Network intrusion detection using supervised machine learning technique with feature selection
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This document details the development of a novel supervised machine learning system for classifying network traffic as either malicious or benign, utilizing artificial neural networks (ANN) and support vector machines (SVM) alongside feature selection methods. The study finds that the ANN-based system outperforms the SVM in terms of detection success rate when evaluated with the NSL-KDD dataset. Moreover, it highlights the importance of effective feature selection and presents a system architecture that integrates these components for improved intrusion detection.
Network intrusion detection using supervised machine learning technique with feature selection
- 1. Abstract A novel supervised machine learning system is developed to classify network traffic whether it is malicious or benign. To find the best model considering detection success rate, combination of supervised learning algorithm and feature selection method have been used. Through this study, it is found that Artificial Neural Network (ANN) based machine learning with wrapper feature selection outperform support vector machine (SVM) technique while classifying network traffic. To evaluate the performance, NSL-KDD dataset is used to classify network traffic using SVM and ANN supervised machine learning techniques. Comparative study shows that the proposed model is efficient than other existing models with respect to intrusion detection success rate. Existing System: While network IDS that works based on signature have seen commercial success and widespread adoption by the technology based organization throughout the globe, anomaly based network IDS have not gained success in the same scale. Due to that reason in the field of IDS, currently anomaly based detection is a major focus area of research and development and before going to any wide scale deployment of anomaly based intrusion detection system, key issues remain to be solved. But the literature today is limited when it comes to compare on how intrusion detection performs when using supervised machine learning techniques. Proposed System: The promise and the contribution machine learning did till today are fascinating. There are many real life applications we are using today offered by machine learning. It seems that machine learning will rule the world in coming days. Hence we came out into a hypothesis that the challenge of identifying new attacks or zero day attacks facing by the technology enabled organizations today can be overcome using machine learning techniques. Here we developed a supervised machine learning model that can classify unseen network traffic based on what is learnt from the seen traffic. We used both SVM and ANN learning algorithm to find the best classifier with higher accuracy and success rate.
- 2. System Architecture: The system proposed is composed of feature selection and learning algorithm show in Figure. Feature selection component are responsible to extract most relevant features or attributes to identify the instance to a particular group or class. The learning algorithm component builds the necessary intelligence or knowledge using the result found from the feature selection component. Using the training dataset, the model gets trained and builds its intelligence. Then the learned intelligences are applied to the testing dataset to measure the accuracy of home much the model correctly classified on unseen data.
- 3. Modules: Feature Selection: Feature selection is an important part in machine learning to reduce data dimensionality and extensive research carried out for a reliable feature selection method. For feature selection filter method and wrapper method have been used. In filter method, features are selected on the basis of their scores in various statistical tests that measure the relevance of features by their correlation with dependent variable or outcome variable. Wrapper method finds a subset of features by measuring the usefulness of a subset of feature with the dependent variable. Hence filter methods are independent of any machine learning algorithm whereas in wrapper method the best feature subset selected depends on the machine learning algorithm used to train the model. Building Machine Intelligence: Based on the best features found in the feature selection process, learning models are developed. To develop the learning model, machine learning algorithm is used. Training dataset is used to train the algorithm with the selected features. In supervised machine learning, each instance in the training dataset has the class it belongs to. The algorithm build the learning model based on which machine learning algorithm is being used. Support Vector Machine (SVM): In SVM a separating hyper plane defines the classifier depending on the type of problem and available datasets. In case where dataset is one dimensional, the hyper plane is a point, for two dimensional data it is a separating line as shown in below Figure.
- 4. Artificial Neural Network (ANN): Artificial Neural Network is another tool used in machine learning. As it name suggests, ANN is a system inspired by human brain system and replicate the learning system of human brain. It consists of input and output layers with one or more hidden layers in most cases as shown in Figure. The ANN uses a technique called back propagation to adjust the outcome with the expected result or class.
- 5. SYSTEM CONFIGURATION: Hardware requirements: Processer : Any Update Processer Ram : Min 4 GB Hard Disk : Min 100 GB Software requirements: Operating System : Windows family Technology : Python 3.6 IDE : PyCharm Front-End : PyQt5