Mobile Agent-Based Anomaly Detection and Verification System for Smart Home Sensor Networks
Nonfiction, Science & Nature, Mathematics, Logic, Computers, Advanced Computing, Information Technology, General Computing
| Author: | Muhammad Usman, Vallipuram Muthukkumarasamy, Xin-Wen Wu, Surraya Khanum | ISBN: | 9789811074677 |
| Publisher: | Springer Singapore | Publication: | January 31, 2018 |
| Imprint: | Springer | Language: | English |
| Author: | Muhammad Usman, Vallipuram Muthukkumarasamy, Xin-Wen Wu, Surraya Khanum |
| ISBN: | 9789811074677 |
| Publisher: | Springer Singapore |
| Publication: | January 31, 2018 |
| Imprint: | Springer |
| Language: | English |
This book presents the latest developments regarding a detailed mobile agent-enabled anomaly detection and verification system for resource constrained sensor networks; a number of algorithms on multi-aspect anomaly detection in sensor networks; several algorithms on mobile agent transmission optimization in resource constrained sensor networks; an algorithm on mobile agent-enabled in situ verification of anomalous sensor nodes; a detailed Petri Net-based formal modeling and analysis of the proposed system, and an algorithm on fuzzy logic-based cross-layer anomaly detection and mobile agent transmission optimization. As such, it offers a comprehensive text for interested readers from academia and industry alike.
This book presents the latest developments regarding a detailed mobile agent-enabled anomaly detection and verification system for resource constrained sensor networks; a number of algorithms on multi-aspect anomaly detection in sensor networks; several algorithms on mobile agent transmission optimization in resource constrained sensor networks; an algorithm on mobile agent-enabled in situ verification of anomalous sensor nodes; a detailed Petri Net-based formal modeling and analysis of the proposed system, and an algorithm on fuzzy logic-based cross-layer anomaly detection and mobile agent transmission optimization. As such, it offers a comprehensive text for interested readers from academia and industry alike.
