Software Identification by Standard Machine Learning Tools
This article considers tools for controlling software installed on personal computers of automated system users . The flaws of these software solutions are grounded, and an approach to identifying executable files with the help of a machine learning algorithm is developed and presented. This algorit...
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| Veröffentlicht in: | Automatic control and computer sciences 2021-12, Vol.55 (8), p.1175-1179 |
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| container_end_page | 1179 |
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| container_issue | 8 |
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| container_title | Automatic control and computer sciences |
| container_volume | 55 |
| creator | Sukhoparov, M. E. Salakhutdinova, K. I. Lebedev, I. S. |
| description | This article considers tools for controlling software installed on personal computers of automated system users
.
The flaws of these software solutions are grounded, and an approach to identifying executable files with the help of a machine learning algorithm is developed and presented. This algorithm consists in the gradient decision tree boosting on the basis of such libraries as XGBoost, LightGBM, CatBoost. The identification of programs with the help of XGBoost and LightGBM is executed. The experimental results are compared with the results of earlier studies conducted by other authors. The findings show that the developed method allows for identifying violations in the adopted security policy during information processing in automated systems. |
| doi_str_mv | 10.3103/S0146411621080459 |
| format | Article |
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The flaws of these software solutions are grounded, and an approach to identifying executable files with the help of a machine learning algorithm is developed and presented. This algorithm consists in the gradient decision tree boosting on the basis of such libraries as XGBoost, LightGBM, CatBoost. The identification of programs with the help of XGBoost and LightGBM is executed. The experimental results are compared with the results of earlier studies conducted by other authors. The findings show that the developed method allows for identifying violations in the adopted security policy during information processing in automated systems.</description><identifier>ISSN: 0146-4116</identifier><identifier>EISSN: 1558-108X</identifier><identifier>DOI: 10.3103/S0146411621080459</identifier><language>eng</language><publisher>Moscow: Pleiades Publishing</publisher><subject>Algorithms ; Automation ; Computer Science ; Control Structures and Microprogramming ; Data processing ; Decision trees ; Machine learning ; Personal computers ; Software</subject><ispartof>Automatic control and computer sciences, 2021-12, Vol.55 (8), p.1175-1179</ispartof><rights>Allerton Press, Inc. 2021. ISSN 0146-4116, Automatic Control and Computer Sciences, 2021, Vol. 55, No. 8, pp. 1175–1179. © Allerton Press, Inc., 2021. Russian Text © The Author(s), 2020, published in Problemy Informatsionnoi Bezopasnosti, Komp’yuternye Sistemy.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c268t-677f3f31147ed10487b71d9a5bfa87b3cf85fc7d66ad316251019e1d5194458d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.3103/S0146411621080459$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.3103/S0146411621080459$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Sukhoparov, M. E.</creatorcontrib><creatorcontrib>Salakhutdinova, K. I.</creatorcontrib><creatorcontrib>Lebedev, I. S.</creatorcontrib><title>Software Identification by Standard Machine Learning Tools</title><title>Automatic control and computer sciences</title><addtitle>Aut. Control Comp. Sci</addtitle><description>This article considers tools for controlling software installed on personal computers of automated system users
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The flaws of these software solutions are grounded, and an approach to identifying executable files with the help of a machine learning algorithm is developed and presented. This algorithm consists in the gradient decision tree boosting on the basis of such libraries as XGBoost, LightGBM, CatBoost. The identification of programs with the help of XGBoost and LightGBM is executed. The experimental results are compared with the results of earlier studies conducted by other authors. The findings show that the developed method allows for identifying violations in the adopted security policy during information processing in automated systems.</description><subject>Algorithms</subject><subject>Automation</subject><subject>Computer Science</subject><subject>Control Structures and Microprogramming</subject><subject>Data processing</subject><subject>Decision trees</subject><subject>Machine learning</subject><subject>Personal computers</subject><subject>Software</subject><issn>0146-4116</issn><issn>1558-108X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp1UEtLxDAQDqJgXf0B3gqeq5nm0dSbLD4WVjx0BW8lbZK1y5qsSRbZf29KBQ_iaWb4XsOH0CXgawKY3DQYKKcAvAQsMGX1EcqAMVGk8-0YZSNcjPgpOgthg3HCBM_QbeNM_JJe5wulbRzM0Ms4OJt3h7yJ0irpVf4s-_fB6nyppbeDXecr57bhHJ0YuQ364mfO0OvD_Wr-VCxfHhfzu2XRl1zEgleVIYYA0EorwFRUXQWqlqwzMu2kN4KZvlKcS0XS_www1BoUg5pSJhSZoavJd-fd516H2G7c3tsU2ZacUMHLmvDEgonVexeC16bd-eFD-kMLuB0rav9UlDTlpAmJa9fa_zr_L_oGL4dmTw</recordid><startdate>20211201</startdate><enddate>20211201</enddate><creator>Sukhoparov, M. E.</creator><creator>Salakhutdinova, K. I.</creator><creator>Lebedev, I. S.</creator><general>Pleiades Publishing</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20211201</creationdate><title>Software Identification by Standard Machine Learning Tools</title><author>Sukhoparov, M. E. ; Salakhutdinova, K. I. ; Lebedev, I. S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c268t-677f3f31147ed10487b71d9a5bfa87b3cf85fc7d66ad316251019e1d5194458d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Automation</topic><topic>Computer Science</topic><topic>Control Structures and Microprogramming</topic><topic>Data processing</topic><topic>Decision trees</topic><topic>Machine learning</topic><topic>Personal computers</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sukhoparov, M. E.</creatorcontrib><creatorcontrib>Salakhutdinova, K. I.</creatorcontrib><creatorcontrib>Lebedev, I. S.</creatorcontrib><collection>CrossRef</collection><jtitle>Automatic control and computer sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sukhoparov, M. E.</au><au>Salakhutdinova, K. I.</au><au>Lebedev, I. S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Software Identification by Standard Machine Learning Tools</atitle><jtitle>Automatic control and computer sciences</jtitle><stitle>Aut. Control Comp. Sci</stitle><date>2021-12-01</date><risdate>2021</risdate><volume>55</volume><issue>8</issue><spage>1175</spage><epage>1179</epage><pages>1175-1179</pages><issn>0146-4116</issn><eissn>1558-108X</eissn><abstract>This article considers tools for controlling software installed on personal computers of automated system users
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The flaws of these software solutions are grounded, and an approach to identifying executable files with the help of a machine learning algorithm is developed and presented. This algorithm consists in the gradient decision tree boosting on the basis of such libraries as XGBoost, LightGBM, CatBoost. The identification of programs with the help of XGBoost and LightGBM is executed. The experimental results are compared with the results of earlier studies conducted by other authors. The findings show that the developed method allows for identifying violations in the adopted security policy during information processing in automated systems.</abstract><cop>Moscow</cop><pub>Pleiades Publishing</pub><doi>10.3103/S0146411621080459</doi><tpages>5</tpages></addata></record> |
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| identifier | ISSN: 0146-4116 |
| ispartof | Automatic control and computer sciences, 2021-12, Vol.55 (8), p.1175-1179 |
| issn | 0146-4116 1558-108X |
| language | eng |
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| source | SpringerLink Journals |
| subjects | Algorithms Automation Computer Science Control Structures and Microprogramming Data processing Decision trees Machine learning Personal computers Software |
| title | Software Identification by Standard Machine Learning Tools |
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