عنوان

A Comprehensive Evaluation of Feature-Based Malicious Website Detection

شماره

TL57004

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

عنوان اصلي

A Comprehensive Evaluation of Feature-Based Malicious Website Detection

نام عام مواد

[Thesis]

نام نخستين پديدآور

McGahagan, John F., IV

نام ساير پديدآوران

Cukier, Michel

نام ناشر، پخش کننده و غيره

University of Maryland, College Park

تاریخ نشرو بخش و غیره

2020

متن يادداشت

299 p.

جزئيات پايان نامه و نوع درجه آن

Ph.D.

کسي که مدرک را اعطا کرده

University of Maryland, College Park

امتياز متن

2020

متن يادداشت

Although the internet enables many important functions of modern life, it is also a ground for nefarious activity by malicious actors and cybercriminals. For example, malicious websites facilitate phishing attacks, malware infections, data theft, and disruption. A major component of cybersecurity is to detect and mitigate attacks enabled by malicious websites. Although prior researchers have presented promising results - specifically in the use of website features to detect malicious websites - malicious website detection continues to pose major challenges. This dissertation presents an investigation into feature-based malicious website detection. We conducted six studies on malicious website detection, with a focus on discovering new features for malicious website detection, challenging assumptions of features from prior research, comparing the importance of the features for malicious website detection, building and evaluating detection models over various scenarios, and evaluating malicious website detection models across different datasets and over time. We evaluated this approach on various datasets, including: a dataset composed of several threats from industry; a dataset derived from the Alexa top one million domains and supplemented with open source threat intelligence information; and a dataset consisting of websites gathered repeatedly over time. Results led us to postulate that new, unstudied, features could be incorporated to improve malicious website detection models, since, in many cases, models built with new features outperformed models built from features used in prior research and did so with fewer features. We also found that features discovered using feature selection could be applied to other datasets with minor adjustments. In addition: we demonstrated that the performance of detection models decreased over time; we measured the change of websites in relation to our detection model; and we demonstrated the benefit of re-training in various scenarios.

اصطلاح موضوعی

Artificial intelligence

اصطلاح موضوعی

Computer engineering

اصطلاح موضوعی

Computer science

مستند نام اشخاص تاييد نشده

McGahagan, John F., IV

مستند نام اشخاص تاييد نشده

Cukier, Michel

مستند نام تنالگان تاييد نشده

University of Maryland, College Park

نام الکترونيکي

فرمت انتشار

p

نوع ماده

[Thesis]

کد کاربرگه

276903

سطح دسترسي

a

تكميل شده

Y