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

Описание: 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
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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  .

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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