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.

Applications of Machine Learning in Cyber Security Domain

2020 ◽  
pp. 64-82 ◽  
Author(s):  
Sailesh Suryanarayan Iyer ◽  
Sridaran Rajagopal
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cyber Security ◽  
Traditional Society ◽  
Future Trends ◽  
Human Beings ◽  
Online Transactions ◽  
Network Intrusion ◽  
The World ◽  
Applications Of Machine Learning

Knowledge revolution is transforming the globe from traditional society to a technology-driven society. Online transactions have compounded, exposing the world to a new demon called cybercrime. Human beings are being replaced by devices and robots, leading to artificial intelligence. Robotics, image processing, machine vision, and machine learning are changing the lifestyle of citizens. Machine learning contains algorithms which are capable of learning from historical occurrences. This chapter discusses the concept of machine learning, cyber security, cybercrime, and applications of machine learning in cyber security domain. Malware detection and network intrusion are a few areas where machine learning and deep learning can be applied. The authors have also elaborated on the research advancements and challenges in machine learning related to cyber security. The last section of this chapter lists the future trends and directions in machine learning and 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.

scholarly journals Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions

2021 ◽  
Vol 18 (3) ◽  
pp. 1117
Author(s):  
Tarik Alafif ◽  
Abdul Muneeim Tehame ◽  
Saleh Bajaba ◽  
Ahmed Barnawi ◽  
Saad Zia
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
State Of The Art ◽  
Diagnosis And Treatment ◽  
Future Directions ◽  
Health Community ◽  
The World ◽  
The Status ◽  
And Performance

With many successful stories, machine learning (ML) and deep learning (DL) have been widely used in our everyday lives in a number of ways. They have also been instrumental in tackling the outbreak of Coronavirus (COVID-19), which has been happening around the world. The SARS-CoV-2 virus-induced COVID-19 epidemic has spread rapidly across the world, leading to international outbreaks. The COVID-19 fight to curb the spread of the disease involves most states, companies, and scientific research institutions. In this research, we look at the Artificial Intelligence (AI)-based ML and DL methods for COVID-19 diagnosis and treatment. Furthermore, in the battle against COVID-19, we summarize the AI-based ML and DL methods and the available datasets, tools, and performance. This survey offers a detailed overview of the existing state-of-the-art methodologies for ML and DL researchers and the wider health community with descriptions of how ML and DL and data can improve the status of COVID-19, and more studies in order to avoid the outbreak of COVID-19. Details of challenges and future directions are also provided.

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.

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.

USA's View on World Cyber Security Issues

2011 ◽  
pp. 446-452
Author(s):  
Norman Schneidewind
Keyword(s):  
Cyber Security ◽  
International Security ◽  
The Internet ◽  
Cyber Attack ◽  
Security Analysts ◽  
Security Issues ◽  
The World ◽  
Critical Issues ◽  
Source Of Information

There is little evidence that the world is more secure from a major cyber attack than in 2000 because attacks on the Internet go on unabated . In addition to calling for new legislation and oversight, this chapter serves as a source of information about cyber security that domestic and international security analysts can use as a resource for understanding the critical issues and as a guide for preparing for hearings and legislative initiatives.

scholarly journals A State of the Art Survey of Machine Learning Algorithms for IoT Security

2021 ◽  
pp. 12-34
Author(s):  
Alan Fuad Jahwar ◽  
Subhi R. M. Zeebaree
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Smart Cities ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Security Measures ◽  
Iot Security ◽  
Security Issues ◽  
The Internet Of Things

The Internet of Things (IoT) is a paradigm shift that enables billions of devices to connect to the Internet. The IoT's diverse application domains, including smart cities, smart homes, and e-health, have created new challenges, chief among them security threats. To accommodate the current networking model, traditional security measures such as firewalls and Intrusion Detection Systems (IDS) must be modified. Additionally, the Internet of Things and Cloud Computing complement one another, frequently used interchangeably when discussing technical services and collaborating to provide a more comprehensive IoT service. In this review, we focus on recent Machine Learning (ML) and Deep Learning (DL) algorithms proposed in IoT security, which can be used to address various security issues. This paper systematically reviews the architecture of IoT applications, the security aspect of IoT, service models of cloud computing, and cloud deployment models. Finally, we discuss the latest ML and DL strategies for solving various security issues in IoT networks.

scholarly journals Home Automation and RFID-Based Internet of Things Security: Challenges and Issues

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Haram Fatima ◽  
Habib Ullah Khan ◽  
Shahzad Akbar
Keyword(s):  
Internet Of Things ◽  
Radio Frequency Identification ◽  
Cyber Security ◽  
The Internet ◽  
Home Automation ◽  
Iot Security ◽  
Security Issues ◽  
Network Layers ◽  
Security Research ◽  
Educational Applications

Internet of Things (IoT) protection refers to the software field related to securing the Internet of Things and associated linked devices and systems. The IoT is a system of interconnected computers, sensors, actuators, or people on the World Wide Web (WWW). All these different devices have a unique identity in the IoT and must convey data across the network automatically. If computers are not adequately secured, allowing them to connect to the Internet exposes them to a range of serious vulnerabilities. Because the consequences of IoT failures are severe, it is necessary to observe and analyze security issues related to IoT. The prime goal of IoT security is to protect personal safety, while also guaranteeing and ensuring accessibility. In the context of IoT technology, the present study conducts a systematic literature review that analyzes the security problems associated with commercial and educational applications of home automation and details the technical possibilities of IoT with respect to the network layer. In this systematic review, we discuss how current contexts result in the inability of designers of IoT devices to enhance their cyber-security initiatives. Typically, application developers are responsible for training themselves to understand recent security advancements. As a result, active participation on the ridge scale with passive improvement can be achieved. A comparative analysis of the literature was conducted. The main objective of this research is to provide an overview of current IoT security research in home automation, particularly those using authentication methods in different devices, and related technologies in radio frequency identification (RFID) on network layers. IoT security issues are addressed, and various security problems in each layer are analyzed. We describe cross-layer heterogeneous integration as a domain of IoT and demonstrate how it can provide some promising solutions.

Machine Learning Techniques to Mitigate Security Attacks in IoT

2020 ◽  
pp. 65-93
Author(s):  
Kavi Priya S. ◽  
Vignesh Saravanan K. ◽  
Vijayalakshmi K.
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Information Exchange ◽  
Machine Learning Techniques ◽  
Smart Devices ◽  
The Internet ◽  
Sensitive Data ◽  
Iot Security ◽  
Security Issues ◽  
Learning Techniques

Evolving technologies involve numerous IoT-enabled smart devices that are connected 24-7 to the internet. Existing surveys propose there are 6 billion devices on the internet and it will increase to 20 billion devices within a few years. Energy conservation, capacity, and computational speed plays an essential part in these smart devices, and they are vulnerable to a wide range of security attack challenges. Major concerns still lurk around the IoT ecosystem due to security threats. Major IoT security concerns are Denial of service(DoS), Sensitive Data Exposure, Unauthorized Device Access, etc. The main motivation of this chapter is to brief all the security issues existing in the internet of things (IoT) along with an analysis of the privacy issues. The chapter mainly focuses on the security loopholes arising from the information exchange technologies used in internet of things and discusses IoT security solutions based on machine learning techniques including supervised learning, unsupervised learning, and reinforcement learning.

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.

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