A deep Recurrent Neural Network based approach for Internet of Things malware threat hunting

By Hamed Haddad Pajouh, Ali Dehghantanha, R. Khayami, Kim-Kwang Raymond Choo. Future generations computer systems, 2018

417 citations recorded by the source corpus.

Abstract Internet of Things (IoT) devices are increasingly deployed in different industries and for different purposes (e.g. sensing/collecting of environmental data in both civilian and military settings). The increasing presence in a broad range of applications, and their increasing computing and processing capabilities make them a valuable attack target, such as malware designed to compromise specific IoT devices. In this paper, we explore the potential of using Recurrent Neural Network (RNN) deep learning in detecting IoT malware. Specifically, our approach uses RNN to analyze ARM-based IoT applications’ execution operation codes (OpCodes). To train our models, we use an IoT application dataset comprising 281 malware and 270 benign ware. Then, we evaluate the trained model using 100 new IoT malware samples (i.e. not previously exposed to the model) with three different Long Short Term Memory (LSTM) configurations. Findings of the 10-fold cross validation analysis show that the second configuration with 2-layer neurons has the highest accuracy (98.18%) in the detection of new malware samples. A comparative summary with other machine learning classifiers also demonstrate that the LSTM approach delivers the best possible outcome.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/c2d2e401140cac58412a792c5147a744ba0ebc37

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Citations in the corpus

Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition (2019, background): “This rapid growth of the Malware market is affecting the security of the computer industry [248-250].”
Machine Learning in IoT Security: Current Solutions and Future Challenges (2019, methodology): “[184] proposed an RNN-based DL approach for malware analysis technique in IoT.”
You Are What You Do: Hunting Stealthy Malware via Data Provenance Analysis (2020, background): “Malware detection has greatly advanced beyond traditional defenses [1], [2] due to innovations such as machine learning based detection [3], [4], [5], [6] and threat intelligence computing [7].”