scholarly journals Using Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection

2017 ◽  
Vol 3 (4) ◽  
pp. 109-111 ◽  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Intrusion Detection ◽  
Cyber Security ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Using Data

scholarly journals A Survey : Data Mining and Machine Learning Methods for Cyber Security

2021 ◽  
pp. 24-34
Author(s):  
Ashish Prajapati ◽  
Shital Gupta
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Intrusion Detection ◽  
Survey Data ◽  
Cyber Security ◽  
Literature Survey ◽  
Learning Methods ◽  
Survey Paper ◽  
Machine Learning Methods ◽  
Number Of Citations

This survey paper describes the literature survey for cyber analytics in support of intrusion detection of machine learnings (ML) and data mining (DM) methods. Short ML/DM method tutorial details will be given. Documents representing each method were categorized, read and summarized based on the number of citations and significance of an evolving method. Since data is so important.

Towards Behaviour Recognition with Unlabelled Sensor Data

2013 ◽  
pp. 86-110
Author(s):  
Sook-Ling Chua ◽  
Stephen Marsland ◽  
Hans W. Guesgen
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Inverse Problem ◽  
Sensor Data ◽  
Training Set ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Using Data ◽  
Symbolic Approach ◽  
Behaviour Recognition

The problem of behaviour recognition based on data from sensors is essentially an inverse problem: given a set of sensor observations, identify the sequence of behaviours that gave rise to them. In a smart home, the behaviours are likely to be the standard human behaviours of living, and the observations will depend upon the sensors that the house is equipped with. There are two main approaches to identifying behaviours from the sensor stream. One is to use a symbolic approach, which explicitly models the recognition process. Another is to use a sub-symbolic approach to behaviour recognition, which is the focus in this chapter, using data mining and machine learning methods. While there have been many machine learning methods of identifying behaviours from the sensor stream, they have generally relied upon a labelled dataset, where a person has manually identified their behaviour at each time. This is particularly tedious to do, resulting in relatively small datasets, and is also prone to significant errors as people do not pinpoint the end of one behaviour and commencement of the next correctly. In this chapter, the authors consider methods to deal with unlabelled sensor data for behaviour recognition, and investigate their use. They then consider whether they are best used in isolation, or should be used as preprocessing to provide a training set for a supervised method.

scholarly journals Defining and Predicting Pain Volatility in Users of the Manage My Pain App: Analysis Using Data Mining and Machine Learning Methods

10.2196/12001 ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. e12001 ◽  
Author(s):  
Quazi Abidur Rahman ◽  
Tahir Janmohamed ◽  
Meysam Pirbaglou ◽  
Hance Clarke ◽  
Paul Ritvo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Using Data

Threat Detection in Cyber Security Using Data Mining and Machine Learning Techniques

2020 ◽  
pp. 234-253
Author(s):  
Daniel Kobla Gasu
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Intrusion Detection ◽  
Cyber Security ◽  
Attack Detection ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Threat Detection ◽  
Use Of The Internet ◽  
Using Data

The internet has become an indispensable resource for exchanging information among users, devices, and organizations. However, the use of the internet also exposes these entities to myriad cyber-attacks that may result in devastating outcomes if appropriate measures are not implemented to mitigate the risks. Currently, intrusion detection and threat detection schemes still face a number of challenges including low detection rates, high rates of false alarms, adversarial resilience, and big data issues. This chapter describes a focused literature survey of machine learning (ML) and data mining (DM) methods for cyber analytics in support of intrusion detection and cyber-attack detection. Key literature on ML and DM methods for intrusion detection is described. ML and DM methods and approaches such as support vector machine, random forest, and artificial neural networks, among others, with their variations, are surveyed, compared, and contrasted. Selected papers were indexed, read, and summarized in a tabular format.

scholarly journals Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey

2019 ◽  
Vol 9 (20) ◽  
pp. 4396 ◽  
Author(s):  
Hongyu Liu ◽  
Bo Lang
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Cyber Security ◽  
Detection System ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
Detection Accuracy ◽  
Learning Methods ◽  
Machine Learning Methods

Networks play important roles in modern life, and cyber security has become a vital research area. An intrusion detection system (IDS) which is an important cyber security technique, monitors the state of software and hardware running in the network. Despite decades of development, existing IDSs still face challenges in improving the detection accuracy, reducing the false alarm rate and detecting unknown attacks. To solve the above problems, many researchers have focused on developing IDSs that capitalize on machine learning methods. Machine learning methods can automatically discover the essential differences between normal data and abnormal data with high accuracy. In addition, machine learning methods have strong generalizability, so they are also able to detect unknown attacks. Deep learning is a branch of machine learning, whose performance is remarkable and has become a research hotspot. This survey proposes a taxonomy of IDS that takes data objects as the main dimension to classify and summarize machine learning-based and deep learning-based IDS literature. We believe that this type of taxonomy framework is fit for cyber security researchers. The survey first clarifies the concept and taxonomy of IDSs. Then, the machine learning algorithms frequently used in IDSs, metrics, and benchmark datasets are introduced. Next, combined with the representative literature, we take the proposed taxonomic system as a baseline and explain how to solve key IDS issues with machine learning and deep learning techniques. Finally, challenges and future developments are discussed by reviewing recent representative studies.

scholarly journals Comparison of Prediction Models for Delays of Freight Trains by Using Data Mining and Machine Learning Methods

2019 ◽  
Vol 29 (1) ◽  
pp. 45-47
Author(s):  
Dennis Leser ◽  
Matthias Wastian ◽  
Matthias Rößler ◽  
Michael Landsiedl ◽  
Edmond Hajrizi
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Prediction Models ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Using Data

Using of data science in healthcare

2021 ◽  
pp. 149-156
Author(s):  
Ihor Ponomarenko ◽  
Oleksandra Lubkovska
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Business Intelligence ◽  
Data Science ◽  
Large Data ◽  
Science Methods ◽  
Medical Field ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Using Data

The subject of the research is the approach to the possibility of using data science methods in the field of health care for integrated data processing and analysis in order to optimize economic and specialized processes The purpose of writing this article is to address issues related to the specifics of the use of Data Science methods in the field of health care on the basis of comprehensive information obtained from various sources. Methodology. The research methodology is system-structural and comparative analyzes (to study the application of BI-systems in the process of working with large data sets); monograph (the study of various software solutions in the market of business intelligence); economic analysis (when assessing the possibility of using business intelligence systems to strengthen the competitive position of companies). The scientific novelty the main sources of data on key processes in the medical field. Examples of innovative methods of collecting information in the field of health care, which are becoming widespread in the context of digitalization, are presented. The main sources of data in the field of health care used in Data Science are revealed. The specifics of the application of machine learning methods in the field of health care in the conditions of increasing competition between market participants and increasing demand for relevant products from the population are presented. Conclusions. The intensification of the integration of Data Science in the medical field is due to the increase of digitized data (statistics, textual informa- tion, visualizations, etc.). Through the use of machine learning methods, doctors and other health professionals have new opportunities to improve the efficiency of the health care system as a whole. Key words: Data science, efficiency, information, machine learning, medicine, Python, healthcare.

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