DeapSECURE module 4: Deep Learning (Neural Network)

Deep learning is an advanced form of machine learning that uses neural networks, which mimic the neurons in the human brain, to collect data, learn and modify the model as it learns more.

Malicious apps (malware) have become a prevalent tool for compromising mobile devices, stealing personal information, or spying on a user’s activities.

Researchers have leveraged artificial intelligence (AI) / machine learning (ML) to keep up with increasing security challenges.

Large amounts of data are critical to train and validate accurate and effective ML-based cybersecurity techniques.

ML models can be used to distinguish smartphone apps and potentially identify malware.

Deep neural networks basically have linear and nonlinear parts.

Backpropagation is used to adjust the network parameters.

An HPC can be adopted to speed up the training and inference processes.

Keras is an easy-to-use high-level API for building neural networks.

Main parts of a neural network layer: number of hidden neurons, activation function.

Main parts of a neural network model: layers, optimizer, learning rate, loss function, performance metrics.

On KERAS, we can easily build the network by defining the layers and connecting them together.

Neural network models are tuned by tweaking the architecture and tuning the training hyperparameters.

How scripting works by converting the notebook to job scripts

First key point. Brief Answer to questions. (FIXME)

Layer design