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Lightweight Meta-Learning BotNet Attack Detection
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DOI：10.1109/JIOT.2022.3229463.png)
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摘要
En 中文
Modern society is increasingly dependent on numerous Internet of Things (IoT) devices to assist in a variety of scenarios, such as smart homes and cities, healthcare systems, and cyber-physical systems. Despite IoT's increasing popularity, IoT security remains a challenge due to the multitude of attack vectors. Existing cyber-attack defense methods attempt to protect the network from both within and outside the network. Network intrusion detection systems (NIDSs) act as device borders within network security and offer a potential defense methodology. This research analyzes the performance of an Artificial Intelligent Internet of Things (AIoT) lightweight botnet attack detection model by deploying meta-learning ensemble botnet detection models and evaluates the capability of a single-board system in addressing cyber-attack threats. The Aposemat IoT-23 [1], UC Irvine KDD99 [2], and UNSW TON [3] data sets provide IoT and network traffic network flow captures which are used to evaluate the proposed meta-learning methodologies. Experiments show that deployment of our proposed methodologies on edge devices exhibits similar results to PC-based Desktop CPU-trained models. Over the three data sets, when considering a binary classifier (benign versus malignant), our models can consistently achieve above 97.9% accuracy with a false positive rate (FPR) less than 3.8% and an inference time less than 3.95 s. In this work, we show that for binary classification our meta-learners provide consistently stable high accuracy low FPR performance across all three data sets, while maintaining reasonable inference times.
Keyword:
Internet of Things
Botnet
Computational modeling
Feature extraction
Deep learning
Intrusion detection
Computer security
Artificially Intelligent Internet of Things (AIoT)
BotNet
ensemble learning
meta-learning
network intrusion detection system (NIDS)
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
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