scholarly journals USE OF MACHINE LEARNING IN CYBER SECURITY

2021 ◽  
Vol 12 (4) ◽  
pp. 132-142
Author(s):  
Yevhen Ivanichenko ◽  
Mylana Sablina ◽  
Kateryna Kravchuk
Keyword(s):  
Machine Learning ◽  
Information Security ◽  
Cyber Security ◽  
Effective Means ◽  
Learning Technologies ◽  
Cyber Attack ◽  
Security Systems ◽  
Technical Literature ◽  
Artificial Neural ◽  
Self Learning

The urgency of the topic is the integration of machine learning technologies into cybersecurity systems. After getting acquainted with the technical literature, the main technologies of machine learning that are implemented in the organization of cybersecurity were formulated. Acquainted with the main type of artificial neural network used in the prevention and detection of cyber threats and found that the main to consider the general application of machine learning technologies are artificial neural networks based on a multilayer perceptron with inverse error propagation. It is proposed to use indicators of compromise cyberattacks as initial information for automatic machine learning systems. Emphasis is placed on the main types of data that can be used by surveillance subsystems for information security and cybersecurity to perform tasks and prevent, classify and predict cybersecurity events. According to the results of the analysis, the main problem areas for their implementation in information security systems are identified. The problem of using machine learning (ML) in cybersecurity is difficult to solve, because advances in this area open up many opportunities, from which it is difficult to choose effective means of implementation and decision-making. In addition, this technology can also be used by hackers to create a cyber attack. The purpose of the study is to implement machine learning in information security and cybersecurity technology, and to depict a model based on self-learning

scholarly journals Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme environments – cyber risk in the colonisation of Mars

2021 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Kevin Page ◽  
Max Van Kleek ◽  
Omar Santos ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Cyber Security ◽  
Extreme Environments ◽  
Learning Technologies ◽  
Cyber Attacks ◽  
Edge Computing ◽  
Mathematical Formulas ◽  
Cyber Risk

AbstractMultiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

scholarly journals Impacted Cyber Attacks Assessment in Wide Range of Big Data Security Systems

2019 ◽  
Vol 8 (3S) ◽  
pp. 625-629
Keyword(s):  
Big Data ◽  
Cyber Security ◽  
Cyber Attacks ◽  
Cyber Attack ◽  
Security Systems ◽  
Computing Systems ◽  
Wide Range ◽  
Software Platforms ◽  
The Cost ◽  
And Storage

The technological advancements in image storage, data processing, and signal analysis of Big Data include (a) the fastly degrade the cost of storage and CPU power in recent arena; the flexibility and cost-effectiveness of data operating platforms and cloud computing systems for flexible computation and storage; and (c) the development of new frameworks , which allow users to take advantage of these divided computing systems storing large amount of data which is almost flexible parallel processing. The proposed survey work focused on discussing the various impacted cyber-attack critics available in industry and the trending algorithms available for cyber security etc. Big data in IoT clouds handling and software platforms which allow the malware enter into the working systems are analyzed, reliable methods to avoid the miscellaneous malwares are clearly depicted here.

Role of Cyber Security in Today's Scenario

2017 ◽  
pp. 177-191 ◽  
Author(s):  
Manju Khari ◽  
Gulshan Shrivastava ◽  
Sana Gupta ◽  
Rashmi Gupta
Keyword(s):  
Information Security ◽  
Computer Security ◽  
Cyber Security ◽  
Cyber Attacks ◽  
Cyber Attack ◽  
Individual Property ◽  
Security Breaches ◽  
Information Tools ◽  
History Of ◽  
Security Standards

Cyber Security is generally used as substitute with the terms Information Security and Computer Security. This work involves an introduction to the Cyber Security and history of Cyber Security is also discussed. This also includes Cyber Security that goes beyond the limits of the traditional information security to involve not only the security of information tools but also the other assets, involving the person's own confidential information. In computer security or information security, relation to the human is basically to relate their duty(s) in the security process. In Cyber security, the factor has an added dimension, referring humans as the targets for the cyber-attacks or even becoming the part of the cyber-attack unknowingly. This also involves the details about the cybercriminals and cyber risks going ahead with the classification of the Cybercrimes which is against individual, property, organisation and society. Impacts of security breaches are also discussed. Countermeasures for computer security are discussed along with the Cyber security standards, services, products, consultancy services, governance and strategies. Risk management with the security architecture has also been discussed. Other section involves the regulation and certification controls; recovery and continuity plans and Cyber security skills.

Role of Cyber Security in Today's Scenario

2018 ◽  
pp. 1-15 ◽  
Author(s):  
Manju Khari ◽  
Gulshan Shrivastava ◽  
Sana Gupta ◽  
Rashmi Gupta
Keyword(s):  
Information Security ◽  
Computer Security ◽  
Cyber Security ◽  
Cyber Attacks ◽  
Cyber Attack ◽  
Individual Property ◽  
Security Breaches ◽  
Information Tools ◽  
History Of ◽  
Security Standards

Cyber Security is generally used as substitute with the terms Information Security and Computer Security. This work involves an introduction to the Cyber Security and history of Cyber Security is also discussed. This also includes Cyber Security that goes beyond the limits of the traditional information security to involve not only the security of information tools but also the other assets, involving the person's own confidential information. In computer security or information security, relation to the human is basically to relate their duty(s) in the security process. In Cyber security, the factor has an added dimension, referring humans as the targets for the cyber-attacks or even becoming the part of the cyber-attack unknowingly. This also involves the details about the cybercriminals and cyber risks going ahead with the classification of the Cybercrimes which is against individual, property, organisation and society. Impacts of security breaches are also discussed. Countermeasures for computer security are discussed along with the Cyber security standards, services, products, consultancy services, governance and strategies. Risk management with the security architecture has also been discussed. Other section involves the regulation and certification controls; recovery and continuity plans and Cyber security skills.

