Software-Defined Networks (SDN) offer enhanced network management and control, but also introduce new security vulnerabilities. This book presents a comprehensive approach for intrusion detection in SDN environments, combining Elliptic Curve Cryptography (ECC) for secure data transmission with a hybrid machine learning model for accurate attack classification. The system utilizes the Curve25519-Dalek-Hash (CDH) key exchange protocol to encrypt sensitive network data, ensuring confidentiality and integrity. A hybrid model integrating XG Boost and Light GBM algorithms is employed for efficient and accurate attack detection. The proposed system is evaluated on a real-world SDN dataset, demonstrating high accuracy and efficiency in identifying malicious activities.
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Software-Defined Networks (SDN) offer enhanced network management and control, but also introduce new security vulnerabilities. This book presents a comprehensive approach for intrusion detection in SDN environments, combining Elliptic Curve Cryptography (ECC) for secure data transmission with a hybrid machine learning model for accurate attack classification. The system utilizes the Curve25519-Dalek-Hash (CDH) key exchange protocol to encrypt sensitive network data, ensuring confidentiality and integrity. A hybrid model integrating XG Boost and Light GBM algorithms is employed for efficient and accurate attack detection. The proposed system is evaluated on a real-world SDN dataset, demonstrating high accuracy and efficiency in identifying malicious activities.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 92 pp. Englisch. Seller Inventory # 9786207646845
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Taschenbuch. Condition: Neu. INTRUSION DETECTION SYSTEM | INTRUSION DETECTION WHILE ACCESSING LOGDATA FILES IN CLOUD DATALAKEARCHITECTURE | Kanimozhi D (u. a.) | Taschenbuch | Englisch | 2024 | LAP LAMBERT Academic Publishing | EAN 9786207646845 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. Seller Inventory # 130285124
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