04034nam a22005535i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001800172072001600190072002300206072001500229245016000244250001800404264007500422300004500497336002600542337002600568338003600594347002400630490005700654505085600711520099201567650002302559650002802582650002102610650002902631650004202660650002302702650004202725650004402767700008402811700009002895700009102985700008103076710003403157773002603191776003603217776003603253776003603289830006403325856004603389912001403435912001403449999001703463978-3-031-34969-0DE-He21320251030110839.0cr nn 008mamaa231108s2024    sz |    s    |||| 0|eng d  a97830313496909978-3-031-34969-07 a10.1007/978-3-031-34969-02doi 4aTK5101-5105.9 7aTJK2bicssc 7aTEC0410002bisacsh 7aTJK2thema10aCyber Malwareh[electronic resource] :bOffensive and Defensive Systems /cedited by Iman Almomani, Leandros A. Maglaras, Mohamed Amine Ferrag, Nick Ayres.  a1st ed. 2024. 1aCham :bSpringer International Publishing :bImprint: Springer,c2024.  aXXXVI, 280 p. 1 illus.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aSecurity Informatics and Law Enforcement,x2523-85150 aPart 1. Android Malware Analysis -- Chapter 1. A Deep Vision-based Multi-Class Classification System of Android Malware Apps -- Chapter 2. Android Malware detection based on network analysis and federated learning -- Chapter 3. ASParseV3: Auto Static Parser & Customizable Visualizer -- Part 2. Network Malware Analysis -- Chapter 4. Fast Flux Service Networks: Architecture, Characteristics and Detection Mechanisms -- Chapter 5. Efficient Graph-based Malware Detection using Minimized Kernel and SVM -- Chapter. 6 Deep Learning for Windows Malware Analysis -- Part 3. IoT Malware Analysis -- Chapter 7. Malware analysis for IoT and Smart AI-based Applications -- Chapter 8. A Multi-Class Classification Approach for IoT Intrusion Detection Based on Feature Selection and Oversampling -- Chapter 9. Malware Mitigation in Cloud Computing Architecture.  aThis book provides the foundational aspects of malware attack vectors and appropriate defense mechanisms against malware. The book equips readers with the necessary knowledge and techniques to successfully lower the risk against emergent malware attacks. Topics cover protections against malware using machine learning algorithms, Blockchain and AI technologies, smart AI-based applications, automated detection-based AI tools, forensics tools, and much more. The authors discuss theoretical, technical, and practical issues related to cyber malware attacks and defense, making it ideal reading material for students, researchers, and developers. Presents theoretical, technical, and practical knowledge on defending against malware attacks; Covers malware applications using machine learning algorithms, Blockchain and AI, forensics tools, and much more; Includes perspectives from experts in cybersecurity at different institutions, including academia, research centers, and companies. 0aTelecommunication. 0aComputer crimes.967515 0aData protection. 0aSecurity systems.96682114aCommunications Engineering, Networks.24aCybercrime.96751624aData and Information Security.96649224aSecurity Science and Technology.9668221 aAlmomani, Iman.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9675171 aMaglaras, Leandros A.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9675181 aFerrag, Mohamed Amine.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9675191 aAyres, Nick.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9675202 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303134968308iPrinted edition:z978303134970608iPrinted edition:z9783031349713 0aSecurity Informatics and Law Enforcement,x2523-851596752140uhttps://doi.org/10.1007/978-3-031-34969-0  aZDB-2-ENG  aZDB-2-SXE  c56626d56626