This handbook discusses challenges and limitations in existing solutions, and presents state-of-the-art advances from both academia and industry, in big data analytics and digital forensics. The second chapter comprehensively reviews IoT security, privacy, and forensics literature, focusing on IoT and unmanned aerial vehicles (UAVs). The authors propose a deep learning-based approach to process cloud''s log data and mitigate enumeration attacks in the third chapter. The fourth chapter proposes a robust fuzzy learning model to protect IT-based infrastructure against advanced persistent threat (APT) campaigns. Advanced and fair clustering approach for industrial data, which is capable of training with huge volume of data in a close to linear time is introduced in the fifth chapter, as well as offering an adaptive deep learning model to detect cyberattacks targeting cyber physical systems (CPS) covered in the sixth chapter.
The authors evaluate the performance of unsupervised machine learning for detecting cyberattacks against industrial control systems (ICS) in chapter 7, and the next chapter presents a robust fuzzy Bayesian approach for ICS''s cyber threat hunting. This handbook also evaluates the performance of supervised machine learning methods in identifying cyberattacks against CPS. The performance of a scalable clustering algorithm for CPS''s cyber threat hunting and the usefulness of machine learning algorithms for MacOS malware detection are respectively evaluated.
This handbook continues with evaluating the performance of various machine learning techniques to detect the Internet of Things malware. The authors demonstrate how MacOSX cyberattacks can be detected using state-of-the-art machine learning models. In order to identify credit card frauds, the fifteenth chapter introduces a hybrid model. In the sixteenth chapter, the editors propose a model that leverages natural language processing techniques for generating a mapping between APT-related reports and cyber kill chain. A deep learning-based approach to detect ransomware is introduced, as well as a proposed clustering approach to detect IoT malware in the last two chapters.
This handbook primarily targets professionals and scientists working in Big Data, Digital Forensics, Machine Learning, Cyber Security Cyber Threat Analytics and Cyber Threat Hunting as a reference book. Advanced level-students and researchers studying and working in Computer systems, Computer networks and Artificial intelligence will also find this reference useful.
This handbook discusses challenges and limitations in existing solutions, and presents state-of-the-art advances from both academia and industry, in big data analytics and digital forensics. The second chapter comprehensively reviews IoT security, privacy, and forensics literature, focusing on IoT and unmanned aerial vehicles (UAVs). The authors propose a deep learning-based approach to process cloud''s log data and mitigate enumeration attacks in the third chapter. The fourth chapter proposes a robust fuzzy learning model to protect IT-based infrastructure against advanced persistent threat (APT) campaigns. Advanced and fair clustering approach for industrial data, which is capable of training with huge volume of data in a close to linear time is introduced in the fifth chapter, as well as offering an adaptive deep learning model to detect cyberattacks targeting cyber physical systems (CPS) covered in the sixth chapter.
The authors evaluate the performance of unsupervised machine learning for detecting cyberattacks against industrial control systems (ICS) in chapter 7, and the next chapter presents a robust fuzzy Bayesian approach for ICS''s cyber threat hunting. This handbook also evaluates the performance of supervised machine learning methods in identifying cyberattacks against CPS. The performance of a scalable clustering algorithm for CPS''s cyber threat hunting and the usefulness of machine learning algorithms for MacOS malware detection are respectively evaluated.
This handbook continues with evaluating the performance of various machine learning techniques to detect the Internet of Things malware. The authors demonstrate how MacOSX cyberattacks can be detected using state-of-the-art machine learning models. In order to identify credit card frauds, the fifteenth chapter introduces a hybrid model. In the sixteenth chapter, the editors propose a model that leverages natural language processing techniques for generating a mapping between APT-related reports and cyber kill chain. A deep learning-based approach to detect ransomware is introduced, as well as a proposed clustering approach to detect IoT malware in the last two chapters.
This handbook primarily targets professionals and scientists working in Big Data, Digital Forensics, Machine Learning, Cyber Security Cyber Threat Analytics and Cyber Threat Hunting as a reference book. Advanced level-students and researchers studying and working in Computer systems, Computer networks and Artificial intelligence will also find this reference useful.
Prisen for levering afhænger af typen af dit medlemskab, eller om du ikke har et medlemskab.
Hvis du ikke har et medlemsskab er priserne som følger:
Levering til pakkeshop | 39,95 kr. pr. ordre |
Hjemmelevering | 59,90 kr. pr. ordre |
Med et guldmedlemsskab er leveringspriserne:
Levering til pakkeshop. Ordrer under 250 kr. | 34,95 kr. pr. ordre |
Levering til pakkeshop. Ordrer over 250 kr. | 24,95 kr. pr. ordre |
Hjemmelevering. Ordrer under 250 kr. | 59,90 kr. pr. ordre |
Hjemmelevering. Ordrer over 250 kr. | 49,90 kr. pr. ordre |
Med et plating- eller streaming medlemsskab er leveringspriserne:
Levering til pakkeshop. Ordrer under 250 kr. | 24,95 kr. pr. ordre |
Levering til pakkeshop. Ordrer over 250 kr. | 0 kr. pr. ordre |
Hjemmelevering. Ordrer under 250 kr. | 44,90 kr. pr. ordre |
Hjemmelevering. Ordrer over 250 kr. | 19,95 kr. pr. ordre |
Bemærk venligst, at vi forbeholder os retten til at ændre i et fragtbeløb efter ordreafgivelse, hvis man som kunde har opnået en særlig fragtpris pga. køb for over 250 kr. og efterfølgende retter i sin ordre, så ordrebeløbet kommer under 250 kr. Ovenstående fragtpriser for ordrer under 250 kr. vil i så fald være gældende.
Levering
Varerne sendes indenfor 1-6 hverdage. Den konkrete leveringstid står oplyst ved hver enkelt vare. Levering sker med PostNord eller DAO distribution. Vi leverer kun i Danmark og ikke til Grønland og Færøerne.
Vær opmærksom på, at DAO ofte leverer om natten, og at der ikke skal kvitteres for modtagelse af pakken fra DAO. Hvis ikke DAO kan levere pakken forsvarligt ved dør eller i postkasse,
vil pakken i stedet blive leveret til nærmeste pakkeshop, også selvom du har betalt for hjemmelevering.