<Record><identifier xmlns="http://purl.org/dc/elements/1.1/">URN:NBN:SI:doc-XTZ7XZPK</identifier><date>2021</date><creator>Dong, Xiaoyang</creator><creator>Jian, Liang</creator><creator>Jian, Yan</creator><relation>documents/doc/X/URN_NBN_SI_doc-XTZ7XZPK_001.pdf</relation><relation>documents/doc/X/URN_NBN_SI_doc-XTZ7XZPK_001.txt</relation><format format_type="issue">3</format><format format_type="volume">45</format><format format_type="type">article</format><format format_type="extent">str. 441-445</format><identifier identifier_type="ISSN">0350-5596</identifier><identifier identifier_type="ISSN">1854-3871</identifier><identifier identifier_type="COBISSID_HOST">98377731</identifier><identifier identifier_type="URN">URN:NBN:SI:doc-XTZ7XZPK</identifier><language>eng</language><publisher>Slovensko društvo Informatika</publisher><source>Informatica (Ljubljana)</source><rights>BY</rights><subject language_type_id="slv">globoko učenje</subject><subject language_type_id="slv">računalniška varnost</subject><subject language_type_id="slv">strojno učenje</subject><subject language_type_id="slv">umetna inteligenca</subject><title>Detection and recognition of abnormal data caused by network intrusion using deep learning</title></Record>