Deep learning for intrusion detection techniques and applications / edited by Faheem Syeed Masoodi, Alwi Bamhdi.

Other author Masoodi, Faheem Syeed
Other author Bamhdi, Alwi
Format Electronic
Publication InfoHoboken, New Jersey : Wiley, [2026]
Descriptionpages cm
Supplemental ContentFull text available from IEEE Xplore Wiley Data and Cybersecurity eBooks Library
Supplemental ContentFull text available from eBooks on EBSCOhost
Subjects

Contents Intrusion detection in the age of deep learning : an introduction -- Machine learning for intrusion detection -- Deep learning fundamentals-I -- Deep learning fundamentals-II -- Intrusion detection through deep learning: emerging trends and challenges -- Dataset for evaluating deep learning-based intrusion detection -- Deep learning features: techniques for extraction and selection -- Exploring advanced artificial intelligence for anomaly detection -- Enhancing security in smart environments using deep learning: a comprehensive approach -- Deep learning-based intrusion detection in wireless networks -- Deep learning-based intrusion detection in wireless networks -- Securing IoT environments : deep learning-based intrusion detection -- A deep learning approach for the detection of zero-day attacks.
Abstract "Deep learning for intrusion detection is an emerging field that combines the power of machine learning with cybersecurity. Intrusion detection is an essential aspect of cybersecurity that involves detecting and preventing unauthorized access to computer systems. Traditional methods of intrusion detection have been limited in their effectiveness and have struggled to keep up with the evolving nature of cyber threats. Deep learning is a subfield of machine learning that uses artificial neural networks to analyze and learn from large sets of data. It has shown great potential in various applications, including image and speech recognition, natural language processing, and now intrusion detection"-- Provided by publisher.
Bibliography noteIncludes bibliographical references.
Access restrictionAvailable only to authorized users.
Technical detailsMode of access: World Wide Web
Genre/formElectronic books.
LCCN 2025043963
ISBN9781394285167 hardback
ISBNadobe pdf
ISBNepub

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