LEADER 03980nam 22006015 450 001 9910484465603321 005 20230418065217.0 010 $a3-030-61675-4 024 7 $a10.1007/978-3-030-61675-5 035 $a(CKB)4100000011631462 035 $a(DE-He213)978-3-030-61675-5 035 $a(MiAaPQ)EBC6420881 035 $a(PPN)252516427 035 $a(EXLCZ)994100000011631462 100 $a20201204d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning for Authorship Attribution and Cyber Forensics$b[electronic resource] /$fby Farkhund Iqbal, Mourad Debbabi, Benjamin C. M. Fung 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (IX, 158 p. 38 illus., 28 illus. in color.) 225 1 $aInternational Series on Computer, Entertainment and Media Technology,$x2364-9488 311 $a3-030-61674-6 320 $aIncludes bibliographical references. 327 $a1. Cybersecurity And Cybercrime Investigation -- 2. Machine Learning Framework For Messaging Forensics -- 3. Header-Level Investigation And Analyzing Network Information -- 4. Authorship Analysis Approaches -- 5. Authorship Analysis - Writeprint Mining For Authorship Attribution -- 6. Authorship Attribution With Few Training Samples -- 7. Authorship Characterization -- 8. Authorship Verification -- 9. Authorship Attribution Using Customized Associative Classification -- 10. Criminal Information Mining -- 11. Artificial Intelligence And Digital Forensics. 330 $aThe book first explores the cybersecurity?s landscape and the inherent susceptibility of online communication system such as e-mail, chat conversation and social media in cybercrimes. Common sources and resources of digital crimes, their causes and effects together with the emerging threats for society are illustrated in this book. This book not only explores the growing needs of cybersecurity and digital forensics but also investigates relevant technologies and methods to meet the said needs. Knowledge discovery, machine learning and data analytics are explored for collecting cyber-intelligence and forensics evidence on cybercrimes. Online communication documents, which are the main source of cybercrimes are investigated from two perspectives: the crime and the criminal. AI and machine learning methods are applied to detect illegal and criminal activities such as bot distribution, drug trafficking and child pornography. Authorship analysis is applied to identify the potential suspects and their social linguistics characteristics. Deep learning together with frequent pattern mining and link mining techniques are applied to trace the potential collaborators of the identified criminals. Finally, the aim of the book is not only to investigate the crimes and identify the potential suspects but, as well, to collect solid and precise forensics evidence to prosecute the suspects in the court of law. . 410 0$aInternational Series on Computer, Entertainment and Media Technology,$x2364-9488 606 $aData mining 606 $aMachine learning 606 $aComputer crimes 606 $aData Mining and Knowledge Discovery 606 $aMachine Learning 606 $aCybercrime 615 0$aData mining. 615 0$aMachine learning. 615 0$aComputer crimes. 615 14$aData Mining and Knowledge Discovery. 615 24$aMachine Learning. 615 24$aCybercrime. 676 $a363.25028563 700 $aIqbal$b Farkhund$01065698 702 $aDebbabi$b Mourad 702 $aFung$b Benjamin C. M. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bUtOrBLW 906 $aBOOK 912 $a9910484465603321 996 $aMachine learning for authorship attribution and cyber forensics$92547561 997 $aUNINA