التفاصيل البيبلوغرافية
| العنوان: |
A Hybrid Trust Model against Insider Packet Drop Attacks in Wireless Sensor Networks |
| المؤلفون: |
Youngho Cho, Gang Qu |
| المصدر: |
Sensors, Vol 23, Iss 9, p 4407 (2023) |
| بيانات النشر: |
MDPI AG, 2023. |
| سنة النشر: |
2023 |
| المجموعة: |
LCC:Chemical technology |
| مصطلحات موضوعية: |
insider packet drop attack, trust model, trust-based routing, wireless sensor networks, Chemical technology, TP1-1185 |
| الوصف: |
Quick and accurate detection of inside packet drop attackers is of critical importance to reduce the damage they can have on the network. Trust mechanisms have been widely used in wireless sensor networks for this purpose. However, existing trust models are not effective because they cannot distinguish between packet drops caused by an attack and those caused by normal network failure. We observe that insider packet drop attacks will cause more consecutive packet drops than a network abnormality. Therefore, we propose the use of consecutive packet drops to speed up the detection of inside packet drop attackers. In this article, we describe a new trust model based on consecutive drops and develop a hybrid trust mechanism to seamlessly integrate the new trust model with existing trust models. We perform extensive OPNET (Optimized Network Engineering Tool) simulations using a geographic greedy routing protocol to validate the effectiveness of our new model. The simulation results show that our hybrid trust model outperforms existing trust models for all types of inside packet drop attacks, not only in terms of detection speed and accuracy as it is designed for, but also in terms of other important network performance metrics, such as packet delivery rate, routing reliability, and energy efficiency. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
1424-8220 |
| Relation: |
https://www.mdpi.com/1424-8220/23/9/4407; https://doaj.org/toc/1424-8220 |
| DOI: |
10.3390/s23094407 |
| URL الوصول: |
https://doaj.org/article/103468cefc4f4351a9ab88e7216ec0eb |
| رقم الانضمام: |
edsdoj.103468cefc4f4351a9ab88e7216ec0eb |
| قاعدة البيانات: |
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