2 research outputs found
Terrain Classification using Transfer Learning on Hyperspectral Images: A Comparative study
A Hyperspectral image contains much more number of channels as compared to a
RGB image, hence containing more information about entities within the image.
The convolutional neural network (CNN) and the Multi-Layer Perceptron (MLP)
have been proven to be an effective method of image classification. However,
they suffer from the issues of long training time and requirement of large
amounts of the labeled data, to achieve the expected outcome. These issues
become more complex while dealing with hyperspectral images. To decrease the
training time and reduce the dependence on large labeled dataset, we propose
using the method of transfer learning. The hyperspectral dataset is
preprocessed to a lower dimension using PCA, then deep learning models are
applied to it for the purpose of classification. The features learned by this
model are then used by the transfer learning model to solve a new
classification problem on an unseen dataset. A detailed comparison of CNN and
multiple MLP architectural models is performed, to determine an optimum
architecture that suits best the objective. The results show that the scaling
of layers not always leads to increase in accuracy but often leads to
overfitting, and also an increase in the training time.The training time is
reduced to greater extent by applying the transfer learning approach rather
than just approaching the problem by directly training a new model on large
datasets, without much affecting the accuracy
NFDLM: A Lightweight Network Flow based Deep Learning Model for DDoS Attack Detection in IoT Domains
In the recent years, Distributed Denial of Service (DDoS) attacks on Internet
of Things (IoT) devices have become one of the prime concerns to Internet users
around the world. One of the sources of the attacks on IoT ecosystems are
botnets. Intruders force IoT devices to become unavailable for its legitimate
users by sending large number of messages within a short interval. This study
proposes NFDLM, a lightweight and optimised Artificial Neural Network (ANN)
based Distributed Denial of Services (DDoS) attack detection framework with
mutual correlation as feature selection method which produces a superior result
when compared with Long Short Term Memory (LSTM) and simple ANN. Overall, the
detection performance achieves approximately 99\% accuracy for the detection of
attacks from botnets. In this work, we have designed and compared four
different models where two are based on ANN and the other two are based on LSTM
to detect the attack types of DDoS.Comment: 7 page