High Accuracy Detection of Mobile Malware Using Machine Learning / / Suleiman Yerima, editor.
As increasingly sophisticated and evasive malware attacks continue to emerge, more effective detection solutions to tackle the problem are being sought through the application of advanced machine learning techniques. This reprint presents several advances in the field including: a new method of gene...
Saved in:
TeilnehmendeR: | |
---|---|
Place / Publishing House: | [Place of publication not identified] : : MDPI - Multidisciplinary Digital Publishing Institute,, 2023. |
Year of Publication: | 2023 |
Language: | English |
Physical Description: | 1 online resource (226 pages) |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
993600123604498 |
---|---|
ctrlnum |
(CKB)4960000000467826 (NjHacI)994960000000467826 (EXLCZ)994960000000467826 |
collection |
bib_alma |
record_format |
marc |
spelling |
High Accuracy Detection of Mobile Malware Using Machine Learning / Suleiman Yerima, editor. [Place of publication not identified] : MDPI - Multidisciplinary Digital Publishing Institute, 2023. 1 online resource (226 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Description based on publisher supplied metadata and other sources. As increasingly sophisticated and evasive malware attacks continue to emerge, more effective detection solutions to tackle the problem are being sought through the application of advanced machine learning techniques. This reprint presents several advances in the field including: a new method of generating adversarial samples through byte sequence feature extraction using deep learning; a state-of-the-art comparative evaluation of deep learning approaches for mobile botnet detection; a novel visualization-based approach that utilizes images for Android botnet detection; a study on the detection of drive-by exploits in images using deep learning; etc. Furthermore, this reprint presents state-of-the-art reviews about machine learning-based detection techniques that will increase researchers' knowledge in the field and enable them to identify future research and development directions. Malware (Computer software) 3-0365-7175-2 Yerima, Suleiman, editor. |
language |
English |
format |
eBook |
author2 |
Yerima, Suleiman, |
author_facet |
Yerima, Suleiman, |
author2_variant |
s y sy |
author2_role |
TeilnehmendeR |
title |
High Accuracy Detection of Mobile Malware Using Machine Learning / |
spellingShingle |
High Accuracy Detection of Mobile Malware Using Machine Learning / |
title_full |
High Accuracy Detection of Mobile Malware Using Machine Learning / Suleiman Yerima, editor. |
title_fullStr |
High Accuracy Detection of Mobile Malware Using Machine Learning / Suleiman Yerima, editor. |
title_full_unstemmed |
High Accuracy Detection of Mobile Malware Using Machine Learning / Suleiman Yerima, editor. |
title_auth |
High Accuracy Detection of Mobile Malware Using Machine Learning / |
title_new |
High Accuracy Detection of Mobile Malware Using Machine Learning / |
title_sort |
high accuracy detection of mobile malware using machine learning / |
publisher |
MDPI - Multidisciplinary Digital Publishing Institute, |
publishDate |
2023 |
physical |
1 online resource (226 pages) |
isbn |
3-0365-7174-4 3-0365-7175-2 |
callnumber-first |
Q - Science |
callnumber-subject |
QA - Mathematics |
callnumber-label |
QA76 |
callnumber-sort |
QA 276.76 C68 H544 42023 |
illustrated |
Not Illustrated |
dewey-hundreds |
000 - Computer science, information & general works |
dewey-tens |
000 - Computer science, knowledge & systems |
dewey-ones |
005 - Computer programming, programs & data |
dewey-full |
005.8 |
dewey-sort |
15.8 |
dewey-raw |
005.8 |
dewey-search |
005.8 |
work_keys_str_mv |
AT yerimasuleiman highaccuracydetectionofmobilemalwareusingmachinelearning |
status_str |
n |
ids_txt_mv |
(CKB)4960000000467826 (NjHacI)994960000000467826 (EXLCZ)994960000000467826 |
carrierType_str_mv |
cr |
is_hierarchy_title |
High Accuracy Detection of Mobile Malware Using Machine Learning / |
author2_original_writing_str_mv |
noLinkedField |
_version_ |
1796653160873328642 |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01901nam a2200289 i 4500</leader><controlfield tag="001">993600123604498</controlfield><controlfield tag="005">20230629041901.0</controlfield><controlfield tag="006">m o d </controlfield><controlfield tag="007">cr |||||||||||</controlfield><controlfield tag="008">230629s2023 xx o 000 0 eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">3-0365-7174-4</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(CKB)4960000000467826</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(NjHacI)994960000000467826</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(EXLCZ)994960000000467826</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">NjHacI</subfield><subfield code="b">eng</subfield><subfield code="e">rda</subfield><subfield code="c">NjHacl</subfield></datafield><datafield tag="050" ind1=" " ind2="4"><subfield code="a">QA76.76.C68</subfield><subfield code="b">.H544 2023</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">005.8</subfield><subfield code="2">23</subfield></datafield><datafield tag="245" ind1="0" ind2="0"><subfield code="a">High Accuracy Detection of Mobile Malware Using Machine Learning /</subfield><subfield code="c">Suleiman Yerima, editor.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">[Place of publication not identified] :</subfield><subfield code="b">MDPI - Multidisciplinary Digital Publishing Institute,</subfield><subfield code="c">2023.</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (226 pages)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="588" ind1=" " ind2=" "><subfield code="a">Description based on publisher supplied metadata and other sources.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">As increasingly sophisticated and evasive malware attacks continue to emerge, more effective detection solutions to tackle the problem are being sought through the application of advanced machine learning techniques. This reprint presents several advances in the field including: a new method of generating adversarial samples through byte sequence feature extraction using deep learning; a state-of-the-art comparative evaluation of deep learning approaches for mobile botnet detection; a novel visualization-based approach that utilizes images for Android botnet detection; a study on the detection of drive-by exploits in images using deep learning; etc. Furthermore, this reprint presents state-of-the-art reviews about machine learning-based detection techniques that will increase researchers' knowledge in the field and enable them to identify future research and development directions.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Malware (Computer software)</subfield></datafield><datafield tag="776" ind1=" " ind2=" "><subfield code="z">3-0365-7175-2</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yerima, Suleiman,</subfield><subfield code="e">editor.</subfield></datafield><datafield tag="906" ind1=" " ind2=" "><subfield code="a">BOOK</subfield></datafield><datafield tag="ADM" ind1=" " ind2=" "><subfield code="b">2023-07-08 12:21:36 Europe/Vienna</subfield><subfield code="f">system</subfield><subfield code="c">marc21</subfield><subfield code="a">2023-05-13 19:27:32 Europe/Vienna</subfield><subfield code="g">false</subfield></datafield><datafield tag="AVE" ind1=" " ind2=" "><subfield code="i">DOAB Directory of Open Access Books</subfield><subfield code="P">DOAB Directory of Open Access Books</subfield><subfield code="x">https://eu02.alma.exlibrisgroup.com/view/uresolver/43ACC_OEAW/openurl?u.ignore_date_coverage=true&portfolio_pid=5345687790004498&Force_direct=true</subfield><subfield code="Z">5345687790004498</subfield><subfield code="b">Available</subfield><subfield code="8">5345687790004498</subfield></datafield></record></collection> |