Comparison of Malware Classification Methods using Convolutional Neural Network based on API Call Stream

A Convolutional Neural Network based on Adaptive Pooling for Classification of Noisy Images

Convolutional neural network is one of the effective methods for classifying images that performs learning using convolutional, pooling and fully-connected layers. All kinds of noise disrupt the operation of this network. Noise images reduce classification accuracy and increase convolutional neural network training time. Noise is an unwanted signal that destroys the original signal. Noise chang...

Malware Characterization Using Windows API Call Sequences

EMG-based wrist gesture recognition using a convolutional neural network

Background: Deep learning has revolutionized artificial intelligence and has transformed many fields. It allows processing high-dimensional data (such as signals or images) without the need for feature engineering. The aim of this research is to develop a deep learning-based system to decode motor intent from electromyogram (EMG) signals. Methods: A myoelectric system based on convolutional ne...

Convolutional Neural Networks for Malware Classification

According to AV vendors malicious software has been growing exponentially last years. One of the main reasons for these high volumes is that in order to evade detection, malware authors started using polymorphic and metamorphic techniques. As a result, traditional signature-based approaches to detect malware are being insufficient against new malware and the categorization of malware samples ha...

3D model classification using convolutional neural network

Our goal is to classify 3D models directly using convolutional neural network. Most of existing approaches rely on a set of human-engineered features. We use 3D convolutional neural network to let the network learn the features over 3D space to minimize classification error. We trained and tested over ShapeNet dataset with data augmentation by applying random transformations. We made various vi...