Automatic test data generation for big data input values

Dissertant

al-Buqur, Suud Muhammad Abd

Thesis advisor

al-Junayni, Muhammad Ali Hashim
Qutaysh, Usamah

University

Isra University

Faculty

Faculty of Information Technology

Department

Department Software Engineering

University Country

Jordan

Degree

Master

Degree Date

2020

English Abstract

There is needed for the efficient intrusion detection system working over the network system to detect the whole possible attacks.

Intrusion detection is so much popular since the last two decades, where intruders attempted to break into or misuse the system.

There are many techniques used in intrusion detection (IDS) for protecting computers and networks from network-based and host-based attacks.

In this thesis, the proposed approach presents a new model for IDS using a bat algorithm that aims to select the best features using big data.

The proposed approach divided into several phases to extract and find all possible features that effect directly in the detection process.

The proposed approach was tested using the KNIME Analytics Platform based on Support Vector Machine (SVM) and Naive base classifiers.

The experiment results give a high accuracy (97.52%) with reducing the error classification into (2.47%) using the SVM classifier.

Main Subjects

Information Technology and Computer Science

Topics

• Intrusion detection systems(Computer security)
• Big data

No. of Pages

51

Table of Contents

• APA
• MLA
• AMA

Language

English

Data Type

Arab Theses

Record ID

BIM-985131