Classifying malware using convolutional gated neural network

Kim, Chang Hoon; Kabanga, Espoir; Kang, Sin-Jae

doi:10.23919/icact.2018.8323639

Cited by 3 publications

(1 citation statement)

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“…system used a couple of different deep neural networks, the Convolutional Neural Network and the Recurrent Neural Network. Proposed classification malware [2] using convolutional gated neural networks, the system runs malware or mischievous software, is a significant threat to the IT community. In this paper, the author proposed a convolutional gated RNN design that can classify malware into their respective families.…”

Section: Literature Reviewmentioning

confidence: 99%

Micro Clustering Methodology for Document Objects using Deep Learning Techniques

Mulla,

Patil

2019

IJEAT

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Large data clustering and classification is a very challenging task in data mining. Various machine learning and deep learning systems have been proposed by many researchers on a different dataset. Data volume, data size and structure of data may affect the time complexity of the system. This paper described a new document object classification approach using deep learning (DL) and proposed a recurrent neural network (RNN) for classification with a micro-clustering approach.TF-IDF and a density-based approach are used to store the best features. The plane work used supervised learning method and it extracts features set called as BK of the desired classes. once the training part completed then proceeds to figure out the particular test instances with the help of the planned classification algorithm. Recurrent Neural Network categorized the particular test object according to their weights. The system can able to work on heterogeneous data set and generate the micro-clusters according to classified results. The system also carried out experimental analysis with classical machine learning algorithms. The proposed algorithm shows higher accuracy than the existing density-based approach on different data sets.

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Section: Literature Reviewmentioning

confidence: 99%