What question did this study set out to answer?

This study aims to develop a deep learning approach to detect software piracy and malware attacks in IoT environments.

January 1, 2019SHILAP Revista de lepidopterología247 citationsOpen Access

Cyber Security Threats Detection in Internet of Things Using Deep Learning Approach

FUFarhan UllahPrince Mohammad bin Fahd University HNHamad NaeemPrince Mohammad bin Fahd University SJSohail JabbarImam Mohammad ibn Saud Islamic University

Key Points

This study aims to develop a deep learning approach to detect software piracy and malware attacks in IoT environments.
Utilized a TensorFlow deep neural network for identifying pirated software through source code plagiarism detection.
Applied tokenization and weighting feature methods to filter noisy data and emphasize important tokens.
Employed a deep convolutional neural network for detecting malicious infections using color image visualization.
Proposed solution exhibits superior classification performance compared to state-of-the-art methods.
Experimental results indicate effective detection of both software piracy and malware within IoT.
Achieved improved accuracy rates in identifying cybersecurity threats in the IoT network.

Abstract

The IoT (Internet of Things) connect systems, applications, data storage, and services that may be a new gateway for cyber-attacks as they continuously offer services in the organization. Currently, software piracy and malware attacks are high risks to compromise the security of IoT. These threats may steal important information that causes economic and reputational damages. In this paper, we have proposed a combined deep learning approach to detect the pirated software and malware-infected files across the IoT network. The TensorFlow deep neural network is proposed to identify pirated software using source code plagiarism. The tokenization and weighting feature methods are used to filter the noisy data and further, to zoom the importance of each token in terms of source code plagiarism. Then, the deep learning approach is used to detect source code plagiarism. The dataset is collected from Google Code Jam (GCJ) to investigate software piracy. Apart from this, the deep convolutional neural network is used to detect malicious infections in IoT network through color image visualization. The malware samples are obtained from Maling dataset for experimentation. The experimental results indicate that the classification performance of the proposed solution to measure the cybersecurity threats in IoT are better than the state of the art methods.

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