Optimal Machine Learning Algorithms for Cyber Threat Detection

For defining the optimal machine learning algorithm, the decision was not easy for which we shall choose. To help future researchers, we describe in this paper the optimal among the best of the algorithms. We built a synthetic data set and performed the supervised machine learning runs for five different algorithms. For heterogeneity, we identified Random Forest, among others, to be the best algorithm.

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Ensemble Learning Mechanisms for Threat Detection

Machine Learning and Cognitive Science Applications in Cyber Security - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-8100-0.ch010 ◽

2019 ◽

pp. 240-281 ◽

Cited By ~ 4

Author(s):

Rajakumar Arul ◽

Rajalakshmi Shenbaga Moorthy ◽

Ali Kashif Bashir

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Threat Detection ◽

Learning Mechanisms ◽

Learning Mechanism ◽

Telecommunication Systems ◽

Security Standard ◽

Intrusion Detection And Prevention

Technology evolution in the network security space has been through many dramatic changes recently. Enhancements in the field of telecommunication systems invite fruitful security solutions to address various threats that arise due to the exponential growth in the number of users. It's crucial for upgrading the entire infrastructure to safeguard the system from specific threats. So, there is a huge demand for the learning mechanism to realize the behavior of attacks. Recent upcoming technologies like machine learning and deep learning can support in the process of learning the behavior of all types of attacks irrespective of their deployment criteria. In this chapter, the analysis of various machine learning algorithms with respect to a few scenarios that can be adopted for the benefits of improving the security standard of the network. This chapter briefly discusses various know attacks and their classification and how machine learning algorithms can be involved to overcome the popular attacks. Also, various intrusion detection and prevention schemes were discussed in detail.

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Deducing of Optimal Machine Learning Algorithms for Heterogeneity

10.36227/techrxiv.17162147.v1 ◽

2021 ◽

Author(s):

Omar Alfarisi ◽

Zeyar Aung ◽

Mohamed Sassi

Keyword(s):

Machine Learning ◽

Random Forest ◽

Learning Algorithm ◽

Learning Algorithms ◽

Synthetic Data ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Machine Learning Algorithm ◽

Data Set ◽

Optimal Machine

For defining the optimal machine learning algorithm, the decision was not easy for which we shall choose. To help future researchers, we describe in this paper the optimal among the best of the algorithms. We built a synthetic data set and performed the supervised machine learning runs for five different algorithms. For heterogeneity, we identified Random Forest, among others, to be the best algorithm.

Download Full-text

Supplemental Material for One Model to Rule Them All? Using Machine Learning Algorithms to Determine the Number of Factors in Exploratory Factor Analysis

Psychological Methods ◽

10.1037/met0000262.supp ◽

2020 ◽

Keyword(s):

Machine Learning ◽

Factor Analysis ◽

Exploratory Factor Analysis ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Number Of Factors

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Forecasting US movies box office performances in Turkey using machine learning algorithms

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189120 ◽

2020 ◽

Vol 39 (5) ◽

pp. 6579-6590

Author(s):

Sandy Çağlıyor ◽

Başar Öztayşi ◽

Selime Sezgin

Keyword(s):

Machine Learning ◽

Global Economy ◽

Learning Algorithms ◽

Forecast Model ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

High Stakes ◽

Box Office ◽

Industry Forecast ◽

The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

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Intelligent system of English composition scoring model based on improved machine learning algorithm

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189235 ◽

2020 ◽

pp. 1-11

Author(s):

Jie Liu ◽

Lin Lin ◽

Xiufang Liang

Keyword(s):

Machine Learning ◽

Evaluation System ◽

Intelligent System ◽

Learning Algorithm ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Assessment System ◽

English Composition ◽

Region Extraction ◽

Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

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The Unlearnable Checkerboard Pattern

Communications of the Blyth Institute ◽

10.33014/issn.2640-5652.1.2.holloway.1 ◽

2019 ◽

Vol 1 (2) ◽

pp. 78-80

Author(s):

Eric Holloway

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Checkerboard Pattern ◽

Simple Task

Detecting some patterns is a simple task for humans, but nearly impossible for current machine learning algorithms. Here, the "checkerboard" pattern is examined, where human prediction nears 100% and machine prediction drops significantly below 50%.

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