Metrics for Intrusion Detection in Wireless Sensor Networks | Measurement, Analysis and Evaluation

Daniel Thies

ISBN 10: 3639625978 / ISBN 13: 9783639625974

Published by AV Akademikerverlag, 2015

About this Item

Metrics for Intrusion Detection in Wireless Sensor Networks | Measurement, Analysis and Evaluation | Daniel Thies | Taschenbuch | 168 S. | Englisch | 2015 | AV Akademikerverlag | EAN 9783639625974 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 105400388

Bibliographic Details

• Title: Metrics for Intrusion Detection in Wireless Sensor Networks | Measurement, Analysis and Evaluation
• Publisher: AV Akademikerverlag
• Publication Date: 2015
• Language: English
• ISBN 10: 3639625978
• ISBN 13: 9783639625974
• 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

Wireless sensor networks are commonly deployed for collecting information about environments by using sensors. The nodes are restricted in their resources and dynamically maintain the network structure by themselves. Therefore, they are vulnerable to many intrusions or network attacks. In this book a novel approach for intrusion detection in wireless sensor networks is developed. Common performance and network metrics are considered to be indicators for a local intrusion detection on wireless sensor nodes. The mechanism does not need additional data collection, because most of the local metrics are already collected on each node. For the analysis the metrics are measured in different scenarios and evaluated in a real wireless sensor network deployment at the Technical University in Darmstadt. The considered and implemented network attacks for the intrusion detection are jamming and blackhole. The evaluation shows that several metrics are suitable for the detection. The results can be used for lightweight implementations of intrusion detection systems in wireless sensor networks.

About the Author

Daniel Thies, Master of Science: Born 1984 in Germany. Study of Computer Science at the Technical University in Darmstadt (Major Subject: IT Security, Minor Subject: Psychology). Scholarship of "Stiftung Industrieforschung". Nominated for CAST e.V. Sponsorship Award IT Security 2013. He currently works as an IT security consultant.