Screen LibraryData science in cybersecurity and cyberthreat intelligence /
Presents a collection of state-of-the-art approaches to utilizing machine learning, formal knowledge bases and rule sets, and semantic reasoning to detect attacks on communication networks, including IoT infrastructures, to automate malicious code detection, to efficiently predict cyberattacks in en...
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| Group Author: | ; |
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| Published: |
Springer,
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| Publisher Address: | Cham, Switzerland : |
| Publication Dates: | [2020] |
| Literature type: | Book |
| Language: | English |
| Series: |
Intelligent systems reference library,
volume 177 |
| Subjects: | |
| Summary: |
Presents a collection of state-of-the-art approaches to utilizing machine learning, formal knowledge bases and rule sets, and semantic reasoning to detect attacks on communication networks, including IoT infrastructures, to automate malicious code detection, to efficiently predict cyberattacks in enterprises, to identify malicious URLs and DGA-generated domain names, and to improve the security of mHealth wearables. This book details how analyzing the likelihood of vulnerability exploitation using machine learning classifiers can offer an alternative to traditional penetration testing solutions. In addition, the book describes a range of techniques that support data aggregation and data fusion to automate data-driven analytics in cyberthreat intelligence, allowing complex and previously unknown cyberthreats to be identified and classified, and countermeasures to be incorporated in novel incident response and intrusion detection mechanisms. |
| Carrier Form: | xii, 129 pages : illustrations (some color) ; 24 cm. |
| Bibliography: | Includes bibliographical references. |
| ISBN: |
9783030387877 3030387879 |
| Index Number: | QA76 |
| CLC: |
TP309 TP393.08 |
| Call Number: | TP393.08/D232-2 |