Application of Deep Learning in Malware Detection and Classification by Samaneh Mahdavifar скачать в хорошем качестве

Application of Deep Learning in Malware Detection and Classification by Samaneh Mahdavifar

A webinar on “Application of Deep Learning in Malware Detection and Classification” by Samaneh Mahdavifar, a Ph.D. student at the Computer Science Department of the University of New Brunswick and a Researcher at the Canadian Institute for Cybersecurity. During the presentation, the focus is on the application of Deep Learning in malware detection/ classification and several Deep Learning models that have been applied to this cybersecurity area. Outline: An Introduction to Artificial Neural Networks Basic Deep Network Architectures Application of Deep Networks to Malware Detection Challenges and Limitations Concluding Remarks Q&A ------------------------------- Samaneh Mahdavifar's Google Scholar page: https://scholar.google.com/citations?... To learn more about the Canadian Institute for Cybersecurity watch,    • Canadian Institute for Cybersecurity  . #cybersecurityawareness #deeplearning #malwaredetection #Canada #backpropagation #malwareanalysis #Autoencoder #MaliciousSoftware Stay connected with us! Twitter:   / cic_unb   Facebook: https://fb.me/cicunbca LinkedIn:   / canadian_institute_cybersecurity   Blog: https://cyberdailyreport.com/blog Website: https://www.unb.ca/cic/ Canadian Institute for Cybersecurity University of New Brunswick 46 Dineen Drive, Fredericton, NB E3B 9W4 Canada 0:00 Introduction 0:53 What will you learn today? 1:24 Human Brain 3:00 Perceptron . The artificial model of a neuron is called Perceptron 3:59 Perceptron Learning 4:55 Artificial Neural Network 5:12 Feedforward Neural Network 6:16 Get rid of linearity 7:04 Backpropagation 9:24 Gradient Descent with Momentum 12:57 Restricted Boltzman Machine (RBM) 13:36 Stacked Autoencoders (SAE) 13:55 Deep Belief Networks (DBN) 14:18 Convolutional Neural Networks (CNN) 14:58 Recurrent Neural Networks (RNN) 15:32 Malware Detection and Classification 18:09 Why Do We Need Deep Learning for Malware Detection? 19:34 Deep Networks and Malware Detection 21:15 High level vs Low level Features 23:18 Integration of CNN and RNN 24:03 Does API block help? 25:15 Maldozer 27:00 Challenges and Limitations 28:35 Concluding Remarks