Malware
portes grátis
Malware
Handbook of Prevention and Detection
Choo, Kim-Kwang Raymond; Gritzalis, Dimitris; Patsakis, Constantinos
Springer International Publishing AG
12/2024
410
Dura
9783031662447
Pré-lançamento - envio 15 a 20 dias após a sua edição
Descrição não disponível.
Part I Theoretical foundation and modeling.- Chapter 1 Classifying Malware using Tensor Decomposition.- Chapter 2 Radial Spike and Slab Bayesian Neural Networks for Sparse Data in Ransomware Attacks.- Chapter 3 Mathematical models for malware propagation: state of art and perspectives.- Chapter 4 Botnet Defense System: A System to Fight Botnets with Botnets.- Part II Machine learning for malware classification.- Chapter 5 Machine Learning-Based Malware Detection in a Production Setting.- Chapter 6 Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Research.- Chapter 7 Conventional Machine Learning-based Android Malware Detectors.- Chapter 8 Conventional Machine Learning-based Android Malware Detectors.- Chapter 9 Method to automate the classification of PE32 malware using Word2vec and LSTM.- Part III Social and legal.- Chapter 10 The South African and Senegalese legislative response to malware facilitated cybercrime.- Chapter 11Malware as a Geopolitical Tool.-Part IV Malware analysis in practice and evasions.- Chapter 12 Advancements in Malware Evasion: Analysis Detection and the Future Role of AI.-Chapter 13 Unpacking malware in the real world: a step by step guide.- Chapter 14 Forensic Analysis of CapraRAT Android Malware.- Chapter 15 Hidden Realms: Exploring Steganography Methods in Games for Covert Malware Delivery.- Part V Malware ecosystem.- Chapter 16 The Malware as a Service ecosystem.- Chapter 17Preventing and detecting malware in smart environments. The smart home case.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Malware;malware analysis;malware detection;malware classification;packers;advance persistent threats;ransomware;digital forensics;machine learning;botnet;malware as a service
Part I Theoretical foundation and modeling.- Chapter 1 Classifying Malware using Tensor Decomposition.- Chapter 2 Radial Spike and Slab Bayesian Neural Networks for Sparse Data in Ransomware Attacks.- Chapter 3 Mathematical models for malware propagation: state of art and perspectives.- Chapter 4 Botnet Defense System: A System to Fight Botnets with Botnets.- Part II Machine learning for malware classification.- Chapter 5 Machine Learning-Based Malware Detection in a Production Setting.- Chapter 6 Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Research.- Chapter 7 Conventional Machine Learning-based Android Malware Detectors.- Chapter 8 Conventional Machine Learning-based Android Malware Detectors.- Chapter 9 Method to automate the classification of PE32 malware using Word2vec and LSTM.- Part III Social and legal.- Chapter 10 The South African and Senegalese legislative response to malware facilitated cybercrime.- Chapter 11Malware as a Geopolitical Tool.-Part IV Malware analysis in practice and evasions.- Chapter 12 Advancements in Malware Evasion: Analysis Detection and the Future Role of AI.-Chapter 13 Unpacking malware in the real world: a step by step guide.- Chapter 14 Forensic Analysis of CapraRAT Android Malware.- Chapter 15 Hidden Realms: Exploring Steganography Methods in Games for Covert Malware Delivery.- Part V Malware ecosystem.- Chapter 16 The Malware as a Service ecosystem.- Chapter 17Preventing and detecting malware in smart environments. The smart home case.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
