In this paper, we explore an adaptive online learning strategy for real-time IoT botnet attack detection.
In this paper, we explore an adaptive online learning strategy for real-time IoT botnet attack detection. Furthermore, we operate the proposed adaptive strategy�...
Oct 25, 2021 � To evaluate the proposed strategy, we use real IoT traffic data, including benign traffic data and botnet traffic data infected by Mirai. In�...
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Feb 23, 2024 � Deep learning approaches can analyze various data sources, such as network traffic, device logs, and sensor readings, to detect botnet-related�...
Oct 4, 2022 � This study proposes machine learning methods for classifying binary classes. This purpose is served by using the publicly available dataset UNSW-NB15.
The proposed work is focused on developing an adaptive voting classifier that can detect all the attack categories in BoT-IoT dataset with higher performance�...
Missing: online learning
In this study, we propose an adaptive online DDoS attack detection framework that detects and adapts to concept drifts in streaming data.
This paper proposes a self-adaptive anomaly detection system for IoT traffic, including unknown attacks. The proposed system comprises a honeypot server and a�...
Missing: online | Show results with:online
Oct 12, 2023 � Abstract: In the dynamic landscape of cyber threats, multistage malware botnets have surfaced as significant threats of concern.
The proposed model integrates NIDS with federated learning, allowing devices to locally analyze their data and contribute to the detection of anomalous traffic.
