scholarly journals Optimizing Filter-Based Feature Selection Method Flow for Intrusion Detection System

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2114
Author(s):  
Murtaza Ahmed Siddiqi ◽  
Wooguil Pak
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
High Efficiency ◽  
Detection System ◽  
Feature Selection Method ◽  
Extraction Methods ◽  
Security Requirements ◽  
Digital Information ◽  
Selection Methods ◽  
Process Flow

In recent times, with the advancement in technology and revolution in digital information, networks generate massive amounts of data. Due to the massive and rapid transmission of data, keeping up with security requirements is becoming more challenging. Machine learning (ML)-based intrusion detection systems (IDSs) are considered as one of the most suitable solutions for big data security. Despite the progress in ML, unrelated features can drastically influence the performance of an IDS. Feature selection plays a significant role in improving ML-based IDSs. However, the recent growth of dimensionality in data poses quite a challenge for current feature selection and extraction methods. Due to high data dimensionality, feature selection methods suffer in terms of efficiency and effectiveness. In this paper, we are introducing a new process flow for filter-based feature selection with the help of a transformation technique. Generally, normalization or transformation is implemented before classification. In our proposed model, we implemented and evaluated the effects of normalization before feature selection. To present a clear analysis on the effects of power transformation, five different transformations were implemented and evaluated. Furthermore, we implemented and compared different feature selection methods with the proposed process flow. Results show that compared with existing process flow and feature selection methods, our proposed process flow for feature selection can locate a more relevant set of features with high efficiency and accuracy.

scholarly journals IDSFS: A Signature Based Intrusion Detection System with High Pertinent Feature Selection Method

2019 ◽  
Vol 8 (2) ◽  
pp. 25-31
Author(s):  
S. Latha ◽  
Sinthu Janita Prakash
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
False Positive Rate ◽  
Feature Selection Method ◽  
Selection Method ◽  
Positive Rate ◽  
Frame Work ◽  
Pertinent Feature

Securing a network from the attackers is a challenging task at present as many users involve in variety of computer networks. To protect any individual host in a network or the entire network, some security system must be implemented. In this case, the Intrusion Detection System (IDS) is essential to protect the network from the intruders. The IDS have to deal with a lot of network packets with different characteristics. A signature-based IDS is a potential tool to understand former attacks and to define suitable method to conquest it in variety of applications. This research article elucidates the objective of IDS with a mechanism which combines the network and host-based IDS. The benchmark dataset for DARPA is considered to generate the IDS mechanism. In this paper, a frame work IDSFS – a signature-based IDS with high pertinent feature selection method is framed. This frame work consists of earlier proposed Feature Selection method (HPFSM), Artificial Neural Network for classification of nodes or packets in the network, then the signatures or attack rules are configured by implementing Association Rule mining algorithm and finally the rules are restructured using a pattern matching algorithm-Aho-Corasick to ease the rule checking. The metrics like number of features, classification accuracy, False Positive Rate (FPR), Precision, Number of rules, Running Time and Memory consumption are checked and proved the proposed frame work’s efficiency.

A Study of Feature Selection and Dimensionality Reduction Methods for Classification-Based Phishing Detection System

2021 ◽  
Vol 11 (1) ◽  
pp. 1-35
Author(s):  
Amit Singh ◽  
Abhishek Tiwari
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Detection System ◽  
Parallel Evolution ◽  
Feature Selection Method ◽  
Sensitive Information ◽  
Selection Methods ◽  
Survey Paper ◽  
Reduction Methods

Phishing was introduced in 1996, and now phishing is the biggest cybercrime challenge. Phishing is an abstract way to deceive users over the internet. Purpose of phishers is to extract the sensitive information of the user. Researchers have been working on solutions of phishing problem, but the parallel evolution of cybercrime techniques have made it a tough nut to crack. Recently, machine learning-based solutions are widely adopted to tackle the menace of phishing. This survey paper studies various feature selection method and dimensionality reduction methods and sees how they perform with machine learning-based classifier. The selection of features is vital for developing a good performance machine learning model. This work is comparing three broad categories of feature selection methods, namely filter, wrapper, and embedded feature selection methods, to reduce the dimensionality of data. The effectiveness of these methods has been assessed on several machine learning classifiers using k-fold cross-validation score, accuracy, precision, recall, and time.

A Comparative Study on the Feature Selection Techniques for Intrusion Detection System

2019 ◽  
Vol 8 (1) ◽  
pp. 42-47
Author(s):  
D. Selvamani ◽  
V. Selvi
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Comparative Study ◽  
Intrusion Detection System ◽  
Detection System ◽  
Feature Selection Method ◽  
Support Vector ◽  
Network Intrusion ◽  
Chi Square Analysis ◽  
Feature Selection Techniques

The Intrusion Detection System (IDS) can be used broadly for securing the network. Intrusion detection systems (IDS) are typically positioned laterally through former protecting safety automation, like access control and verification, as a subsequent line of resistance that guards data classifications. Feature selection is employed to diminish the number of features in various applications where data has more than hundreds of attributes. Essential or relevant attribute recognition has converted a vital job to utilize data mining algorithms efficiently in today world situations. This article describes the comparative study on the Information Gain, Gain Ratio, Symmetrical Uncertainty, Chi-Square analysis feature selection techniques with different Classification methods like Artificial Neural Network, Naïve Bayes and Support Vector Machine. In this article, different performance metrics has utilized to choose the appropriate Feature Selection method for better data classification in IDS.

scholarly journals Effects of Machine Learning Approach in Flow Based Anomaly Detection on Software Defined Networking

2019 ◽  
Author(s):  
Samrat Kumar Dey ◽  
Md. Mahbubur Rahman
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Anomaly Detection ◽  
Learning Algorithm ◽  
Detection System ◽  
Single Point ◽  
Feature Selection Method ◽  
Software Defined Networking ◽  
Recursive Feature Elimination

Recent advancements in Software Defined Networking (SDN) makes it possible to overcome the management challenges of traditional network by logically centralizing control plane and decoupling it from forwarding plane. Through centralized controllers, SDN can prevent security breach, but it also brings in new threats and vulnerabilities. Central controller can be a single point of failure. Hence, flow-based anomaly detection system in OpenFlow Controller can secure SDN to a great extent. In this paper, we investigated two different approaches of flow-based intrusion detection system in OpenFlow Controller. The first of which is based on machine-learning algorithm where NSL-KDD dataset with feature selection ensures the accuracy of 82% with Random Forest classifier using Gain Ratio feature selection evaluator. In the later phase, the second approach is combined with Gated Recurrent Unit Long Short-Term Memory based intrusion detection model based on Deep Neural Network (DNN) where we applied an appropriate ANOVA F-Test and Recursive Feature Elimination feature selection method to improve the classifier performance and achieved an accuracy of 88%. Substantial experiments with comparative analysis clearly show that, deep learning would be a better choice for intrusion detection in OpenFlow Controller.

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