Python Algorithm Analysis

Abstract

In the current network security situation, the development of important business applications in lcoud centres has greatly increased. The development and use of software-defined networks in cloud data centres have greatly reduced the effect of traditional network security boundary protection. To find an effective algorithm to protect important applications in open multi-step large-scale cloud data centres is a problem we need to solve.

Goal

Threat intelligence has become an important means to solve complex network attacks and realize real-time threat early warning and attack tracking because of it's ability to analyze the threat intelligence data of various network attacks. Based on research, this projects evaluated performance of 6 popular machine and deep learning algorithms for classification tasks using network attack-related datasets. These algorithms are prepared according to several performance evaluation metrics including precision, recall, F1-score, accuracy and confusion matrix.

Results

Both the machine learning and deep learning algorithms were used to perform comprehensive experiments that indicate that considering all performance metrics CNN deep learning algorithm performed better than other machine learning models. While among deep learning models, ANN and CNN achieved more interesting results.

API Library Used

Sklearn

Sklearn library consists of a lot of efficient tools for machine learning and statistical modeling.

Here, we have used the same to implement statistical modeling tools which are classification, regression, clustering and dimensionality reduction.

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Keras

A high-level, deep learning API developed by google for implementing neural networks.

Imported Maxpooling2D, Dense, Dropout, Activation, Flatten, Convolution2D,

Sequential.

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Sklearn Metrics

The metrics imported from the Sklearn library for the project are precision_score, recall_score, f1_score and accuracy_score.

These are used to compare the performance of each deep and machine learning models to determine the most suitable algorithm in accord to the data.

Sklearn Models

The Sklearn models framework imported for the projects are SVM, Naive Bayes where we used BernoulliNB, KNN, Decision Tree, Random Forest Classifier.

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MatplotLib

This library is often used to display graphical representation of a dataset to better understand and extract information for the stakeholders. In this project it is used along side with NumPy. Uses NumPy and helps in splitting the arrays in the dataset to plot the bar graphs.

Numpy

Numpy is a python library used to perform

mathematical operations specifically on arrays. It is used under the MatPyLib as a big data numerical handling resource. In this project we use NumPy to help us split the arrays of data into training and testing sets.

Tkinter

Tkinter is a standard library for creating graphical user interface for desktop based applications.

Here in this project we have used Tkinter to help contruct the GUI and include labels, buttons, dialog boxes and frames.

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Keras Models

Keras is a deep learning algorithm library that i used to implement neural networks profiling where in this project i opted for LSTM and CNN algorithms to test them both with performance metrics such as accuracy, precision, recall and Fmeasure.

System Architechture

1. Network Attack Dataset- A KDD datasets which consists of approximately 4,900,000 single connection vectors each of which contains 41 features and is labelled as either normal or an attack with exactly one specific attack type.

2. Data Preprocessing- The processor removes null parameters, clear empty datasets.This is called as Data Normalization.

3.Machine Learning and Deep Learning Algorithms- SVM, LSTM,CNN,Random Forest, Decision Tree, Naïve Bayes and KNN alogirthms are implemented.

4. Algorithm Comparision- Based on the comaparison, a bar chart will be displayed showing which algorithm gives best accuracy and suitable for network based attack.