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<p>NB-IoT is a form of network, which is designed to link smart devices that do not require substantial power or transmit high data, making it suitable for things such as sensors and trackers. The issue of security is quite a concern because of the numerous interconnected devices and possible attacks. In this paper, a distributed security architecture utilizing DNN-based node behavioral analysis (DNN-NBA) is proposed, and it detects anomalies while minimizing energy consumption. The system does not use a centralized security system, but more so, it decentralizes security tasks to nodes to allow greater scalability and resilience. This DNN-NBA is a deep neural network with four dense layers with 128 neurons, which is capable of examining patterns of device behavior. The proposed model identifies botnet attacks based on network flow-based characteristics that have the ability to capture the behavior of an NB-IoT node. The N-BaIoT dataset was used to train the DNN-NBA model, and it is represented by files with 115 features, with binary classification labels of either benign or transmission control protocol (TCP) attack. TCP attacks are also divided into Mirai and Bashlite attacks in the multiclass categorization. DNN-NBA is energy efficient, relying on lightweight optimization such as Principal Component Analysis (PCA) and extreme gradient boosting (XGBoost) to implement embedded NB-IoT devices. The concept of adaptive learning applied in this case reduces unnecessary computations and enhances energy efficiency. The suggested approach is confirmed by simulations and demonstrates better attack detection capabilities with less energy use than conventional security measures.</p>

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A Novel Distributed Narrow Band-Internet of Things (NB-IoT) Security Solution with Minimal Energy Conservation Using Deep Neural Network-Based Node Behavioral Analysis

  • 期刊论文

  • 发表日期：

    2026年6月18日

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  • Reshma R,
  • Mohana Sundaram N,
  • Santhosh R

摘要

NB-IoT is a form of network, which is designed to link smart devices that do not require substantial power or transmit high data, making it suitable for things such as sensors and trackers. The issue of security is quite a concern because of the numerous interconnected devices and possible attacks. In this paper, a distributed security architecture utilizing DNN-based node behavioral analysis (DNN-NBA) is proposed, and it detects anomalies while minimizing energy consumption. The system does not use a centralized security system, but more so, it decentralizes security tasks to nodes to allow greater scalability and resilience. This DNN-NBA is a deep neural network with four dense layers with 128 neurons, which is capable of examining patterns of device behavior. The proposed model identifies botnet attacks based on network flow-based characteristics that have the ability to capture the behavior of an NB-IoT node. The N-BaIoT dataset was used to train the DNN-NBA model, and it is represented by files with 115 features, with binary classification labels of either benign or transmission control protocol (TCP) attack. TCP attacks are also divided into Mirai and Bashlite attacks in the multiclass categorization. DNN-NBA is energy efficient, relying on lightweight optimization such as Principal Component Analysis (PCA) and extreme gradient boosting (XGBoost) to implement embedded NB-IoT devices. The concept of adaptive learning applied in this case reduces unnecessary computations and enhances energy efficiency. The suggested approach is confirmed by simulations and demonstrates better attack detection capabilities with less energy use than conventional security measures.