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008			231108s2024 sz | fo |||| 0|eng d
020			_a9783031349690
024	7		_a10.1007/978-3-031-34969-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.76 .C68 _b2024 EB
245	0	0	_aCyber Malware : _bOffensive and Defensive Systems _cedited by Iman Almomani, Leandros A. Maglaras, Mohamed Amine Ferrag, Nick Ayres
250			_a1st ed. 2024.
264		1	_aCham _bSpringer International Publishing _c2024
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aSecurity Informatics and Law Enforcement _x2523-8515
505	0		_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.
520			_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.
988			_aSpringer_Engineering_2024
650		7	_2embne _9146255 _aVirus informáticos
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-34969-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2024 _dz _ean _zSI