Introduction to Deep Learning

Dense Deep Neural Network Architecture for Keystroke Dynamics Authentication in Mobile Phone

“…The performance of the algorithms depend on how well the features are extracted before modeling the classifier. Therefore, inefficient feature extraction may lead to poor classification accuracy [34] unlike deep learning which does not require independent feature extraction as it is done automatically and can work on large data size [35,36]. Finally, a [37] convolutional neural network is applied to create an authentication scheme based on tap sequence and usage behavior of users.…”

“…Deep Learning is an aspect of machine learning which represents multiple hidden layers that can learn on multiple attributes to produce better results [36]. Traditional machine learning algorithms have limitation on processing real data.…”

“…Building a classification model with conventional machine learning algorithms, requiring reasonable amount of human expertise and efficient feature engineering to transform the raw data to desirable features that can be fed into any learning system or classifier. Deep learning permits inputting raw data into the algorithm without being transformed to feature vector or feature engineering as the features are learnt automatically during training the network [36]. Problems are solved in a hierarchical manner in deep learning, where the lower layers depict the basic representation of the problem and the upper layers are created based on the lower layers to build a more complex model.…”

“…Problems are solved in a hierarchical manner in deep learning, where the lower layers depict the basic representation of the problem and the upper layers are created based on the lower layers to build a more complex model. Deep learning is a hierarchical process as each layer in the deep learning network uses the output of the previous layer and the output of the current layer serves as the input to the successive layer to continuously build a complex concept [36]. The number of layers in a network determines the depth of the network.…”

“…Cconventional machine learning only focuses on one or two layers, whereas in deep learning, the network contains at least three or more hidden layers. The unique aspect of deep learning is that the feature layers are automatically learned on the raw data not extracted by human expertise unlike the conventional machine learning [36]. The major advantages of deep learning over traditional machine learning are; automatic feature extraction and efficient handling of massive amount of data as a result, it keeps improving as the data gets larger [36].…”