Enhanced SqueezeNet model for detecting IoT-Bot attacks: A comprehensive approach

Abstract

The exponential growth of Internet of Things (abbreviated as IoT) has led to a surge in cyber threats, especially botnet attacks that compromise network security. Although machine learning (abbreviated ML) & deep learning (abbreviated as DL) approaches have shown promise in detecting these attacks, they often struggle with limited accuracy & high computational requirements, making them unsuitable for real-time detection in resource-constrained IoT environments. To overcome these limitations, this research proposes an enhanced detection framework based on an improved SqueezeNet model integrated with a Deep Convolutional Neural Network (abbreviated as DCNN) and an optimized stochastic mixed Lp layer. This model aims to improve detection accuracy while maintaining computational efficiency. Experimental evaluation using a large-scale intrusion detection dataset demonstrates that the proposed model significantly outperforms existing techniques such as Bi-GRU, CNN, PolyNet, and LinkNet, achieving a classification accuracy of 0.97 and a reduced false positive rate of 0.054. The complete research process is outlined below:

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  Data Pre-processing: Min-max normalization is applied to the input dataset to ensure consistent data scaling and enhance model learning performance.
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  Feature Extraction and Classification: The improved SqueezeNet is integrated with DCNN & a stochastic mixed Lp layer to extract meaningful features and classify attacks accurately.
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  Model Evaluation: Performance is validated through accuracy, precision, recall, and false positive rate using a benchmark intrusion detection dataset.