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010   $a3-031-34969-5
010   $a9783031349690$b(ebook)
024 7 $a10.1007/978-3-031-34969-0
035   $a(MiAaPQ)EBC30876565
035   $a(Au-PeEL)EBL30876565
035   $a(DE-He213)978-3-031-34969-0
035   $a(CKB)28804792700041
035   $a(EXLCZ)9928804792700041
100   $a20231108d2024      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aCyber Malware $eOffensive and Defensive Systems /$fedited by Iman Almomani, Leandros A. Maglaras, Mohamed Amine Ferrag, Nick Ayres
205   $a1st ed. 2024.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2024.
215   $a1 online resource (xxxvi, 280 pages) $cillustrations
225 1 $aSecurity Informatics and Law Enforcement,$x2523-8515
311 08$aPrint version: Almomani, Iman Cyber Malware Cham : Springer International Publishing AG,c2023 9783031349683 
320   $aIncludes bibliographical references and index.
327   $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.
330   $aThis book provides the foundational aspects of malware attack vectors and appropriate defense mechanisms against malware. The book equips readers with the necessary knowledge and techniques to successfully lower the risk against emergent malware attacks. Topics cover protections against malware using machine learning algorithms, Blockchain and AI technologies, smart AI-based applications, automated detection-based AI tools, forensics tools, and much more. The authors discuss theoretical, technical, and practical issues related to cyber malware attacks and defense, making it ideal reading material for students, researchers, and developers. Presents theoretical, technical, and practical knowledge on defending against malware attacks; Covers malware applications using machine learning algorithms, Blockchain and AI, forensics tools, and much more; Includes perspectives from experts in cybersecurity at different institutions, including academia, research centers, and companies.
410  0$aSecurity Informatics and Law Enforcement,$x2523-8515
606   $aTelecommunication
606   $aComputer crimes
606   $aData protection
606   $aSecurity systems
606   $aCommunications Engineering, Networks
606   $aCybercrime
606   $aData and Information Security
606   $aSecurity Science and Technology
615  0$aTelecommunication.
615  0$aComputer crimes.
615  0$aData protection.
615  0$aSecurity systems.
615 14$aCommunications Engineering, Networks.
615 24$aCybercrime.
615 24$aData and Information Security.
615 24$aSecurity Science and Technology.
676   $a929.605
676   $a005.88
702   $aAlmomani$b Iman
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910760297803321
996   $aCyber Malware$93598668
997   $aUNINA