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The IOT (Internet of Things) engulfs a widespread ecosystem made up of networks, processing technologies, and smart items. IoT is a popular platform that allows many of the objects in our environment to interact with one another via sensors and, most significantly, the internet, thereby rendering our lives easier. Today, users and attackers are paying close attention to anomaly detection on the Internet of Things. The development we are noticing is the outcome of enhanced sensor monitoring technologies. Gathering data is crucial for creating an estimate for analyzing unhealthy behavior. IoT networks are more accessible to cyberattacks, and the deployment of attacks within the network and user privacy are the primary concerns. Intrusion detection systems might be a smart plan to safeguard IoT networks from various threats. The following strategies were employed in this study to find anomalies: Decision Tree, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Logistical Classification. An assessment is made using the Accuracy, Precision, Recall, F1-Score, and Confusion Matrix. The UNSW-NB 15 dataset was used. The model’s predicted output is the identification of attacks or anomalies harmful to the devices and potentially damaging to user privacy and security.

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Malicious Attack Detection Using Deep Learning in IoT Network

  • 会议论文

  • 发表日期：

    2025年

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  • Amisha Srivastava,
  • V. Anantha Narayanan,
  • A. K. Sumesh

摘要

The IOT (Internet of Things) engulfs a widespread ecosystem made up of networks, processing technologies, and smart items. IoT is a popular platform that allows many of the objects in our environment to interact with one another via sensors and, most significantly, the internet, thereby rendering our lives easier. Today, users and attackers are paying close attention to anomaly detection on the Internet of Things. The development we are noticing is the outcome of enhanced sensor monitoring technologies. Gathering data is crucial for creating an estimate for analyzing unhealthy behavior. IoT networks are more accessible to cyberattacks, and the deployment of attacks within the network and user privacy are the primary concerns. Intrusion detection systems might be a smart plan to safeguard IoT networks from various threats. The following strategies were employed in this study to find anomalies: Decision Tree, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Logistical Classification. An assessment is made using the Accuracy, Precision, Recall, F1-Score, and Confusion Matrix. The UNSW-NB 15 dataset was used. The model’s predicted output is the identification of attacks or anomalies harmful to the devices and potentially damaging to user privacy and security.