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{ "item_title" : "Intrusion Detection System", "item_author" : [" Kanimozhi D", "Nathiya V. C. "], "item_description" : "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.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/6/20/764/684/6207646843_b.jpg", "price_data" : { "retail_price" : "66.00", "online_price" : "66.00", "our_price" : "66.00", "club_price" : "66.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Intrusion Detection System|Kanimozhi D

Intrusion Detection System

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Overview

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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Details

  • ISBN-13: 9786207646845
  • ISBN-10: 6207646843
  • Publisher: LAP Lambert Academic Publishing
  • Publish Date: September 2024
  • Dimensions: 9 x 6 x 0.22 inches
  • Shipping Weight: 0.32 pounds
  • Page Count: 92

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