Deep Learning Applications for Cyber Security

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Alazab, Mamoun (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Tang, MingJian (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Έκδοση: 1st ed. 2019.
• Σειρά: Advanced Sciences and Technologies for Security Applications,
• Θέματα: Big data.; Computer crimes.; Neural networks (Computer science) .; Computer security.; System safety.; Big Data.; Cybercrime.; Mathematical Models of Cognitive Processes and Neural Networks.; Systems and Data Security.; Security Science and Technology.

Cybercrime remains a growing challenge in terms of security and privacy practices. Working together, deep learning and cyber security experts have recently made significant advances in the fields of intrusion detection, malicious code analysis and forensic identification. This book addresses questio...

Πίνακας περιεχομένων:

• Adversarial Attack, Defense, and Applications with Deep Learning Frameworks
• Intelligent Situational-Awareness Architecture for Hybrid Emergency Power Systems in More Electric Aircraft
• Deep Learning in Person Re-identication for Cyber-Physical Surveillance Systems
• Deep Learning-based Detection of Electricity Theft Cyber-attacks in Smart Grid AMI Networks
• Using Convolutional Neural Networks for Classifying Malicious Network Traffic
• DBD: Deep Learning DGA-based Botnet Detection
• Enhanced Domain Generating Algorithm Detection Based on Deep Neural Networks
• Intrusion Detection in SDN-based Networks: Deep Recurrent Neural Network Approach
• SeqDroid: Obfuscated Android Malware Detection using Stacked Convolutional and Recurrent Neural Networks
• Forensic Detection of Child Exploitation Material using Deep Learning
• Toward Detection of Child Exploitation Material: A Forensic Approach.