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035 a| (Sirsi) o1144181693;

035 a| (OCoLC)1144181693;

049 a| EREE;

090 a| TK5105.59;

100 1 a| Lakshminarayana, Deepthi Hassan,e| author.?| UNAUTHORIZED;

245 10 a| Intrusion detection using machine learning algorithms /c| by Deepthi Hassan Lakshminarayana.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2019.;

300 a| 61 pages :b| illustrations (chiefly color);

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text fileb| PDFc| 715.9 KB2| rda;

538 a| System requirements: Adobe Reader.;

538 a| Mode of access: World Wide Web.;

502 b| M.S.c| East Carolina Universityd| 2019.;

500 a| Presented to the faculty of the Department of Computer Science;

500 a| Advisor: Nasseh Tabrizi;

500 a| Title from PDF t.p. (viewed May,11 2020).;

520 3 a| With the growing rate of cyber-attacks, there is a significant need for intrusion detection systems (IDS) in networked environments. As intrusion tactics become more sophisticated and more challenging to detect, this necessitates improved intrusion detection technology to retain user trust and preserve network security. Over the last decade, several detection methodologies have been designed to provide users with reliability, privacy, and information security. The first half of this thesis surveys the literature on intrusion detection techniques based on machine learning, deep learning, and blockchain technology from 2009 to 2018. The survey identifies applications, drawbacks, and challenges of these three intrusion detection methodologies that identify threats in computer network environments. The second half of this thesis proposes a new machine learning Model for intrusion detection that employs random forest, naive Bayes, and decision tree algorithms. We evaluate its performance on a standard dataset of simulated network attacks used in the literature, NSL-KDD. We discuss preprocessing of the dataset and feature selection for training our hybrid model and report its performance using standard metrics such as accuracy, precision, recall, and f-measure. In the final part of the thesis, we evaluate our intrusion model against the performance of existing machine learning models for intrusion detection reported in the literature. Our model predicts the Denial of Service (DOS) attack using a random forest classifier with 99.81% accuracy, Probe attack with 97.89% accuracy, and R2L attack with 97.92% accuracy achieving equivalent or superior performance in comparison with the existing models.;

504 a| Includes bibliographical references.;

650 0 a| Intrusion detection systems (Computer security)=| ^A1037231;

650 0 a| Machine learning.=| ^A145211;

650 0 a| Blockchains (Databases)=| ^A1361117;

653 a| security;

653 a| network;

653 a| deep learning;

653 a| NSL KDD;

653 a| algorithms;

653 a| classifiers;

700 1 a| Tabrizi, M. H. N.,e| degree supervisor.?| UNAUTHORIZED;

710 2 a| East Carolina University.b| Department of Computer Science.?| UNAUTHORIZED;

856 40 z| Access via ScholarShipu| http://hdl.handle.net/10342/7650;

949 o| wjh;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 5266615;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5266615-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 3/13/2020x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5266615-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 3/13/2020x| ETDz| HERESOURCE;

Intrusion detection using machine learning algorithms / by Deepthi Hassan Lakshminarayana.

Author/creator	Lakshminarayana, Deepthi Hassan author.
Other author	Tabrizi, M. H. N., degree supervisor.
Other author	East Carolina University. Department of Computer Science.
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2019.
Description	61 pages : illustrations (chiefly color)
Supplemental Content	Access via ScholarShip
Subjects	Intrusion detection systems (Computer security) Machine learning. Blockchains (Databases)

Summary	With the growing rate of cyber-attacks, there is a significant need for intrusion detection systems (IDS) in networked environments. As intrusion tactics become more sophisticated and more challenging to detect, this necessitates improved intrusion detection technology to retain user trust and preserve network security. Over the last decade, several detection methodologies have been designed to provide users with reliability, privacy, and information security. The first half of this thesis surveys the literature on intrusion detection techniques based on machine learning, deep learning, and blockchain technology from 2009 to 2018. The survey identifies applications, drawbacks, and challenges of these three intrusion detection methodologies that identify threats in computer network environments. The second half of this thesis proposes a new machine learning Model for intrusion detection that employs random forest, naive Bayes, and decision tree algorithms. We evaluate its performance on a standard dataset of simulated network attacks used in the literature, NSL-KDD. We discuss preprocessing of the dataset and feature selection for training our hybrid model and report its performance using standard metrics such as accuracy, precision, recall, and f-measure. In the final part of the thesis, we evaluate our intrusion model against the performance of existing machine learning models for intrusion detection reported in the literature. Our model predicts the Denial of Service (DOS) attack using a random forest classifier with 99.81% accuracy, Probe attack with 97.89% accuracy, and R2L attack with 97.92% accuracy achieving equivalent or superior performance in comparison with the existing models.
General note	Presented to the faculty of the Department of Computer Science
General note	Advisor: Nasseh Tabrizi
General note	Title from PDF t.p. (viewed May,11 2020).
Dissertation note	M.S. East Carolina University 2019.
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

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Intrusion detection using machine learning algorithms / by Deepthi Hassan Lakshminarayana.

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