Malware Detection Using Optimized Activation-Based Deep Belief Network: An Application on Internet of Things

Sagar, G. Vidhya

doi:10.1142/s0219649219500424

Cited by 3 publications

(1 citation statement)

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“…As machine learning methods, such as support vector machine (SVM), extreme learning machine (ELM), neural network (NN), have shown good achievements on classification tasks, there has been a surge of interest in machine learning methods on edge malware detection in recent years. In [29], Sagar developed a three-stage malware detection model to improve detection performance. Term frequency-inverse document frequency (TF-IDF) and information gain (IG) features were extracted in the first stage, and then principal component analysis (PCA) technique was brought in for feature extraction.…”

Section: Machine Learning Methods On Edge Malware Detection and Categmentioning

confidence: 99%

Categorizing Malware via A Word2Vec-based Temporal Convolutional Network Scheme

et al. 2020

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As edge computing paradigm achieves great popularity in recent years, there remain some technical challenges that must be addressed to guarantee smart device security in Internet of Things (IoT) environment. Generally, smart devices transmit individual data across the IoT for various purposes nowadays, and it will cause losses and impose a huge threat to users since malware may steal and damage these data. To improve malware detection performance on IoT smart devices, we conduct a malware categorization analysis based on the Kaggle competition of Microsoft Malware Classification Challenge (BIG 2015) dataset in this article. Practically speaking, motivated by temporal convolutional network (TCN) structure, we propose a malware categorization scheme mainly using Word2Vec pre-trained model. Considering that the popular one-hot encoding converts input names from malicious files to high-dimensional vectors since each name is represented as one dimension in one-hot vector space, more compact vectors with fewer dimensions are obtained through the use of Word2Vec pre-training strategy, and then it can lead to fewer parameters and stronger malware feature representation. Moreover, compared with long short-term memory (LSTM), TCN demonstrates better performance with longer effective memory and faster training speed in sequence modeling tasks. The experimental comparisons on this malware dataset reveal better categorization performance with less memory usage and training time. Especially, through the performance comparison between our scheme and the state-of-the-art Word2Vec-based LSTM approach, our scheme shows approximately 1.3% higher predicted accuracy than the latter on this malware categorization task. Additionally, it also demonstrates that our scheme reduces about 90 thousand parameters and more than 1 hour on the model training time in this comparison.

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Section: Machine Learning Methods On Edge Malware Detection and Categmentioning

confidence: 99%