Deep Learning | Deep Learning Approaches for Intrusion Detection and Attack Severity Classification in IOT Network

Bhukya Madhu (u. a.)

ISBN 10: 6205527960 / ISBN 13: 9786205527962

Published by LAP LAMBERT Academic Publishing, 2022

About this Item

Deep Learning | Deep Learning Approaches for Intrusion Detection and Attack Severity Classification in IOT Network | Bhukya Madhu (u. a.) | Taschenbuch | Englisch | 2022 | LAP LAMBERT Academic Publishing | EAN 9786205527962 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 126452020

Bibliographic Details

• Title: Deep Learning | Deep Learning Approaches for Intrusion Detection and Attack Severity Classification in IOT Network
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2022
• Language: English
• ISBN 10: 6205527960
• ISBN 13: 9786205527962
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 11D millimeters 220 millimeters width by 150 millimeters height by 11 millimeters depth
• Weight: 268 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

The Internet of things (IoT) has gained more attention in recent years because of its ubiquitous operations, connectivity, methods of communication, and intelligent decisions to evoke activities from various devices. Therefore, artificial intelligence techniques have been integrated into all aspects of the Internet of Things and making life more comfortable in various ways. A novel deep learning model named Device-based Intrusion Detection System (DIDS) was proposed in the second phase. This DIDS learning model incorporates the prediction of unknown attacks to handle the computational overhead in large networks and increase the throughput with a low false alarm rate. Our proposed algorithm has been evaluated with standard algorithms, and the results show that it detects attacks earlier than standard algorithms. The computational time has also been reduced, and 99% of accuracy has been achieved in detecting the attacks.