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The Internet is making our daily lives as digital as possible, and
this new era is called the Internet of Everything (IoE). The key
force behind the rapid growth of the Internet is the technological
advancement of enterprises. The digital world we live in is
facilitated by these enterprises' advances and business
intelligence. These enterprises need to deal with gazillions of
bytes of data, and in today's age of General Data Protection
Regulation, enterprises are required to ensure privacy and security
of large-scale data collections. However, the increased
connectivity and devices used to facilitate IoE are continually
creating more room for cybercriminals to find vulnerabilities in
enterprise systems and flaws in their corporate governance.
Ensuring cybersecurity and corporate governance for enterprises
should not be an afterthought or present a huge challenge. In
recent times, the complex diversity of cyber-attacks has been
skyrocketing, and zero-day attacks, such as ransomware, botnet, and
telecommunication attacks, are happening more frequently than
before. New hacking strategies would easily bypass existing
enterprise security and governance platforms using advanced,
persistent threats. For example, in 2020, the Toll Group firm was
exploited by a new crypto-attack family for violating its data
privacy, where an advanced ransomware technique was launched to
exploit the corporation and request a huge figure of monetary
ransom. Even after applying rational governance hygiene,
cybersecurity configuration and software updates are often
overlooked when they are most needed to fight cyber-crime and
ensure data privacy. Therefore, the threat landscape in the context
of enterprises has become wider and far more challenging. There is
a clear need for collaborative work throughout the entire value
chain of this network. In this context, this book addresses the
cybersecurity and cooperate governance challenges associated with
enterprises, which will provide a bigger picture of the concepts,
intelligent techniques, practices, and open research directions in
this area. This book serves as a single source of reference for
acquiring the knowledge on the technology, process, and people
involved in next-generation privacy and security.
Digital forensics plays a crucial role in identifying, analysing,
and presenting cyber threats as evidence in a court of law.
Artificial intelligence, particularly machine learning and deep
learning, enables automation of the digital investigation process.
This book provides an in-depth look at the fundamental and advanced
methods in digital forensics. It also discusses how machine
learning and deep learning algorithms can be used to detect and
investigate cybercrimes. This book demonstrates digital forensics
and cyber-investigating techniques with real-world applications. It
examines hard disk analytics and style architectures, including
Master Boot Record and GUID Partition Table as part of the
investigative process. It also covers cyberattack analysis in
Windows, Linux, and network systems using virtual machines in
real-world scenarios. Digital Forensics in the Era of Artificial
Intelligence will be helpful for those interested in digital
forensics and using machine learning techniques in the
investigation of cyberattacks and the detection of evidence in
cybercrimes.
More frequent and complex cyber threats require robust, automated
and rapid responses from cyber security specialists. This book
offers a complete study in the area of graph learning in cyber,
emphasising graph neural networks (GNNs) and their cyber security
applications. Three parts examine the basics; methods and
practices; and advanced topics. The first part presents a grounding
in graph data structures and graph embedding and gives a taxonomic
view of GNNs and cyber security applications. Part two explains
three different categories of graph learning including
deterministic, generative and reinforcement learning and how they
can be used for developing cyber defence models. The discussion of
each category covers the applicability of simple and complex
graphs, scalability, representative algorithms and technical
details. Undergraduate students, graduate students, researchers,
cyber analysts, and AI engineers looking to understand practical
deep learning methods will find this book an invaluable resource.
More frequent and complex cyber threats require robust, automated
and rapid responses from cyber security specialists. This book
offers a complete study in the area of graph learning in cyber,
emphasising graph neural networks (GNNs) and their cyber security
applications. Three parts examine the basics; methods and
practices; and advanced topics. The first part presents a grounding
in graph data structures and graph embedding and gives a taxonomic
view of GNNs and cyber security applications. Part two explains
three different categories of graph learning including
deterministic, generative and reinforcement learning and how they
can be used for developing cyber defence models. The discussion of
each category covers the applicability of simple and complex
graphs, scalability, representative algorithms and technical
details. Undergraduate students, graduate students, researchers,
cyber analysts, and AI engineers looking to understand practical
deep learning methods will find this book an invaluable resource.
