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001 on1231501393;

003 OCoLC;

005 20210720020610.0;

006 m o d ;

007 cr unu||||||||;

008 210114s2020 ncua obm 000 0 eng d;

035 a| (Sirsi) o1231501393;

035 a| (OCoLC)1231501393;

049 a| EREE;

090 a| TK5105.743;

100 1 a| Sokolov, Mark,e| author.?| UNAUTHORIZED;

245 10 a| Applied machine learning for cybersecurity in spam filtering and malware detection /c| by Mark Sokolov.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2020.;

300 a| 62 pages :b| color illustrations;

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text fileb| PDFc| 2.354 MB2| rda;

538 a| System requirements: Adobe Reader.;

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

502 b| M.S.c| East Carolina Universityd| 2020.;

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

500 a| Advisor: Nic Herndon;

500 a| Title from PDF t.p. (viewed July 12, 2021).;

520 3 a| Machine learning is one of the fastest-growing fields and its application to cybersecurity is increasing. In order to protect people from malicious attacks, several machine learning algorithms have been used to predict the malicious attacks. This research emphasizes two vulnerable areas of cybersecurity that could be easily exploited. First, we show that spam filtering is a well known problem that has been addressed by many authors, yet it still has vulnerabilities. Second, with the increase of malware threats in our world, a lot of companies use AutoAI to help protect their systems. Nonetheless, AutoAI is not perfect, and data scientists can still design better models. In this thesis I show that although there are efficient mechanisms to prevent malicious attacks, there are still vulnerabilities that could be easily exploited. In the visual spoofing experiment, we show that using a classifier trained on data using Latin alphabet, to classify a message with a combination of Latin and Cyrillic letters leads to much lower classification accuracy. In Malware prediction experiment, our model has been able to predict malware attacks on Microsoft computers and got higher accuracy than any well known Auto AI.;

504 a| Includes bibliographical references.;

650 0 a| Spam filtering (Electronic mail)=| ^A628320;

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

650 0 a| Malware (Computer software)=| ^A1065353;

650 0 a| Data protection.=| ^A141234;

653 a| Computer Science;

653 a| LGBM;

653 a| Visual spoofing;

653 a| prediction;

700 1 a| Herndon, Nic,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/10342/8798;

949 o| wjh;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 5547858;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5547858-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 1/14/2021x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5547858-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 1/14/2021x| ETDz| HERESOURCE;

Applied machine learning for cybersecurity in spam filtering and malware detection / by Mark Sokolov.

Author/creator	Sokolov, Mark author.
Other author	Herndon, Nic, degree supervisor.
Other author	East Carolina University. Department of Computer Science.
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2020.
Description	62 pages : color illustrations
Supplemental Content	Access via ScholarShip
Subjects	Spam filtering (Electronic mail) Machine learning. Malware (Computer software) Data protection.

Summary	Machine learning is one of the fastest-growing fields and its application to cybersecurity is increasing. In order to protect people from malicious attacks, several machine learning algorithms have been used to predict the malicious attacks. This research emphasizes two vulnerable areas of cybersecurity that could be easily exploited. First, we show that spam filtering is a well known problem that has been addressed by many authors, yet it still has vulnerabilities. Second, with the increase of malware threats in our world, a lot of companies use AutoAI to help protect their systems. Nonetheless, AutoAI is not perfect, and data scientists can still design better models. In this thesis I show that although there are efficient mechanisms to prevent malicious attacks, there are still vulnerabilities that could be easily exploited. In the visual spoofing experiment, we show that using a classifier trained on data using Latin alphabet, to classify a message with a combination of Latin and Cyrillic letters leads to much lower classification accuracy. In Malware prediction experiment, our model has been able to predict malware attacks on Microsoft computers and got higher accuracy than any well known Auto AI.
General note	Presented to the faculty of the Department of Computer Science
General note	Advisor: Nic Herndon
General note	Title from PDF t.p. (viewed July 12, 2021).
Dissertation note	M.S. East Carolina University 2020.
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

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Applied machine learning for cybersecurity in spam filtering and malware detection / by Mark Sokolov.

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