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Next-Generation Cyber Defense: Deep Learning Approaches for Intrusion Detection and Prevention - Brossura
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Cybersecurity has become one of the most critical challenges of the digital era, with the rapid growth of cloud computing, the Internet of Things (IoT), Industrial IoT (IIoT), and interconnected smart systems. Traditional intrusion detection methods struggle to identify sophisticated and previously unseen cyberattacks, creating a pressing need for intelligent, adaptive, and real-time security solutions. Next-Generation Cyber Defense: Deep Learning Approaches for Intrusion Detection and Prevention presents advanced artificial intelligence techniques for designing highly effective Intrusion Detection and Prevention Systems (IDPS). The book introduces two novel frameworks: the DCNN-BiGRU hybrid deep learning model, which combines Deep Convolutional Neural Networks with Bidirectional Gated Recurrent Units for accurate multiclass attack detection, and the Leading Ensemble Decision Classifier Module (LEDCM), an innovative online ensemble framework that enhances prediction accuracy through dynamic class-wise leader selection. These models achieve outstanding performance on benchmark cybersecurity datasets, demonstrating their effectiveness in detecting both known and emerging cyber threats. The book provides a comprehensive overview of intrusion detection systems, machine learning and deep learning techniques, ensemble learning, feature engineering, data preprocessing, performance evaluation, and real-world implementation using benchmark datasets. It bridges theoretical concepts with practical methodologies, enabling readers to understand, design, and deploy intelligent cyber defense systems for modern network environments. In addition, it explores emerging research directions such as explainable AI, federated learning, adversarial robustness, and edge-based intrusion detection, offering valuable insights into the future of cybersecurity. Designed for researchers, postgraduate students, cybersecurity professionals, and AI practitioners, this book serves as both an academic reference and a practical guide. By integrating cutting-edge deep learning architectures with robust ensemble techniques, it provides scalable and reliable solutions for protecting next-generation.
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Next-Generation Cyber Defense (Paperback)
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Paperback. Condizione: new. Paperback. Cybersecurity has become one of the most critical challenges of the digital era, with the rapid growth of cloud computing, the Internet of Things (IoT), Industrial IoT (IIoT), and interconnected smart systems. Traditional intrusion detection methods struggle to identify sophisticated and previously unseen cyberattacks, creating a pressing need for intelligent, adaptive, and real-time security solutions. Next-Generation Cyber Defense: Deep Learning Approaches for Intrusion Detection and Prevention presents advanced artificial intelligence techniques for designing highly effective Intrusion Detection and Prevention Systems (IDPS). The book introduces two novel frameworks: the DCNN-BiGRU hybrid deep learning model, which combines Deep Convolutional Neural Networks with Bidirectional Gated Recurrent Units for accurate multiclass attack detection, and the Leading Ensemble Decision Classifier Module (LEDCM), an innovative online ensemble framework that enhances prediction accuracy through dynamic class-wise leader selection. These models achieve outstanding performance on benchmark cybersecurity datasets, demonstrating their effectiveness in detecting both known and emerging cyber threats. The book provides a comprehensive overview of intrusion detection systems, machine learning and deep learning techniques, ensemble learning, feature engineering, data preprocessing, performance evaluation, and real-world implementation using benchmark datasets. It bridges theoretical concepts with practical methodologies, enabling readers to understand, design, and deploy intelligent cyber defense systems for modern network environments. In addition, it explores emerging research directions such as explainable AI, federated learning, adversarial robustness, and edge-based intrusion detection, offering valuable insights into the future of cybersecurity. Designed for researchers, postgraduate students, cybersecurity professionals, and AI practitioners, this book serves as both an academic reference and a practical guide. By integrating cutting-edge deep learning architectures with robust ensemble techniques, it provides scalable and reliable solutions for protecting next-generation. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9789999347648
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Paperback. Condizione: new. Paperback. Cybersecurity has become one of the most critical challenges of the digital era, with the rapid growth of cloud computing, the Internet of Things (IoT), Industrial IoT (IIoT), and interconnected smart systems. Traditional intrusion detection methods struggle to identify sophisticated and previously unseen cyberattacks, creating a pressing need for intelligent, adaptive, and real-time security solutions. Next-Generation Cyber Defense: Deep Learning Approaches for Intrusion Detection and Prevention presents advanced artificial intelligence techniques for designing highly effective Intrusion Detection and Prevention Systems (IDPS). The book introduces two novel frameworks: the DCNN-BiGRU hybrid deep learning model, which combines Deep Convolutional Neural Networks with Bidirectional Gated Recurrent Units for accurate multiclass attack detection, and the Leading Ensemble Decision Classifier Module (LEDCM), an innovative online ensemble framework that enhances prediction accuracy through dynamic class-wise leader selection. These models achieve outstanding performance on benchmark cybersecurity datasets, demonstrating their effectiveness in detecting both known and emerging cyber threats. The book provides a comprehensive overview of intrusion detection systems, machine learning and deep learning techniques, ensemble learning, feature engineering, data preprocessing, performance evaluation, and real-world implementation using benchmark datasets. It bridges theoretical concepts with practical methodologies, enabling readers to understand, design, and deploy intelligent cyber defense systems for modern network environments. In addition, it explores emerging research directions such as explainable AI, federated learning, adversarial robustness, and edge-based intrusion detection, offering valuable insights into the future of cybersecurity. Designed for researchers, postgraduate students, cybersecurity professionals, and AI practitioners, this book serves as both an academic reference and a practical guide. By integrating cutting-edge deep learning architectures with robust ensemble techniques, it provides scalable and reliable solutions for protecting next-generation. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Codice articolo 9789999347648