Digital forensics plays a crucial role in identifying, analysing,
and presenting cyber threats as evidence in a court of law.
Artificial intelligence, particularly machine learning and deep
learning, enables automation of the digital investigation process.
This book provides an in-depth look at the fundamental and advanced
methods in digital forensics. It also discusses how machine
learning and deep learning algorithms can be used to detect and
investigate cybercrimes. This book demonstrates digital forensics
and cyber-investigating techniques with real-world applications. It
examines hard disk analytics and style architectures, including
Master Boot Record and GUID Partition Table as part of the
investigative process. It also covers cyberattack analysis in
Windows, Linux, and network systems using virtual machines in
real-world scenarios. Digital Forensics in the Era of Artificial
Intelligence will be helpful for those interested in digital
forensics and using machine learning techniques in the
investigation of cyberattacks and the detection of evidence in
cybercrimes.
This book presents that explainable artificial intelligence (XAI)
is going to replace the traditional artificial, machine learning,
deep learning algorithms which work as a black box as of today. To
understand the algorithms better and interpret the complex networks
of these algorithms, XAI plays a vital role. In last few decades,
we have embraced AI in our daily life to solve a plethora of
problems, one of the notable problems is cyber security. In coming
years, the traditional AI algorithms are not able to address the
zero-day cyber attacks, and hence, to capitalize on the AI
algorithms, it is absolutely important to focus more on XAI. Hence,
this book serves as an excellent reference for those who are
working in cyber security and artificial intelligence.
This book states that the major aim audience are people who have
some familiarity with Internet of things (IoT) but interested to
get a comprehensive interpretation of the role of deep Learning in
maintaining the security and privacy of IoT. A reader should be
friendly with Python and the basics of machine learning and deep
learning. Interpretation of statistics and probability theory will
be a plus but is not certainly vital for identifying most of the
book's material.
This book presents that explainable artificial intelligence (XAI)
is going to replace the traditional artificial, machine learning,
deep learning algorithms which work as a black box as of today. To
understand the algorithms better and interpret the complex networks
of these algorithms, XAI plays a vital role. In last few decades,
we have embraced AI in our daily life to solve a plethora of
problems, one of the notable problems is cyber security. In coming
years, the traditional AI algorithms are not able to address the
zero-day cyber attacks, and hence, to capitalize on the AI
algorithms, it is absolutely important to focus more on XAI. Hence,
this book serves as an excellent reference for those who are
working in cyber security and artificial intelligence.
This book states that the major aim audience are people who have
some familiarity with Internet of things (IoT) but interested to
get a comprehensive interpretation of the role of deep Learning in
maintaining the security and privacy of IoT. A reader should be
friendly with Python and the basics of machine learning and deep
learning. Interpretation of statistics and probability theory will
be a plus but is not certainly vital for identifying most of the
book's material.
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AI 2020: Advances in Artificial Intelligence - 33rd Australasian Joint Conference, AI 2020, Canberra, ACT, Australia, November 29-30, 2020, Proceedings (Paperback, 1st ed. 2020)
Marcus Gallagher, Nour Moustafa, Erandi Lakshika
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R2,763
Discovery Miles 27 630
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Ships in 10 - 15 working days
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This book constitutes the proceedings of the 33rd Australasian
Joint Conference on Artificial Intelligence, AI 2020, held in
Canberra, ACT, Australia, in November 2020.*The 36 full papers
presented in this volume were carefully reviewed and selected from
57 submissions. The paper were organized in topical sections named:
applications; evolutionary computation; fairness and ethics; games
and swarms; and machine learning. *The conference was held
virtually due to the COVID-19 pandemic.
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