Phd Assistance Lab
Smart Intelligent Intrusion Detection Systems (IDS) How to build a Deep learning Networks based IDS
Phd Assistance Lab
EXECUTIVE SUMMARY
Phd Assistance Lab
2 | How to build a Deep learning Networks based IDS
1
2
3
Artificial Intelligence is a recent trend in machine learning, and Deep Learning is a major leap forward in terms of technology
Deep Learning performs complicated computations, therey making it suitable for large datasets and big data applications.
Large Image datasets can make use of Deep Learning effectively. However, it can also be useful for IDS applications.
Engineering and Technology
Phd Assistance Lab
3 | How to build a Deep learning Networks based IDS
Engineering and Technology
Phd Assistance Lab
INTRODUCTION
Simple identification of an object in an image is very simple for even a small kid. While this might be easy for humans, this was extremely difficult for computers. A specific background can be identified through GIS, but identifying specific objects like birds, trees, and animals seemed a tedious task for the computers. While machine learning has been available for some time, it has been limited to certain criteria and could not be utilized for complicated predictions and identifications.The introduction of Deep learning has been instrumental in advanced capabilities and has
4 | How to build a Deep learning Networks based IDS
taken the field of machine learning a leap forward. It is a type of Artificial Intelligence, where it imitates the process of the human brain for carrying out the computations and generating patterns for making decisions. Hence, it is also known as Deep Neural network. It is an unsupervised learning system where it can learn by creating feedback loops. It is different from conventional neural networks with respect to the number of layers. DNN contains hidden layers that are used for computations.
Engineering and Technology
Phd Assistance Lab
SMART INTELLIGENT
Although it requires computational power, it is not necessary to have a large amount of data with respect to textual data. However, when images are considered, it requires lots of data to the tune of big data. It is highly difficult for a human being to go through this data, and it will take years just to understand and retrieve the data. The introduction of Deep Neural Networks has largely improved the process and has improved the speed of training and classification. Large corporations now use this to collect user data and managing them. It has found itself in a wide range of applications right
reduced features each form a hidden layer where they are mapped through each of the individual features of instances. Each instance acts as an Input and then passes through the various hidden layers and finally results in two outputs, where it will be classified into attacks or normal traffic. In some datasets, multiclass results are available, and hence each attack is represented by an output. Hence, the number of outputs is not two but depends on the number of attacks .When the training dataset is given to the DNN, the artificial neuronsget assigned in such a way that it can solve related solutions.Hence when unlabelled attacks are sent to the classifier, it
from image processing to detecting bank frauds over the internet, Each layer in the neural network builds on the previous layers with additional data related to the considered factors. Each factor may have thousands of data instances, making it extremely difficult for human computations. The Deep network may generate a pattern for each instance. From the different layers, the final layers put these together and give out the final instances. If an Intrusion Detection System (IDS) is taken as an example or implementing the deep network, the features of each attack will
involve a hidden layer. See Also: How to Best Select the dataset and sizes for building IDS ModelsIn the previous article, the datasets are discussed, and it is known that there is a presence of unnecessary features in the datasets. These can be removed using feature selection algorithms. With feature selection, unnecessary defunct features will be removed. These features not only remain unnecessary for the classification and training process, rather they affect the performance by reducing the accuracy and increase the classification time. These
5 | How to build a Deep learning Networks based IDS
can be able to identify the attacks with high precision. Due to the higher number of hidden layers, they are able to identify the attacks with higher accuracy.
Engineering and Technology
SUMMARY
Phd Assistance Lab
Neural networks are sometimes not preferred since they start blankly without any knowledge and then pass through to get accurate models. Since they do not make any assumptions on the functional correlation between input and output, they are considered to be inefficient. However, this is only applicable for very small data. This issue will not be faced in large datasets; hence, this algorithm can be considered whenever large datasets are considered.
6 | How to build a Deep learning Networks based IDS
Engineering and Technology
Phd Assistance Lab
REFERENCES
ABOUT THE DEPARTMENT
1.
Abusitta, A., Bellaiche, M., Dagenais, M., & Halabi, T. (2019). A deep learning approach for the proactive multi-cloud cooperative intrusion detection system. Future Generation Computer Systems, 98, 308–318. https://doi.org/10.1016/j.future.2019.03.043.
Engineering and Technology Lab at Phd Assistance is involved in exploring novel research areas for the challenges faced by today’s technology-oriented business or organisation, the market, the economy, finance and sales. The research team explores and identifies troubling questions that exist in scholarly literature, in theory, or in practices that needs deliberate investigation.
2.
Chiba, Z., Abghour, N., Moussaid, K., El Omri, A., & Rida, M. (2019). Intelligent approach to building a Deep Neural Network-based IDS for cloud environment using a combination of machine learn ing algorithms. Computers&Security, 86, 291–317. https://doi.org/10.1016/j.cose.2019.06.013
ABOUT US
3.
Chien, J.-T. (2019). Deep Neural Network. In Source Separation and Machine Learning (pp. 259–320). Elsevier. https://doi.org/10.1016/B978-0-12-8045664.00019-X
4.
Hodges, C., Bennamoun, M., & Rahmani, H. (2019). Single image dehazing using deep neural networks. Pattern Recognition Letters, 128, 70–77. https://doi. org/10.1016/j.patrec.2019.08.013
5.
Li, B., & Pi, D. (2019). Learning deep neural net works for node classification. Expert Systems with Applications, 137, 324–334. https://doi.org/10.1016/j. eswa.2019.07.006
6.
Zhou, D.-X. (2019). The universality of deep convolutional neural networks.Applied and Com putational Harmonic Analysis. https://doi. org/10.1016/j.acha.2019.06.004
Format Type : E-Book © 2019-2020 All Rights Reserved, No part of this document should be modified/used without prior consent UK: 10 Park Place, Manchester M4 4EY, UK: +44-1143520021 Email:info@phdassistance.com, Web:www.phdassistance.com.
Phd Assistance, is world’s reputed academic guidance provider for have guided more than 4,500 Ph.D. scholars and 10,500 Masters Students across the globe. We support students, research scholars, entrepreneurs, and professionals from various organizations in providing consistently high-quality writing and data analytical services every time. We value every client and make sure their requirements are identified and understood by our specialized professionals and analysts, enriched in experience to deliver technically sound output withinthe requested timeframe. Writers at Tutors India are best referred as ‘Researchers’ since every topic they handle unique and challenging. We specialize in handling text and data, i.e., content development and Statistical analysis where the latest statistical applications are exhausted by our expert analysts for determining the outcome of the data analysed. Qualified and experienced researchers including Ph.D. holders, statisticians, and research analysts offer cutting edge research consulting and writing services to meet your business information or academic project requirement. Our expertise has passion towards research and personal assistance as we work closely with you for a very professional and quality output within your stipulated time frame. Our services cover vast areas, and we also support either part or entire research paper/service as per your requirement at competitive prices.