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From Heterogeneous Network Datasets to Real-Time Intrusion Detection Systems: Feature Harmonization, Dataset Engineering, and Operational AI for Modern Intrusion Detection - Brossura
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Modern Network Intrusion Detection Systems (NIDS) often achieve remarkable accuracy on benchmark datasets, yet many fail when deployed in real-world environments due to inconsistent feature representations, dataset bias, and limited cross-domain generalization. This book presents a data-centric approach to intrusion detection, demonstrating how principled data engineering can significantly improve operational performance without relying on increasingly complex deep learning architectures. Drawing from the development of the Aegis DLP platform, the book introduces a complete pipeline for transforming heterogeneous cybersecurity datasets—including KDD Cup'99, UNSW-NB15, CICIDS 2018, and live packet captures—into a unified feature space suitable for real-time intrusion detection. Readers are guided through semantic feature alignment, statistical feature derivation, temporal behavioral modeling, and the design of scalable preprocessing pipelines capable of supporting both offline training and online inference. Beyond dataset engineering, the book explores the implementation of a real-time monitoring framework using Scapy, Python, Flask, and machine learning, validated through a controlled virtual laboratory consisting of Kali Linux, Metasploitable 2, and Windows-based packet capture. Multiple machine learning approaches are examined before presenting a lightweight Multi-Layer Perceptron optimized for operational deployment. Rather than focusing solely on achieving high benchmark accuracy, this work emphasizes cross-dataset generalization, real-world validation, reproducible feature engineering, and practical cybersecurity deployment. It is intended for researchers, cybersecurity professionals, postgraduate students, and AI practitioners seeking to bridge the gap between academic intrusion detection research and deployable enterprise security solutions.
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From Heterogeneous Network Datasets to Real-Time Intrusion Detection Systems (Paperback)
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Paperback. Condizione: new. Paperback. Modern Network Intrusion Detection Systems (NIDS) often achieve remarkable accuracy on benchmark datasets, yet many fail when deployed in real-world environments due to inconsistent feature representations, dataset bias, and limited cross-domain generalization. This book presents a data-centric approach to intrusion detection, demonstrating how principled data engineering can significantly improve operational performance without relying on increasingly complex deep learning architectures. Drawing from the development of the Aegis DLP platform, the book introduces a complete pipeline for transforming heterogeneous cybersecurity datasets-including KDD Cup'99, UNSW-NB15, CICIDS 2018, and live packet captures-into a unified feature space suitable for real-time intrusion detection. Readers are guided through semantic feature alignment, statistical feature derivation, temporal behavioral modeling, and the design of scalable preprocessing pipelines capable of supporting both offline training and online inference. Beyond dataset engineering, the book explores the implementation of a real-time monitoring framework using Scapy, Python, Flask, and machine learning, validated through a controlled virtual laboratory consisting of Kali Linux, Metasploitable 2, and Windows-based packet capture. Multiple machine learning approaches are examined before presenting a lightweight Multi-Layer Perceptron optimized for operational deployment. Rather than focusing solely on achieving high benchmark accuracy, this work emphasizes cross-dataset generalization, real-world validation, reproducible feature engineering, and practical cybersecurity deployment. It is intended for researchers, cybersecurity professionals, postgraduate students, and AI practitioners seeking to bridge the gap between academic intrusion detection research and deployable enterprise security solutions. 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 9789999349673
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From Heterogeneous Network Datasets to Real-Time Intrusion Detection Systems (Paperback)
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Paperback. Condizione: new. Paperback. Modern Network Intrusion Detection Systems (NIDS) often achieve remarkable accuracy on benchmark datasets, yet many fail when deployed in real-world environments due to inconsistent feature representations, dataset bias, and limited cross-domain generalization. This book presents a data-centric approach to intrusion detection, demonstrating how principled data engineering can significantly improve operational performance without relying on increasingly complex deep learning architectures. Drawing from the development of the Aegis DLP platform, the book introduces a complete pipeline for transforming heterogeneous cybersecurity datasets-including KDD Cup'99, UNSW-NB15, CICIDS 2018, and live packet captures-into a unified feature space suitable for real-time intrusion detection. Readers are guided through semantic feature alignment, statistical feature derivation, temporal behavioral modeling, and the design of scalable preprocessing pipelines capable of supporting both offline training and online inference. Beyond dataset engineering, the book explores the implementation of a real-time monitoring framework using Scapy, Python, Flask, and machine learning, validated through a controlled virtual laboratory consisting of Kali Linux, Metasploitable 2, and Windows-based packet capture. Multiple machine learning approaches are examined before presenting a lightweight Multi-Layer Perceptron optimized for operational deployment. Rather than focusing solely on achieving high benchmark accuracy, this work emphasizes cross-dataset generalization, real-world validation, reproducible feature engineering, and practical cybersecurity deployment. It is intended for researchers, cybersecurity professionals, postgraduate students, and AI practitioners seeking to bridge the gap between academic intrusion detection research and deployable enterprise security solutions. 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 9789999349673
