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This SpringerBrief discusses how to develop intelligent systems for
cyber attribution regarding cyber-attacks. Specifically, the
authors review the multiple facets of the cyber attribution problem
that make it difficult for "out-of-the-box" artificial intelligence
and machine learning techniques to handle. Attributing a
cyber-operation through the use of multiple pieces of technical
evidence (i.e., malware reverse-engineering and source tracking)
and conventional intelligence sources (i.e., human or signals
intelligence) is a difficult problem not only due to the effort
required to obtain evidence, but the ease with which an adversary
can plant false evidence. This SpringerBrief not only lays out the
theoretical foundations for how to handle the unique aspects of
cyber attribution - and how to update models used for this purpose
- but it also describes a series of empirical results, as well as
compares results of specially-designed frameworks for cyber
attribution to standard machine learning approaches. Cyber
attribution is not only a challenging problem, but there are also
problems in performing such research, particularly in obtaining
relevant data. This SpringerBrief describes how to use
capture-the-flag for such research, and describes issues from
organizing such data to running your own capture-the-flag
specifically designed for cyber attribution. Datasets and software
are also available on the companion website.
Malicious hackers utilize the World Wide Web to share knowledge.
Analyzing the online communication of these threat actors can help
reduce the risk of attacks. This book shifts attention from the
defender environment to the attacker environment, offering a new
security paradigm of 'proactive cyber threat intelligence' that
allows defenders of computer networks to gain a better
understanding of their adversaries by analyzing assets,
capabilities, and interest of malicious hackers. The authors
propose models, techniques, and frameworks based on threat
intelligence mined from the heart of the underground cyber world:
the malicious hacker communities. They provide insights into the
hackers themselves and the groups they form dynamically in the act
of exchanging ideas and techniques, buying or selling malware, and
exploits. The book covers both methodology - a hybridization of
machine learning, artificial intelligence, and social network
analysis methods - and the resulting conclusions, detailing how a
deep understanding of malicious hacker communities can be the key
to designing better attack prediction systems.
The important and rapidly emerging new field known as 'cyber threat
intelligence' explores the paradigm that defenders of computer
networks gain a better understanding of their adversaries by
understanding what assets they have available for an attack. In
this book, a team of experts examines a new type of cyber threat
intelligence from the heart of the malicious hacking underworld -
the dark web. These highly secure sites have allowed anonymous
communities of malicious hackers to exchange ideas and techniques,
and to buy/sell malware and exploits. Aimed at both cybersecurity
practitioners and researchers, this book represents a first step
toward a better understanding of malicious hacking communities on
the dark web and what to do about them. The authors examine
real-world darkweb data through a combination of human and
automated techniques to gain insight into these communities,
describing both methodology and results.
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