A Novel Approach for Detecting Online Malware Detection LSTMRNN and GRU Based Recurrent Neural Network in Cloud Environment

Prabhavathy, M.; Maheswari, S. Uma; Saveeth, R.; Rubini, Stéphane; Surendiran, B.

doi:10.1007/978-981-19-1122-4_1

Cited by 2 publications

(1 citation statement)

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“…These models call for a deep understanding of kernels, layers, and parameters for fine-tuning. In more recent work using GRU for malware identification [ 73 ], the behavioral characteristics of CPU, disk consumption, and memory of programs running in cloud-based platforms without restriction were collected to categorize malicious apps. The primary reason for incorporating GRU into our proposed ensemble model was its shorter training time, which reduced the overall training time of the ensemble model.…”

Section: Methodsmentioning

confidence: 99%

Transfer Learning for Image-Based Malware Detection for IoT

Panda

Kumar

Marappan

et al. 2023

Sensors

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The tremendous growth in online activity and the Internet of Things (IoT) led to an increase in cyberattacks. Malware infiltrated at least one device in almost every household. Various malware detection methods that use shallow or deep IoT techniques were discovered in recent years. Deep learning models with a visualization method are the most commonly and popularly used strategy in most works. This method has the benefit of automatically extracting features, requiring less technical expertise, and using fewer resources during data processing. Training deep learning models that generalize effectively without overfitting is not feasible or appropriate with large datasets and complex architectures. In this paper, a novel ensemble model, Stacked Ensemble—autoencoder, GRU, and MLP or SE-AGM, composed of three light-weight neural network models—autoencoder, GRU, and MLP—that is trained on the 25 essential and encoded extracted features of the benchmark MalImg dataset for classification was proposed. The GRU model was tested for its suitability in malware detection due to its lesser usage in this domain. The proposed model used a concise set of malware features for training and classifying the malware classes, which reduced the time and resource consumption in comparison to other existing models. The novelty lies in the stacked ensemble method where the output of one intermediate model works as input for the next model, thereby refining the features as compared to the general notion of an ensemble approach. Inspiration was drawn from earlier image-based malware detection works and transfer learning ideas. To extract features from the MalImg dataset, a CNN-based transfer learning model that was trained from scratch on domain data was used. Data augmentation was an important step in the image processing stage to investigate its effect on classifying grayscale malware images in the MalImg dataset. SE-AGM outperformed existing approaches on the benchmark MalImg dataset with an average accuracy of 99.43%, demonstrating that our method was on par with or even surpassed them.

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Section: Methodsmentioning

confidence: 99%