عنوان

A new model for worm detection and response :

شماره

TLets554023

عنوان اصلي

A new model for worm detection and response :

نام عام مواد

[Thesis]

نام نخستين پديدآور

Mohd Saudi, Madihah

عنوان اصلي به قلم نويسنده ديگر

development and evaluation of a new model based on knowledge discovery and data mining techniques to detect and respond to worm infection by integrating incident response, security metrics and apoptosis

نام ساير پديدآوران

Cullen, Andrea J. ; Woodward, Mike E.

نام ناشر، پخش کننده و غيره

University of Bradford

تاریخ نشرو بخش و غیره

2011

جزئيات پايان نامه و نوع درجه آن

Thesis (Ph.D.)

امتياز متن

2011

متن يادداشت

Worms have been improved and a range of sophisticated techniques have been integrated, which make the detection and response processes much harder and longer than in the past. Therefore, in this thesis, a STAKCERT (Starter Kit for Computer Emergency Response Team) model is built to detect worms attack in order to respond to worms more efficiently. The novelty and the strengths of the STAKCERT model lies in the method implemented which consists of STAKCERT KDD processes and the development of STAKCERT worm classification, STAKCERT relational model and STAKCERT worm apoptosis algorithm. The new concept introduced in this model which is named apoptosis, is borrowed from the human immunology system has been mapped in terms of a security perspective. Furthermore, the encouraging results achieved by this research are validated by applying the security metrics for assigning the weight and severity values to trigger the apoptosis. In order to optimise the performance result, the standard operating procedures (SOP) for worm incident response which involve static and dynamic analyses, the knowledge discovery techniques (KDD) in modeling the STAKCERT model and the data mining algorithms were used. This STAKCERT model has produced encouraging results and outperformed comparative existing work for worm detection. It produces an overall accuracy rate of 98.75% with 0.2% for false positive rate and 1.45% is false negative rate. Worm response has resulted in an accuracy rate of 98.08% which later can be used by other researchers as a comparison with their works in future.

موضوع مستند نشده

Apoptosis; Data mining; Security metrics; Knowledge discovery technique (KDD); Standard Operating Procedures (SOP); Worm incident response; Static analysis; Dynamic analysis; Worm rules; Worm classification; STAKCERT model; Worm detection; Internet security

مستند نام اشخاص تاييد نشده

Mohd Saudi, Madihah

مستند نام اشخاص تاييد نشده

Cullen, Andrea J. ; Woodward, Mike E.

مستند نام تنالگان تاييد نشده

University of Bradford

نام الکترونيکي

فرمت انتشار

p

نوع ماده

[Thesis]

کد کاربرگه

276903

سطح دسترسي

a

تكميل شده

Y