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Book Synopsis

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.

Malware

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    A Hardback by Dimitris Gritzalis

    3 in stock


      View other formats and editions of Malware by Dimitris Gritzalis

      Publisher: Springer
      Publication Date: 12/16/2024
      ISBN13: 9783031662447, 978-3031662447
      ISBN10: 303166244X
      Also in:
      Privacy and data protection

      Description

      Book Synopsis

      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.

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