features subset. To the best of our knowledge, this is the most accurate publicly known anti-phishing email classifier that uses a single classification algorithm. * A novel URL tokenization technique that, by analyzing the URLs lexically following a training phase, achieves a classification accuracy of 97%. To the best of our knowledge, this is the most accurate publicly known website classification technique that is solely based on lexical URL analysis.

روابط وملفات

التعريف والنوع

رقم الوثيقة

edffa2a2-267b-4ee2-89a4-349a3596e686

رقم العقد

0

نوع الوسائط

Crawler

نوع المحتوى

الرسائل العلمية

صيغة المصدر

رسائل ماجيستير

نوع الملف

pdf text

أسماء الملفات

2284066_1.pdf

بيانات النشر

ألقاب المؤلفين

[{"name_ar":" Andrew Jones ","title_ar":"اشراف","title_en":"Supervision"},{"name_ar":" Youssef Iraqi","title_ar":"اشراف","title_en":"Supervision"},{"name_ar":"Mahmoud Khonji","title_ar":"اعداد","title_en":"Preparation"}]

اللغة

English

المصدر والدورية

اسم المصدر

Mitigation of Phishing Attacks

المحتوى والصفحات

عدد الصفحات

0

كلمات الباحثين

Social Engineering, Phishing E-Mail Classification, Machine Learning, Feature Subset Selection, Phishing Website Classification, Lexical URL Analysis. i

إشراف وإعداد

الإشراف

Andrew Jones , Youssef Iraqi

الإعداد

Mahmoud Khonji

الاقتباسات الببليوغرافية