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Paperback. Condizione: new. Paperback. Modern Network Intrusion Detection Systems (NIDS) often achieve remarkable accuracy on benchmark datasets, yet many fail when deployed in real-world environments due to inconsistent feature representations, dataset bias, and limited cross-domain generalization. This book presents a data-centric approach to intrusion detection, demonstrating how principled data engineering can significantly improve operational performance without relying on increasingly complex deep learning architectures. Drawing from the development of the Aegis DLP platform, the book introduces a complete pipeline for transforming heterogeneous cybersecurity datasets-including KDD Cup'99, UNSW-NB15, CICIDS 2018, and live packet captures-into a unified feature space suitable for real-time intrusion detection. Readers are guided through semantic feature alignment, statistical feature derivation, temporal behavioral modeling, and the design of scalable preprocessing pipelines capable of supporting both offline training and online inference. Beyond dataset engineering, the book explores the implementation of a real-time monitoring framework using Scapy, Python, Flask, and machine learning, validated through a controlled virtual laboratory consisting of Kali Linux, Metasploitable 2, and Windows-based packet capture. Multiple machine learning approaches are examined before presenting a lightweight Multi-Layer Perceptron optimized for operational deployment. Rather than focusing solely on achieving high benchmark accuracy, this work emphasizes cross-dataset generalization, real-world validation, reproducible feature engineering, and practical cybersecurity deployment. It is intended for researchers, cybersecurity professionals, postgraduate students, and AI practitioners seeking to bridge the gap between academic intrusion detection research and deployable enterprise security solutions. 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 9789999349673
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From Heterogeneous Network Datasets to Real-Time Intrusion Detection Systems : Feature Harmonization, Dataset Engineering, and Operational AI for Modern Intrusion Detection
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Modern Network Intrusion Detection Systems (NIDS) often achieve remarkable accuracy on benchmark datasets, yet many fail when deployed in real-world environments due to inconsistent feature representations, dataset bias, and limited cross-domain generalization. This book presents a data-centric approach to intrusion detection, demonstrating how principled data engineering can significantly improve operational performance without relying on increasingly complex deep learning architectures. Drawing from the development of the Aegis DLP platform, the book introduces a complete pipeline for transforming heterogeneous cybersecurity datasets-including KDD Cup'99, UNSW-NB15, CICIDS 2018, and live packet captures-into a unified feature space suitable for real-time intrusion detection. Readers are guided through semantic feature alignment, statistical feature derivation, temporal behavioral modeling, and the design of scalable preprocessing pipelines capable of supporting both offline training and online inference. Beyond dataset engineering, the book explores the implementation of a real-time monitoring framework using Scapy, Python, Flask, and machine learning, validated through a controlled virtual laboratory consisting of Kali Linux, Metasploitable 2, and Windows-based packet capture. Multiple machine learning approaches are examined before presenting a lightweight Multi-Layer Perceptron optimized for operational deployment. Rather than focusing solely on achieving high benchmark accuracy, this work emphasizes cross-dataset generalization, real-world validation, reproducible feature engineering, and practical cybersecurity deployment. It is intended for researchers, cybersecurity professionals, postgraduate students, and AI practitioners seeking to bridge the gap between academic intrusion detection research and deployable enterprise security solutions. Codice articolo 9789999349673
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From Heterogeneous Network Datasets to Real-Time Intrusion Detection Systems
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Modern Network Intrusion Detection Systems (NIDS) often achieve remarkable accuracy on benchmark datasets, yet many fail when deployed in real-world environments due to inconsistent feature representations, dataset bias, and limited cross-domain generalization. This book presents a data-centric approach to intrusion detection, demonstrating how principled data engineering can significantly improve operational performance without relying on increasingly complex deep learning architectures. Drawing from the development of the Aegis DLP platform, the book introduces a complete pipeline for transforming heterogeneous cybersecurity datasets-including KDD Cup'99, UNSW-NB15, CICIDS 2018, and live packet captures-into a unified feature space suitable for real-time intrusion detection. Readers are guided through semantic feature alignment, statistical feature derivation, temporal behavioral modeling, and the design of scalable preprocessing pipelines capable of supporting both offline training and online inference. Beyond dataset engineering, the book explores the implementation of a real-time monitoring framework using Scapy, Python, Flask, and machine learning, validated through a controlled virtual laboratory consisting of Kali Linux, Metasploitable 2, and Windows-based packet capture. Multiple machine learning approaches are examined before presenting a lightweight Multi-Layer Perceptron optimized for operational deployment. Rather than focusing solely on achieving high benchmark accuracy, this work emphasizes cross-dataset generalization, real-world validation, reproducible feature engineering, and practical cybersecurity deployment. It is intended for researchers, cybersecurity professionals, postgraduate students, and AI practitioners seeking to bridge the gap between academic intrusion detection research and deployable enterprise security solutions. 94 pp. Englisch. Codice articolo 9789999349673
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From Heterogeneous Network Datasets to Real-Time Intrusion Detection Systems | Feature Harmonization, Dataset Engineering, and Operational AI for Modern Intrusion Detection
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Taschenbuch. Condizione: Neu. From Heterogeneous Network Datasets to Real-Time Intrusion Detection Systems | Feature Harmonization, Dataset Engineering, and Operational AI for Modern Intrusion Detection | Ayush Gupta | Taschenbuch | Englisch | 2026 | Eliva Press | EAN 9789999349673 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Codice articolo 136969238
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