Machine Learning With Avatar-Based Management of Sleptsov Net-Processor Platform to Improve Cyber Security

2019 ◽  
pp. 139-153 ◽  
Author(s):  
Vardan Mkrttchian ◽  
Leyla Ayvarovna Gamidullaeva ◽  
Sergey Kanarev
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Petri Net ◽  
Cyber Security ◽  
Theoretical Basis ◽  
Network Processor ◽  
Security Applications ◽  
The Future ◽  
Universal Network ◽  
Special Case

The literature review of known sources forming the theoretical basis of calculations on Sleptsova networks and on the basis of authors' developments in machine learning with avatar-based management established the basis for the future solutions to hyper-computations to support cyber security applications. The chapter established that the petri net performed exponentially slower and is a special case of the Sleptsov network. The universal network of Sleptsov is a prototype of the Sleptsov network processor. The authors conclude that machine learning with avatar-based management at the platform of the Sleptsov net-processor is the future solution for cyber security applications in Russia.

Machine Learning and Cyber Security

2020 ◽  
pp. 1034-1042
Author(s):  
Vardan Mkrttchian ◽  
Sergey Kanarev ◽  
Leyla Ayvarovna Gamidullaeva
Keyword(s):  
Machine Learning ◽  
International Economic ◽  
Literature Review ◽  
Cyber Security ◽  
Machine Learning Technique ◽  
Theoretical Perspectives ◽  
Emerging Trends ◽  
Learning Technique ◽  
Universal Network ◽  
Social Importance

Cybersecurity has become an important subject of national, international, economic, and social importance that affects multiple nations. The literature review of known sources is forming theoretical bases of calculations on Sleptsov networks. The universal network of Sleptsov is a prototype of the Sleptsov network processor. The authors in the article research the emerging trends and theoretical perspectives of cyber security development using machine-learning technique with avatar-based management at the platform of Sleptsov net-processor and propose further prospects for development of hyper-computation.

scholarly journals Systematic Literature Review on Machine Learning and Student Performance Prediction: Critical Gaps and Possible Remedies

2021 ◽  
Vol 11 (22) ◽  
pp. 10907
Author(s):  
Boran Sekeroglu ◽  
Rahib Abiyev ◽  
Ahmet Ilhan ◽  
Murat Arslan ◽  
John Bush Idoko
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Student Performance ◽  
Systematic Literature Review ◽  
Performance Prediction ◽  
Global Education ◽  
Future Studies ◽  
The Future ◽  
The Common ◽  
Students Success

Improving the quality, developing and implementing systems that can provide advantages to students, and predicting students’ success during the term, at the end of the term, or in the future are some of the primary aims of education. Due to its unique ability to create relationships and obtain accurate results, artificial intelligence and machine learning are tools used in this field to achieve the expected goals. However, the diversity of studies and the differences in their content create confusion and reduce their ability to pioneer future studies. In this study, we performed a systematic literature review of student performance prediction studies in three different databases between 2010 and 2020. The results are presented as percentages by categorizing them as either model, dataset, validation, evaluation, or aims. The common points and differences in the studies are determined, and critical gaps and possible remedies are presented. The results and identified gaps could be eliminated with standardized evaluation and validation strategies. It is determined that student performance prediction studies should be more frequently focused on deep learning models in the future. Finally, the problems that can be solved using a global dataset created by a global education information consortium, as well as its advantages, are presented.

Machine-Learning-Based White-Hat Worm Launcher in Botnet Defense System

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Petri Net ◽  
Cyber Security ◽  
Large Scale ◽  
Security System ◽  
Divide And Conquer ◽  
Defense System ◽  
Divide And Conquer Algorithm

This article proposes a white-hat worm launcher based on machine learning (ML) adaptable to large-scale IoT network for Botnet Defense System (BDS). BDS is a cyber-security system that uses white-hat worms to exterminate malicious botnets. White-hat worms defend an IoT system against malicious bots, the BDS decides the number of white-hat worms, but there is no discussion on the white-hat worms' deployment in IoT network. Therefore, the authors propose a machine-learning-based launcher to launch the white-hat worms effectively along with a divide and conquer algorithm to deploy the launcher to large-scale IoT networks. Then the authors modeled BDS and the launcher with agent-oriented Petri net and confirmed the effect through the simulation of the PN2 model. The result showed that the proposed launcher can reduce the number of infected devices by about 30-40%.