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Cybersecurity has become one of the most critical challenges of the digital era, with the rapid growth of cloud computing, the Internet of Things (IoT), Industrial IoT (IIoT), and interconnected smart systems. Traditional intrusion detection methods struggle to identify sophisticated and previously unseen cyberattacks, creating a pressing need for intelligent, adaptive, and real-time security solutions. Next-Generation Cyber Defense: Deep Learning Approaches for Intrusion Detection and Prevention presents advanced artificial intelligence techniques for designing highly effective Intrusion Detection and Prevention Systems (IDPS). The book introduces two novel frameworks: the DCNN-BiGRU hybrid deep learning model, which combines Deep Convolutional Neural Networks with Bidirectional Gated Recurrent Units for accurate multiclass attack detection, and the Leading Ensemble Decision Classifier Module (LEDCM), an innovative online ensemble framework that enhances prediction accuracy through dynamic class-wise leader selection. These models achieve outstanding performance on benchmark cybersecurity datasets, demonstrating their effectiveness in detecting both known and emerging cyber threats. The book provides a comprehensive overview of intrusion detection systems, machine learning and deep learning techniques, ensemble learning, feature engineering, data preprocessing, performance evaluation, and real-world implementation using benchmark datasets. It bridges theoretical concepts with practical methodologies, enabling readers to understand, design, and deploy intelligent cyber defense systems for modern network environments. In addition, it explores emerging research directions such as explainable AI, federated learning, adversarial robustness, and edge-based intrusion detection, offering valuable insights into the future of cybersecurity. Designed for researchers, postgraduate students, cybersecurity professionals, and AI practitioners, this book serves as both an academic reference and a practical guide. By integrating cutting-edge deep learning architectures with robust ensemble techniques, it provides scalable and reliable solutions for protecting next-generation. Codice articolo 9789999347648
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Paperback. Condizione: new. Paperback. Cybersecurity has become one of the most critical challenges of the digital era, with the rapid growth of cloud computing, the Internet of Things (IoT), Industrial IoT (IIoT), and interconnected smart systems. Traditional intrusion detection methods struggle to identify sophisticated and previously unseen cyberattacks, creating a pressing need for intelligent, adaptive, and real-time security solutions. Next-Generation Cyber Defense: Deep Learning Approaches for Intrusion Detection and Prevention presents advanced artificial intelligence techniques for designing highly effective Intrusion Detection and Prevention Systems (IDPS). The book introduces two novel frameworks: the DCNN-BiGRU hybrid deep learning model, which combines Deep Convolutional Neural Networks with Bidirectional Gated Recurrent Units for accurate multiclass attack detection, and the Leading Ensemble Decision Classifier Module (LEDCM), an innovative online ensemble framework that enhances prediction accuracy through dynamic class-wise leader selection. These models achieve outstanding performance on benchmark cybersecurity datasets, demonstrating their effectiveness in detecting both known and emerging cyber threats. The book provides a comprehensive overview of intrusion detection systems, machine learning and deep learning techniques, ensemble learning, feature engineering, data preprocessing, performance evaluation, and real-world implementation using benchmark datasets. It bridges theoretical concepts with practical methodologies, enabling readers to understand, design, and deploy intelligent cyber defense systems for modern network environments. In addition, it explores emerging research directions such as explainable AI, federated learning, adversarial robustness, and edge-based intrusion detection, offering valuable insights into the future of cybersecurity. Designed for researchers, postgraduate students, cybersecurity professionals, and AI practitioners, this book serves as both an academic reference and a practical guide. By integrating cutting-edge deep learning architectures with robust ensemble techniques, it provides scalable and reliable solutions for protecting next-generation. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9789999347648
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Cybersecurity has become one of the most critical challenges of the digital era, with the rapid growth of cloud computing, the Internet of Things (IoT), Industrial IoT (IIoT), and interconnected smart systems. Traditional intrusion detection methods struggle to identify sophisticated and previously unseen cyberattacks, creating a pressing need for intelligent, adaptive, and real-time security solutions. Next-Generation Cyber Defense: Deep Learning Approaches for Intrusion Detection and Prevention presents advanced artificial intelligence techniques for designing highly effective Intrusion Detection and Prevention Systems (IDPS). The book introduces two novel frameworks: the DCNN-BiGRU hybrid deep learning model, which combines Deep Convolutional Neural Networks with Bidirectional Gated Recurrent Units for accurate multiclass attack detection, and the Leading Ensemble Decision Classifier Module (LEDCM), an innovative online ensemble framework that enhances prediction accuracy through dynamic class-wise leader selection. These models achieve outstanding performance on benchmark cybersecurity datasets, demonstrating their effectiveness in detecting both known and emerging cyber threats. The book provides a comprehensive overview of intrusion detection systems, machine learning and deep learning techniques, ensemble learning, feature engineering, data preprocessing, performance evaluation, and real-world implementation using benchmark datasets. It bridges theoretical concepts with practical methodologies, enabling readers to understand, design, and deploy intelligent cyber defense systems for modern network environments. In addition, it explores emerging research directions such as explainable AI, federated learning, adversarial robustness, and edge-based intrusion detection, offering valuable insights into the future of cybersecurity. Designed for researchers, postgraduate students, cybersecurity professionals, and AI practitioners, this book serves as both an academic reference and a practical guide. By integrating cutting-edge deep learning architectures with robust ensemble techniques, it provides scalable and reliable solutions for protecting next-generation. 66 pp. Englisch. Codice articolo 9789999347648
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Next-Generation Cyber Defense | Deep Learning Approaches for Intrusion Detection and Prevention
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Taschenbuch. Condizione: Neu. Next-Generation Cyber Defense | Deep Learning Approaches for Intrusion Detection and Prevention | Garima Mathur (u. a.) | Taschenbuch | Englisch | 2026 | Eliva Press | EAN 9789999347648 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Codice articolo 136944308
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