scholarly journals Intrusion detection model using machine learning algorithm on Big Data environment

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Suad Mohammed Othman ◽  
Fadl Mutaher Ba-Alwi ◽  
Nabeel T. Alsohybe ◽  
Amal Y. Al-Hashida
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Intrusion Detection ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Detection Model ◽  
Data Environment

Binary Classification of Network-Generated Flow Data Using a Machine Learning Algorithm

2021 ◽  
Vol 15 (1) ◽  
pp. 26-43
Author(s):  
Sikha Bagui ◽  
Keenal M. Shah ◽  
Yizhi Hu ◽  
Subhash Bagui
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Information Gain ◽  
Learning Algorithm ◽  
Binary Classification ◽  
Intrusion Detection Systems ◽  
Machine Learning Algorithm ◽  
Detection Systems ◽  
Data Environment

This study proposes a model for building intrusion detection systems. The dataset used, CICIDS 2017, contains 14 different attacks with 85 features for each attack. This high dimensionality of the data is a major challenge when building efficient intrusion detection systems, especially in today's big data environment, since a lot of the features are redundant. The main goal in this paper was to reduce the number of features and present a detailed discussion of the important features. For feature selection, information gain was used in an iterative way, and for classification, a machine learning algorithm, the J48 decision tree algorithm, was used. The important features for the classification of each attack were identified, and the features that were important for classifying multiple attacks were also identified and discussed.

scholarly journals NIDM: Novel Intrusion Detection Model Using CNN-LSTM for Big Data Environment

2021 ◽  
Vol 9 (10) ◽  
pp. 827-831
Author(s):  
Rohan Benhal
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Intrusion Detection ◽  
Learning Algorithm ◽  
Learning Model ◽  
Attack Detection ◽  
Detection Model ◽  
Deep Learning Algorithm ◽  
Machine Learning Model ◽  
Data Points

Abstract: Machine learning-based (IDS) have become a critical component of safeguarding our economic and national security because of the massive quantities of data produced each day and the growing interconnection of the world's Internet infrastructures. The existing machine Learning Model technique may have difficulty comprehending the ever-increasingly complex distribution of data invasion patterns. With a small number of data points, a single deep learning algorithm may be ineffective at capturing different patterns for intrusive attacks. We presented CNN-LSTM Novel Intrusion Detection Model for Big Data to improve the efficiency of IDS-based CNN-LSTM even further (NIDM). NIDM uses behavioural traits and content functions to understand the characteristics when compared to earlier single learning model tactics, this strategy can improve the rate of intrusive attack detection. Keywords: IDS, Machine Learning, LSTM, CNN.

scholarly journals Big Data for Health Care Analytics using Extreme Machine Learning Based on Map Reduce

2020 ◽  
Vol 9 (3) ◽  
pp. 2758-2762
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Storage ◽  
Clinical Data ◽  
Disease Risk ◽  
Learning Algorithm ◽  
Information Storage ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Data Set

A large volume of datasets is available in various fields that are stored to be somewhere which is called big data. Big Data healthcare has clinical data set of every patient records in huge amount and they are maintained by Electronic Health Records (EHR). More than 80 % of clinical data is the unstructured format and reposit in hundreds of forms. The challenges and demand for data storage, analysis is to handling large datasets in terms of efficiency and scalability. Hadoop Map reduces framework uses big data to store and operate any kinds of data speedily. It is not solely meant for storage system however conjointly a platform for information storage moreover as processing. It is scalable and fault-tolerant to the systems. Also, the prediction of the data sets is handled by machine learning algorithm. This work focuses on the Extreme Machine Learning algorithm (ELM) that can utilize the optimized way of finding a solution to find disease risk prediction by combining ELM with Cuckoo Search optimization-based Support Vector Machine (CS-SVM). The proposed work also considers the scalability and accuracy of big data models, thus the proposed algorithm greatly achieves the computing work and got good results in performance of both veracity and efficiency.

scholarly journals Big data, machine learning and artificial intelligence: a neurologist’s guide

2020 ◽  
pp. practneurol-2020-002688
Author(s):  
Stephen D Auger ◽  
Benjamin M Jacobs ◽  
Ruth Dobson ◽  
Charles R Marshall ◽  
Alastair J Noyce
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Big Data ◽  
Clinical Practice ◽  
Clinical Data ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Basic Principles ◽  
Biological Neural Networks

Modern clinical practice requires the integration and interpretation of ever-expanding volumes of clinical data. There is, therefore, an imperative to develop efficient ways to process and understand these large amounts of data. Neurologists work to understand the function of biological neural networks, but artificial neural networks and other forms of machine learning algorithm are likely to be increasingly encountered in clinical practice. As their use increases, clinicians will need to understand the basic principles and common types of algorithm. We aim to provide a coherent introduction to this jargon-heavy subject and equip neurologists with the tools to understand, critically appraise and apply insights from this burgeoning field.

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