In this study, differently from other studies, autoencoder based deep learning machines are proposed for intrusion detection. KDDcup99 data set containing 22 ...
In this study we illustrate a new intrusion detection method that analyses the flow-based characteristics of the network traffic data.
A deep learning approach for an intrusion detection system (IDS) based on a deep autoencoder (DAE), which is one of the most well-known deep learning models.
Apr 11, 2022 · IDS based on machine/deep learning is currently the main focus in recent research studies in intrusion detection as they are more effective ...
This paper proposes an effective deep learning method, namely AE-IDS (Auto-Encoder Intrusion Detection System) based on random forest algorithm.
Datasets used for intrusion detection should be dynamically generated to reflect current-day attacks. In addition to dynamicity, the dataset should also be ...
Mar 14, 2024 · objective is to develop an intrusion detection system using a special deep learning technique known as an autoencoder to detect network anomaly.
Feb 15, 2024 · An improved deep autoencoder-based network intrusion detection system with enhanced performance · and · Shouvik Dey.
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In this paper, we develop an IDS based on the autoencoder deep learning model (AE-IDS) for the SCADA system. The target SCADA communication protocol of the ...
AutoIDS is presented, a novel yet efficient solution for IDS, based on a semi-supervised machine learning technique that can distinguish abnormal packet ...