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.

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 Artificial Intelligence Techniques for Cyber Security Applications

2020 ◽  
pp. 89-94
Author(s):  
Shanqi Pang ◽  
Yongmei Li
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Significant Role ◽  
Cyber Security ◽  
Cyber Defense ◽  
Artificial Intelligence Techniques ◽  
Security Applications ◽  
The Future

Considering the enhancement in technology, criminals have been using cyberspace in order to commit many crimes. Therefore, it should be noted that cybercrimes are exposed to a number of threats and intrusions if not safeguarded well. Human and physical intervention tend not to be very adequate for the protection and tracking of such infrastructure, that is why there should be the establishment of multifaceted cyber defense networks, which are flexible, robust, and adjustable in order sense a massive collection of invasion and creation of real-time choices. Nevertheless, significant number of bio-related computing techniques of AI (artificial intelligence) tend to be increasing hence a significant role is played in detecting and preventing cybercrime. The main aim of this paper is outlining the actual advancement that have been made possible due to the application of AI methods for the fight against cybercrimes, in order to reveal how the methods are efficient in sensing and preventing cyber invasions, also providing a brief overview of the future works.

Literature Review of Recommendation Systems

2021 ◽  
pp. 119-129
Author(s):  
Irene Maria Gironacci
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Literature Review ◽  
Recommender Systems ◽  
Cyber Security ◽  
Recommendation Systems ◽  
Security And Privacy ◽  
Domestic Industry ◽  
The Core ◽  
Machine Learning Methods

Artificial intelligence technologies are currently at the core of many sectors and industries—from cyber security to healthcare—and also have the power to influence the governance of domestic industry, the security and privacy citizens. In particular, the rise of new machine learning methods, such as those used in recommendation systems, provides many opportunities in terms of personalization. Big players like YouTube, Amazon, Netflix, Spotify, and many others are currently using recommendation systems to improve their business. Recommender systems are critical in some industries as they can generate income and provide a way to stand out from competitors. In this chapter, a literature review of recommendation systems is presented, as well as the application of recommendation systems in industry.

scholarly journals Use of Wavelets in Electrocardiogram Research: a Literature Review

2021 ◽  
Vol 9 (1) ◽  
pp. 49
Author(s):  
Darwan Darwan ◽  
Hindayati Mustafidah
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Deep Learning ◽  
Literature Review ◽  
Case Studies ◽  
Critical Aspect ◽  
Identification Methods ◽  
The Future ◽  
Heart Abnormalities ◽  
Electrocardiogram Ecg

Currently the introduction and detection of heart abnormalities using electrocardiogram (ECG) is very much. ECG conducted many research approaches in various methods, one of which is wavelet. This article aims to explain the trends of ECG research using wavelet approach in the last ten years. We reviewed journals with the keyword title "ecg wavelet" and published from 2011 to 2020. Articles classified by the most frequently discussed topics include: datasets, case studies, pre-processing, feature extraction and classification/identification methods. The increase in the number of ECG-related articles in recent years is still growing in new ways and methods. This study is very interesting because only a few researchers focus on researching about it. Several approaches from many researchers are used to obtain the best results, both by using machine learning and deep learning. This article will provide further explanation of the most widely used algorithms against ECG research with wavelet approaches. At the end of this article it is also shown that the critical aspect of ECG research can be done in the future is the use of datasets, as well as the extraction of characteristics and classifications by looking at the level of accuracy.

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