Malware Analysis Using Artificial Intelligence and Deep Learning (PDF)
90 DeutschlandCard Punkte sammeln
- Lastschrift, Kreditkarte, Paypal, Rechnung
- Kostenloser tolino webreader
¿This book is focused on the use of deep learning (DL) and artificial intelligence (AI) as tools to advance the fields of malware detection and analysis. The individual chapters of the book deal with a wide variety of state-of-the-art AI and DL techniques, which are applied to a number of challenging malware-related problems. DL and AI based approaches to malware detection and analysis are largely data driven and hence minimal expert domain knowledge of malware is needed.
Mamoun Alazab received his PhD degree in Computer Science from the Federation University of Australia, School of Science, Information Technology and Engineering. He is currently an Associate Professor in the College of Engineering, IT and Environment at Charles Darwin University, Australia. He is a cyber-security researcher and practitioner with industry and academic experience. Dr. Alazab's research is multidisciplinary, with a focus on cyber security and digital forensics of computer systems, including current and emerging issues in the cyber environment, such as cyber-physical systems and the Internet of Things. His research takes into consideration the unique challenges present in these environments, with an emphasis on cybercrime detection and prevention.
Andrii Shalaginov is a Researcher in Information Security and Digital Forensics at the Department of Information Security and Communication Technology, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU). Dr. Shalaginov was awarded the PhD degree in Information Security from NTNU in February 2018. During the last decade, Dr. Shalaginov's focus has been on the fields of cybercrime investigation and intelligent malware detection. His primary expertise is in static and dynamic malware analysis, development of machine learning-aided intelligent computer virus detection models, and similarity-based categorization of cyberattacks in the Internet of Things. Further, Dr. Shalaginov has worked as a security researcher for UNICRI/EUIPO on malware analysis for copyright-infringing websites. He was nominated as a representative from Norway at COST Action CA17124 "DigForAsp - Digital forensics: evidence analysis via intelligent systems and practices". In 2018, Dr. Shalaginov, together with his NTNU team, received an award for first place in the "Future of Smart Policing" hackathon competition sponsored by INTERPOL (Singapore). Dr. Shalaginov also holds a second Master's Degree in Information Security (Digital Forensics) from Gjøvik University College (GUC), and he received BSc and MSc degrees in System Designing from the National Technical University of Ukraine "Kyiv Polytechnic Institute", Department of Computer-Aided Design. Finally, Dr. Shalaginov is LE-1/LPIC-1 certified and has extensive industry experience, including work at Samsung R&D Center.
- 2020, 1st ed. 2021, 651 Seiten, Englisch
- Herausgegeben: Mark Stamp, Mamoun Alazab, Andrii Shalaginov
- Verlag: Springer International Publishing
- ISBN-10: 3030625826
- ISBN-13: 9783030625825
- Erscheinungsdatum: 20.12.2020
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
- Dateiformat: PDF
- Größe: 22 MB
- Ohne Kopierschutz
- Vorlesefunktion
Zustand | Preis | Porto | Zahlung | Verkäufer | Rating |
---|
Schreiben Sie einen Kommentar zu "Malware Analysis Using Artificial Intelligence and Deep Learning".
Kommentar verfassen