Security Detection Method for Improved DenseNet 1D in the Internet of Things Environment

Abstract

The continuous expansion of the scale of the IoT has led to frequent occurrence of network attack events and posed severe challenges to device operation and data security. To accurately track the behavior of IoT devices in the network and detect any malicious activities that may occur, this study proposes a spatiotemporal feature-based IoT security detection method that combines TPA mechanism, BiLSTM, DenseNet 1D, and CNN. The experiment was validated using two publicly available datasets, UNSW-NB15 and Bot-IoT. The research method could effectively identify various types of attacks (such as user to administrator privilege escalation attacks, remote to local attacks, etc.), alleviate the impact of imbalanced dataset categories, and maintain low online detection latency. In the UNSW-NB15, the precision, recall, and F1 value of the research method reached 97.6%, 96.8%, and 97.2%, while in the Bot-IoT, they reached 95.7%, 94.1%, and 94.9%. When the sample size increased to 1×106, the running time and energy consumption of the research method were only 13.2 s and 0.9 kWh. The research method effectively enhances the ability to identify multiple types of attack traffic, providing a technical solution that balances accuracy and robustness for malicious behavior detection in IoT environments.