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.

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.

A Survey of Big Data Analytics Using Machine Learning Algorithms

2018 ◽  
pp. 95-123 ◽  
Author(s):  
Usha Moorthy ◽  
Usha Devi Gandhi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Information Management ◽  
Big Data Analytics ◽  
High Volume ◽  
Information Management System ◽  
Research Trends ◽  
Machine Learning Algorithms ◽  
Security Issues

Big data is information management system through the integration of various traditional data techniques. Big data usually contains high volume of personal and authenticated information which makes privacy as a major concern. To provide security and effective processing of collected data various techniques are evolved. Machine Learning (ML) is considered as one of the data technology which handles one of the central and hidden parts of collected data. Same like ML algorithm Deep Learning (DL) algorithm learn program automatically from the data it is considered to enhance the performance and security of the collected massive data. This paper reviewed security issues in big data and evaluated the performance of ML and DL in a critical environment. At first, this paper reviewed about the ML and DL algorithm. Next, the study focuses towards issues and challenges of ML and their remedies. Following, the study continues to investigate DL concepts in big data. At last, the study figures out methods adopted in recent research trends and conclude with a future scope.

Internet of Things in Cyber Security Scope

2022 ◽  
pp. 146-187
Author(s):  
Mazoon Hashil Alrubaiei ◽  
Maiya Hamood Al-Saadi ◽  
Hothefa Shaker ◽  
Bara Sharef ◽  
Shahnawaz Khan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cyber Security ◽  
Rapid Evolution ◽  
The Internet ◽  
Future Directions ◽  
Iot Security ◽  
Security Issues ◽  
The World ◽  
New Challenges

IoT represents a technologically bright future where heterogeneously connected devices will be connected to the internet and make intelligent collaborations with other objects to extend the borders of the world with physical entities and virtual components. Despite rapid evolution, this environment is still facing new challenges and security issues that need to be addressed. This chapter will give a comprehensive view of IoT technologies. It will discuss the IoT security scope in detail. Furthermore, a deep analysis of the most recent proposed mechanisms is classified. This study will be a guide for future studies, which direct to three primary leading technologies—machine learning (ML), blockchain, and artificial intelligence (AI)—as intelligent solutions and future directions for IoT security issues.

Security for IoT

2021 ◽  
pp. 117-143
Author(s):  
Anjum Nazir Qureshi Sheikh ◽  
Asha Ambhaikar ◽  
Sunil Kumar
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
The Internet ◽  
Security Threats ◽  
Security Issues ◽  
The Earth ◽  
The World ◽  
Wireless Medium ◽  
Iot Devices ◽  
The Internet Of Things

The internet of things is a versatile technology that helps to connect devices with other devices or humans in any part of the world at any time. Some of the researchers claim that the number of IoT devices around the world will surpass the total population on the earth after a few years. The technology has made life easier, but these comforts are backed up with a lot of security threats. Wireless medium for communication, large amount of data, and device constraints of the IoT devices are some of the factors that increase their vulnerability to security threats. This chapter provides information about the attacks at different layers of IoT architecture. It also mentions the benefits of technologies like blockchain and machine learning that can help to solve the security issues of IoT.

scholarly journals Juice Jacking: Security Issues and Improvements in USB Technology

2022 ◽  
Vol 14 (2) ◽  
pp. 939
Author(s):  
Debabrata Singh ◽  
Anil Kumar Biswal ◽  
Debabrata Samanta ◽  
Dilbag Singh ◽  
Heung-No Lee 
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Security Threats ◽  
Cyber Attack ◽  
Learning Models ◽  
Universal Serial Bus ◽  
Security Issues ◽  
Generation Computer ◽  
Deep Learning Model ◽  
The Web

For a reliable and convenient system, it is essential to build a secure system that will be protected from outer attacks and also serve the purpose of keeping the inner data safe from intruders. A juice jacking is a popular and spreading cyber-attack that allows intruders to get inside the system through the web and theive potential data from the system. For peripheral communications, Universal Serial Bus (USB) is the most commonly used standard in 5G generation computer systems. USB is not only used for communication, but also to charge gadgets. However, the transferal of data between devices using USB is prone to various security threats. It is necessary to maintain the confidentiality and sensitivity of data on the bus line to maintain integrity. Therefore, in this paper, a juice jacking attack is analyzed, using the maximum possible means through which a system can be affected using USB. Ten different malware attacks are used for experimental purposes. Various machine learning and deep learning models are used to predict malware attacks. An extensive experimental analysis reveals that the deep learning model can efficiently recognize the juice jacking attack. Finally, various techniques are discussed that can either prevent or avoid juice jacking attacks.

