Implementation Of The Machine Learning Classifier For...

\chapter{Implementation} Implementation of the machine learning classifier for anomaly detection involves using some libraries which help to execute the different steps to classify data and perform analysis. In the next section the detailed implementation of this project will be discussed. \section{Dataset selection} This was the most important part of the entire project and consumed a lot of time. Selecting a suitable database to perform the desired type of analysis was a very difficult task as there are a very few well organized and labeled medical databases which are suitable to perform anomaly detection. The database used by this project is the Pima Indians Diabetes database \cite{Dataset} which is a well structured and a labeled†¦show more content†¦This helps to display the data points, attributes and various features in the dataset. The other library that is used in the project is Scikit-learn \cite{scikit}. This library plays an important role in building the classifier and designing the machine learning algorithm. Scikit-learn \cite{scikit} is an open source library which provides efficient tools for data mining and data analysis. It is based on other Python libraries like NumPy \cite{Numpy}, Scipy \cite{Scipy} and Matplotlib \cite{Matplot}. Scikit \cite{scikit} provides various functions and methods for classification and hence helps to build an efficient classifier to detect anomalies in the data. \section{Machine learning algorithms} There is a vast collection of machine learning classifiers that are provided in the Scikit-learn library \cite{scikit}. All that is needed to do is install and import the Scikit-learn \cite{scikit} library. Three different machine learning algorithms are used to build a classification model to detect anomalies in the data. Out of these three the one which provides optimal accuracy is chosen. The machine learning algorithms that are used on the data are the Gaussian Naive Bayes algorithm, Logistic Regression algorithm and Support Vector Machine algorithm. After loading the dataset, the next important step is to visualize the data. Further, there is a need to split the data into training andShow MoreRelatedCyber Security And Technology Detection System922 Words   |  4 Pagesthis tool can be named as cyber security. To guarantee the safety of a system a tool should be able to detect an anomaly or intrusion. Thus this tool set consist of at least an Intrusion detection system. The system tries to prevent intrusion by having firewalls and tries to eliminate the damage done by the use of antivirus. Attacks can be classified as â€Å"known attacks† or â€Å"anomaly based†. 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