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Detecting Compromised IoT Devices Through XGBoost
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AI
DOI：10.1109/TITS.2022.3187252.png)
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摘要
En 中文
The evolution and rapid adoption of the Internet of Things (IoT) led to a rise in the number of attacks that target IoT environments. IoT environments are vulnerable to several attacks because many devices lack memory, processing power, and battery. Most of these vulnerabilities are relatively easy to mitigate when best practices are followed. However, even when best practices are followed, an attack to obtain a device credential and use it to generate false data is difficult to detect. Such an attack is called a replication attack and its impact can be catastrophic in crucial IoT scenarios such as smart transportation. In this sense, this paper proposes a solution to detect these attacks by analyzing abnormal network traffic through machine learning.
Keyword:
Internet of Things
Security
Protocols
Servers
Routing
Authorization
Authentication
Internet of Things
IoT
XGBoost
machine learning
security
replication attack
IoT-23
期刊
IF:
8.4
论文数:
9.7K
被引数:
6.3W
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