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 Artificial Intelligence, Machine Learning and Real-time Probabilistic Data for Cyber Risk (Super) -forecasting: Red Teaming the Connected World (RETCON)

2020 ◽  
Author(s):  
Petar Radanliev
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Cyber Security ◽  
Learning Technologies ◽  
Edge Computing ◽  
Engine Design ◽  
Mathematical Formulas ◽  
Systematic Understanding ◽  
Cyber Risk

This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

Cyber Secure Man-in-the-Middle Attack Intrusion Detection Using Machine Learning Algorithms

2020 ◽  
pp. 291-316 ◽  
Author(s):  
Jayapandian Natarajan
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Learning Algorithm ◽  
Modern Technology ◽  
Network Communication ◽  
Machine Learning Algorithm ◽  
Cyber Attack ◽  
Man In The Middle ◽  
Basic Network ◽  
Middle Man

The main objective of this chapter is to enhance security system in network communication by using machine learning algorithm. Cyber security network attack issues and possible machine learning solutions are also elaborated. The basic network communication component and working principle are also addressed. Cyber security and data analytics are two major pillars in modern technology. Data attackers try to attack network data in the name of man-in-the-middle attack. Machine learning algorithm is providing numerous solutions for this cyber-attack. Application of machine learning algorithm is also discussed in this chapter. The proposed method is to solve man-in-the-middle attack problem by using reinforcement machine learning algorithm. The reinforcement learning is to create virtual agent that should predict cyber-attack based on previous history. This proposed solution is to avoid future cyber middle man attack in network transmission.

scholarly journals Machine Learning-Based Detection for Cyber Security Attacks on Connected and Autonomous Vehicles

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1311
Author(s):  
Qiyi He ◽  
Xiaolin Meng ◽  
Rong Qu ◽  
Ruijie Xi
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Cyber Security ◽  
Unified Modeling Language ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Cyber Attack ◽  
Tree Model ◽  
Data Set

Connected and Autonomous Vehicle (CAV)-related initiatives have become some of the fastest expanding in recent years, and have started to affect the daily lives of people. More and more companies and research organizations have announced their initiatives, and some have started CAV road trials. Governments around the world have also introduced policies to support and accelerate the deployments of CAVs. Along these, issues such as CAV cyber security have become predominant, forming an essential part of the complications of CAV deployment. There is, however, no universally agreed upon or recognized framework for CAV cyber security. In this paper, following the UK CAV cyber security principles, we propose a UML (Unified Modeling Language)-based CAV cyber security framework, and based on which we classify the potential vulnerabilities of CAV systems. With this framework, a new CAV communication cyber-attack data set (named CAV-KDD) is generated based on the widely tested benchmark data set KDD99. This data set focuses on the communication-based CAV cyber-attacks. Two classification models are developed, using two machine learning algorithms, namely Decision Tree and Naive Bayes, based on the CAV-KDD training data set. The accuracy, precision and runtime of these two models when identifying each type of communication-based attacks are compared and analysed. It is found that the Decision Tree model requires a shorter runtime, and is more appropriate for CAV communication attack detection.

scholarly journals Toward to Information Security of AI-Enhanced Weapons

2021 ◽  
pp. 5-11
Author(s):  
Vadim Gribunin ◽  
◽  
Sergey Kondakov ◽  
Keyword(s):  
Machine Learning ◽  
Information Security ◽  
System Analysis ◽  
Learning Technologies ◽  
Point Of View ◽  
Learning Systems ◽  
Security Assessment ◽  
Military Equipment ◽  
The Us ◽  
Article Analysis

Purpose of the article: Analysis of intellectualized weapons using machine learning from the point of view of information security. Development of proposals for the deployment of work in the field of information security in similar products. Research method: System analysis of machine learning systems as objects of protection. Determination on the basis of the analysis of rational priority directions for improving these systems in terms of ensuring information security. Obtained result: New threats to information security arising from the use of weapons and military equipment with elements of artificial intelligence are presented. Machine learning systems are considered by the authors as an object of protection, which made it possible to determine the protected assets of such systems, their vulnerabilities, threats and possible attacks on them. The article analyzes the measures to neutralize the identified threats based on the taxonomy proposed by the US National Institute of Standards and Technology. The insufficiency of the existing regulatory methodological framework in the field of information protection to ensure the security of machine learning systems has been determined. An approach is proposed that should be used in the development and security assessment of systems using machine learning. Proposals for the deployment of work in the field of ensuring the security of intelligent weapons using machine learning technologies are presented.

scholarly journals Detection of Cyber Attack in Networks using Machine Learning Techniques

2021 ◽  
Vol 9 (VII) ◽  
pp. 2401-2404
Author(s):  
Meghana M
Keyword(s):  
Machine Learning ◽  
Information Security ◽  
Intrusion Detection ◽  
Denial Of Service ◽  
Intrusion Detection Systems ◽  
Machine Learning Techniques ◽  
Brute Force ◽  
Cyber Attack ◽  
Detection Systems ◽  
Learning Techniques

The use of recent innovations provides unimaginable blessings to individuals, organizations, and governments, be that because it might, messes some up against them. for example, the protection of serious information, security of place away data stages, accessibility of knowledge so forth. Digital concern, that created an excellent deal of problems individuals and institutions, has received A level that might undermine open and nation security by totally different gatherings, as an example, criminal association, good individuals and digital activists. the foremost common risk to a network’s security is an intrusion like brute force, denial of service or maybe an infiltration from inside a network. this can be wherever machine learning comes into play. Intrusion Detection Systems (IDS) has been created to take care of a strategic distance from digital assaults.

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