03978nam 22006015 450 99641819300331620230418065217.03-030-61675-410.1007/978-3-030-61675-5(CKB)4100000011631462(DE-He213)978-3-030-61675-5(MiAaPQ)EBC6420881(PPN)252516427(EXLCZ)99410000001163146220201204d2020 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierMachine Learning for Authorship Attribution and Cyber Forensics[electronic resource] /by Farkhund Iqbal, Mourad Debbabi, Benjamin C. M. Fung1st ed. 2020.Cham :Springer International Publishing :Imprint: Springer,2020.1 online resource (IX, 158 p. 38 illus., 28 illus. in color.) International Series on Computer, Entertainment and Media Technology,2364-94883-030-61674-6 Includes bibliographical references.1. 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.The 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. .International Series on Computer, Entertainment and Media Technology,2364-9488Data miningMachine learningComputer crimesData Mining and Knowledge DiscoveryMachine LearningCybercrimeData mining.Machine learning.Computer crimes.Data Mining and Knowledge Discovery.Machine Learning.Cybercrime.363.25028563Iqbal Farkhund1065698Debbabi MouradFung Benjamin C. M.MiAaPQMiAaPQUtOrBLWBOOK996418193003316Machine learning for authorship attribution and cyber forensics2547561UNISA01090cam a22002777a 4500991003778489707536200317s2005 gw a b 001 0 eng d9783540230755b14386021-39ule_instBibl. Dip.le Aggr. Matematica e Fisica - Sez. Fisicaeng570.28522LC QH507Roederer, Juan G.524182Information and its role in nature /J.G. RoedererBerlin :Springer,2005xii, 235 p. :ill. ;25 cmFrontiers collection,1612-3018Includes bibliographical references (p. [225]-229) and indexInformation theory in biologyInformation retrieval.b1438602111-01-2117-03-20991003778489707536LE006 Fondo Polezzo 009Ex libris Stefano Polezzo12006000182409le006gE42.39-l- 00000.i1594424411-01-21Information and its role in nature1752227UNISALENTOle00617-03-20ma enggw 00