Machine Learning and Deep Learning in Cyber Security for IoT

2020 ◽  
pp. 975-981
Author(s):  
S. Velliangiri ◽  
Kenanya Kumar Kasaraneni
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Cyber Security

A Review on Cyber Security Mechanisms Using Machine and Deep Learning Algorithms

2020 ◽  
pp. 23-41 ◽  
Author(s):  
Thiyagarajan P.
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Information Security ◽  
Cyber Security ◽  
Learning Algorithms ◽  
The Other ◽  
The Internet ◽  
Learning Techniques

Digitalization is the buzz word today by which every walk of our life has been computerized, and it has made our life more sophisticated. On one side, we are enjoying the privilege of digitalization. On the other side, security of our information in the internet is the most concerning element. A variety of security mechanisms, namely cryptography, algorithms which provide access to protected information, and authentication including biometric and steganography, provide security to our information in the Internet. In spite of the above mechanisms, recently artificial intelligence (AI) also contributes towards strengthening information security by providing machine learning and deep learning-based security mechanisms. The artificial intelligence (AI) contribution to cyber security is important as it serves as a provoked reaction and a response to hackers' malicious actions. The purpose of this chapter is to survey recent papers which are contributing to information security by using machine learning and deep learning techniques.

A Review on Cybersecurity based on Machine Learning and Deep Learning Algorithms

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Alan Fuad Jahwar ◽  
◽  
Siddeeq Y. Ameen ◽  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
Cyber Security ◽  
Critical Role ◽  
Network Intrusion Detection ◽  
Main Principle ◽  
Research Topics ◽  
Security Applications ◽  
Network Intrusion

Machin learning (ML) and Deep Learning (DL) technique have been widely applied to areas like image processing and speech recognition so far. Likewise, ML and DL play a critical role in detecting and preventing in the field of cybersecurity. In this review, we focus on recent ML and DL algorithms that have been proposed in cybersecurity, network intrusion detection, malware detection. We also discuss key elements of cybersecurity, the main principle of information security, and the most common methods used to threaten cybersecurity. Finally, concluding remarks are discussed, including the possible research topics that can be taken into consideration to enhance various cyber security applications using DL and ML algorithms.

scholarly journals Survey on Applications of Deep Learning and Machine Learning Techniques for Cyber Security

2020 ◽  
Vol 14 (16) ◽  
pp. 210
Author(s):  
Mohammed I. Alghamdi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Cyber Security ◽  
Secondary Data ◽  
Extensive Study ◽  
Machine Learning Techniques ◽  
Working Mechanism ◽  
Learning Methods ◽  
Learning Techniques ◽  
Future Direction

<p class="0abstract">The research aimed to conduct an extensive study of machine learning and deep learning methods in cybersecurity. To accomplish the objectives, the research carried out a qualitative study based on secondary data collection to review the available studies and literature. The research has examined three machine learning methods and three deep learning methods to study the most popular techniques used in cybersecurity. During the research, the working mechanism of each method was studied along with their strengths and weaknesses. Also, a comparative discussion has been made to examine the most effective method for cybersecurity. Limitations in the current literature were also identified, and future direction is also given to target and develop the weak areas of machine learning and deep learning methods.<em> </em><strong></strong></p>

Deep learning algorithms for cyber security applications: A survey

2021 ◽  
pp. 1-25
Author(s):  
Guangjun Li ◽  
Preetpal Sharma ◽  
Lei Pan ◽  
Sutharshan Rajasegarar ◽  
Chandan Karmakar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Information Technology ◽  
Big Data ◽  
Deep Learning ◽  
Cyber Security ◽  
High Dimensional ◽  
The Internet ◽  
Security Threats ◽  
Security Issues ◽  
Security Applications

With the development of information technology, thousands of devices are connected to the Internet, various types of data are accessed and transmitted through the network, which pose huge security threats while bringing convenience to people. In order to deal with security issues, many effective solutions have been given based on traditional machine learning. However, due to the characteristics of big data in cyber security, there exists a bottleneck for methods of traditional machine learning in improving security. Owning to the advantages of processing big data and high-dimensional data, new solutions for cyber security are provided based on deep learning. In this paper, the applications of deep learning are classified, analyzed and summarized in the field of cyber security, and the applications are compared between deep learning and traditional machine learning in the security field. The challenges and problems faced by deep learning in cyber security are analyzed and presented. The findings illustrate that deep learning has a better effect on some aspects of cyber security and should be considered as the first option.

scholarly journals Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey

2019 ◽  
Vol 9 (20) ◽  
pp. 4396 ◽  
Author(s):  
Hongyu Liu ◽  
Bo Lang
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Cyber Security ◽  
Detection System ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
Detection Accuracy ◽  
Learning Methods ◽  
Machine Learning Methods

Networks play important roles in modern life, and cyber security has become a vital research area. An intrusion detection system (IDS) which is an important cyber security technique, monitors the state of software and hardware running in the network. Despite decades of development, existing IDSs still face challenges in improving the detection accuracy, reducing the false alarm rate and detecting unknown attacks. To solve the above problems, many researchers have focused on developing IDSs that capitalize on machine learning methods. Machine learning methods can automatically discover the essential differences between normal data and abnormal data with high accuracy. In addition, machine learning methods have strong generalizability, so they are also able to detect unknown attacks. Deep learning is a branch of machine learning, whose performance is remarkable and has become a research hotspot. This survey proposes a taxonomy of IDS that takes data objects as the main dimension to classify and summarize machine learning-based and deep learning-based IDS literature. We believe that this type of taxonomy framework is fit for cyber security researchers. The survey first clarifies the concept and taxonomy of IDSs. Then, the machine learning algorithms frequently used in IDSs, metrics, and benchmark datasets are introduced. Next, combined with the representative literature, we take the proposed taxonomic system as a baseline and explain how to solve key IDS issues with machine learning and deep learning techniques. Finally, challenges and future developments are discussed by reviewing recent representative studies.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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