A Survey of Big Data Analytics Using Machine Learning Algorithms

2022 ◽  
pp. 655-677
Author(s):  
Usha Moorthy ◽  
Usha Devi Gandhi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Information Management ◽  
Big Data Analytics ◽  
High Volume ◽  
Information Management System ◽  
Research Trends ◽  
Machine Learning Algorithms ◽  
Security Issues

Big data is information management system through the integration of various traditional data techniques. Big data usually contains high volume of personal and authenticated information which makes privacy as a major concern. To provide security and effective processing of collected data various techniques are evolved. Machine Learning (ML) is considered as one of the data technology which handles one of the central and hidden parts of collected data. Same like ML algorithm Deep Learning (DL) algorithm learn program automatically from the data it is considered to enhance the performance and security of the collected massive data. This paper reviewed security issues in big data and evaluated the performance of ML and DL in a critical environment. At first, this paper reviewed about the ML and DL algorithm. Next, the study focuses towards issues and challenges of ML and their remedies. Following, the study continues to investigate DL concepts in big data. At last, the study figures out methods adopted in recent research trends and conclude with a future scope.

Advanced Deep Learning Applications in Big Data Analytics

2021 ◽  
pp. 1-28
Author(s):  
Venkatesan Manian ◽  
Vadivel P.
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Internet Of Things ◽  
Data Analytics ◽  
Big Data Analytics ◽  
The Internet ◽  
Infrastructure Development ◽  
Learning Approaches ◽  
The Internet Of Things

This chapter analyzes the Internet of Things (IoT), its history, and its tools in brief. This chapter also explores the contribution of IoT towards the recent development in infrastructure development of nations represented as smart world. This chapter also discuss the contribution of IoT towards big data analytics era. This chapter also briefly introduce the smart bio world and how it is made possible with the internet of things. This chapter also introduces the machine learning approaches and also discusses the contribution of Internet of Thing for this machine learning. This chapter also briefly introduces some tools used for IoT developments.

scholarly journals An Examination of Security and Privacy Challenges in Internet of Things

2020 ◽  
Vol 8 (4S) ◽  
pp. 1-5
Keyword(s):  
Internet Of Things ◽  
Cyber Security ◽  
Security And Privacy ◽  
The Internet ◽  
Security Threats ◽  
Large Network ◽  
Iot Security ◽  
Security Issues ◽  
Wide Range ◽  
The Internet Of Things

The Internet of Things (IoT) has been growing to market from the past several years with great potential. Many several devices have been now available in the market based on IoT, which enables it to connect with your smart phones or with any other kind of smart resources, and then that device is ready to perform smart work via the Internet. With the help of IoT, we are now able to make our devices connect with the internet and then can be operated from anywhere from the geo location as well as it can store and retrieve a large amount of data for better communication between the end-user and the device. IoT also has a wide range of applications that are being used on many platforms. However, this great technology also has to face many problems and among all the problems the main issue arises with its security aspects. The major concern on using IoT security is the hacker wants to enter into the large network system using a particular device as all the devices are connected over the network. Not only this, many other security threats and malware are also a major concern in IoT. So taking these security aspects as a major concern this research paper reviews several security issues and challenges that occur in IoT. As there in every field when it comes to cyber security for any kind of data, we need to follow CIA Security Triangle i.e., Confidentiality, Integrity, and Availability of data. CIA security triangle is the most important concept in terms of security and also must be taken into consideration in the IoT domain. Therefore, considering all these facts and reviewing some of the latest documents as well as researches in the field of IoT, this paper has been based on all the facts related to IoT security issues and its desirable solution which is needed to be done and should follow the security triangle to an extent.

scholarly journals Rage against the machine learning

2020 ◽  
Vol 14 (2 Abr-Jun) ◽  
pp. 06-23
Author(s):  
Arthur Coelho Bezerra ◽  
Marco Antônio de Almeida
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Critical Theory ◽  
The Internet ◽  
Contemporary Society ◽  
Economic Interests ◽  
Learning Techniques ◽  
Theory Of Information ◽  
The 19Th Century

Before being an exaltation to Luddites (the English workers from the 19th century who actually destroyed textile machinery as a form of protest) or to some sort of technophobic movement, the provocative pun contained in the title of this article carries a methodological proposal, in the field of critical theory of information, to build a diagnosis about the algorithmic filtering of information, which reveals itself to be a structural characteristic of the new regime of information that brings challenges to human emancipation. Our analysis starts from the concept of mediation to problematize the belief, widespread in much of contemporary society, that the use of machine learning and deep learning techniques for algorithmic filtering of big data will provide answers and solutions to all our questions and problems. We will argue that the algorithmic mediation of information on the internet, which is responsible for deciding which information we will have access to and which will remain invisible, is operated according to the economic interests of the companies that control the platforms we visit on the internet, acting as obstacle to the prospects of informational diversity and autonomy that are fundamental in free and democratic societies.

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