LEADER 03493nam 22005175 450 001 9910299269203321 005 20200630070751.0 010 $a3-319-73788-0 024 7 $a10.1007/978-3-319-73788-1 035 $a(CKB)3840000000347957 035 $a(MiAaPQ)EBC5301896 035 $a(DE-He213)978-3-319-73788-1 035 $a(PPN)224638696 035 $a(EXLCZ)993840000000347957 100 $a20180216d2018 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aArtificial Intelligence Tools for Cyber Attribution /$fby Eric Nunes, Paulo Shakarian, Gerardo I. Simari, Andrew Ruef 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (97 pages) $cillustrations 225 1 $aSpringerBriefs in Computer Science,$x2191-5768 311 $a3-319-73787-2 320 $aIncludes bibliographical references at the end of each chapters. 330 $aThis SpringerBrief discusses how to develop intelligent systems for cyber attribution regarding cyber-attacks. Specifically, the authors review the multiple facets of the cyber attribution problem that make it difficult for ?out-of-the-box? artificial intelligence and machine learning techniques to handle.  Attributing a cyber-operation through the use of multiple pieces of technical evidence (i.e., malware reverse-engineering and source tracking) and conventional intelligence sources (i.e., human or signals intelligence) is a difficult problem not only due to the effort required to obtain evidence, but the ease with which an adversary can plant false evidence. This SpringerBrief not only lays out the theoretical foundations for how to handle the unique aspects of cyber attribution ? and how to update models used for this purpose ? but it also describes a series of empirical results, as well as compares results of specially-designed frameworks for cyber attribution to standard machine learning approaches.  Cyber attribution is not only a challenging problem, but there are also problems in performing such research, particularly in obtaining relevant data. This SpringerBrief describes how to use capture-the-flag for such research, and describes issues from organizing such data to running your own capture-the-flag specifically designed for cyber attribution. Datasets and software are also available on the companion website. 410 0$aSpringerBriefs in Computer Science,$x2191-5768 606 $aArtificial intelligence 606 $aData protection 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aSecurity$3https://scigraph.springernature.com/ontologies/product-market-codes/I28000 615 0$aArtificial intelligence. 615 0$aData protection. 615 14$aArtificial Intelligence. 615 24$aSecurity. 676 $a006.3 700 $aNunes$b Eric$4aut$4http://id.loc.gov/vocabulary/relators/aut$0921501 702 $aShakarian$b Paulo$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aSimari$b Gerardo I$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aRuef$b Andrew$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910299269203321 996 $aArtificial Intelligence Tools for Cyber Attribution$92067147 997 $aUNINA