Review of Ensemble-Based Filter Feature Selection Techniques for Building Intrusion Detection System

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Ishita Karna ◽  
Aniket Madam ◽  
Chinmay Deokule ◽  
Rahul Adhao ◽  
Vinod Pachghare
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Ensemble Learning ◽  
Detection System ◽  
Critical Role ◽  
Monitoring Network ◽  
Detection Systems ◽  
Computational Overhead ◽  
Optimal Subset ◽  
Feature Selection Techniques

Intrusion detection systems (IDS) play a critical role in network security by monitoring network traffic for malicious activities and detecting vulnerability exploits against target applications or computers. A large number of redundant and irrelevant features increase the dimensionality of the dataset, which increases the computational overhead on the system and reduces its performance. This paper studies different filter-based feature selection techniques to improve performance of system. Feature selection techniques are used to select a well performing subset of features followed by technique of ensemble learning, which selects an optimal subset of features by combining multiple subsets of features. Feature selection combined with ensemble learning is explored in this paper. The performance of the algorithms implemented in existing research in terms of accuracy, false alarm rates, and true positive rates is explored, and their shortcomings are observed.

Fusion of Feature Selection and Random Forest for an Anomaly-Based Intrusion Detection System

2019 ◽  
Vol 16 (8) ◽  
pp. 3603-3607 ◽  
Author(s):  
Shraddha Khonde ◽  
V. Ulagamuthalvi
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Intrusion Detection ◽  
Real Time ◽  
Intrusion Detection System ◽  
New Technologies ◽  
Detection System ◽  
Sensitive Data ◽  
Detection Systems ◽  
New Type

Considering current network scenario hackers and intruders has become a big threat today. As new technologies are emerging fast, extensive use of these technologies and computers, what plays an important role is security. Most of the computers in network can be easily compromised with attacks. Big issue of concern is increase in new type of attack these days. Security to the sensitive data is very big threat to deal with, it need to consider as high priority issue which should be addressed immediately. Highly efficient Intrusion Detection Systems (IDS) are available now a days which detects various types of attacks on network. But we require the IDS which is intelligent enough to detect and analyze all type of new threats on the network. Maximum accuracy is expected by any of this intelligent intrusion detection system. An Intrusion Detection System can be hardware or software that analyze and monitors all activities of network to detect malicious activities happened inside the network. It also informs and helps administrator to deal with malicious packets, which if enters in network can harm more number of computers connected together. In our work we have implemented an intellectual IDS which helps administrator to analyze real time network traffic. IDS does it by classifying packets entering into the system as normal or malicious. This paper mainly focus on techniques used for feature selection to reduce number of features from KDD-99 dataset. This paper also explains algorithm used for classification i.e., Random Forest which works with forest of trees to classify real time packet as normal or malicious. Random forest makes use of ensembling techniques to give final output which is derived by combining output from number of trees used to create forest. Dataset which is used while performing experiments is KDD-99. This dataset is used to train all trees to get more accuracy with help of random forest. From results achieved we can observe that random forest algorithm gives more accuracy in distributed network with reduced false alarm rate.

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.

An Approach to Feature Selection in Intrusion Detection Systems Using Machine Learning Algorithms

2020 ◽  
Vol 16 (4) ◽  
pp. 48-58
Author(s):  
Kavitha G. ◽  
Elango N. M.
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Computational Models ◽  
Detection System ◽  
Rapid Development ◽  
Machine Learning Algorithms ◽  
The Novel ◽  
Feature Selection Technique ◽  
Detection Systems ◽  
Combined Feature

The rapid development of various services that are provided by information technology has been widely accepted by the users who are making use of such services in their day-to-day life activities. Securing such a system application from various intrusions still remains to be a one of the major issues in the current era. Detecting such anomalies from the regular events involves various steps such as data pre-processing, feature selection, and classification. Many of the computational models intend to accurately discriminate the samples of each group for better classification by identifying candidate features prior to the learning phase. This research studies the implementation of a combined feature selection technique such as the GRRF-FWSVM method which is applied to the benchmarked anomaly detection dataset KDD CUP 99. The results prove the novel proposed hybrid model is an effective method in identifying anomalies and it increases the detection rate of about 98.55% of the intrusion detection system with the two most common benchmark models.

Fuzzy Rule-Based Layered Classifier and Entropy-Based Feature Selection for Intrusion Detection System

2021 ◽  
pp. 289-309
Author(s):  
Devaraju Sellappan ◽  
Ramakrishnan Srinivasan
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Fuzzy Rule ◽  
Intrusion Detection Systems ◽  
Rule Based ◽  
Detection Systems ◽  
Positive Rate ◽  
Rule Based Classifier

Intrusion detection systems must detect the vulnerability consistently in a network and also perform efficiently with the huge amount of traffic. Intrusion detection systems must be capable of detecting emerging and proactive threats in the networks. Various classifiers are used to classify the threats as normal or intrusive by supervising the system activity. In this chapter, layered fuzzy rule-based classifier is proposed to detect the various intrusions, and fuzzy entropy-based feature selection is proposed to identify the relevant features. Layered fuzzy rule-based classifier is proposed to improve the performance of the intrusion detection system. KDD dataset contains various attacks; these attacks are grouped into four classes, namely Denial-of-Service (DoS), Probe, Remote-to-Local (R2L), and User-to-Root (U2R). Real-time dataset is also considered in this research. Experimental result shows that the proposed method provides good detection rate, minimizes the false positive rate, and less computational time.

Building an Effective Approach toward Intrusion Detection Using Ensemble Feature Selection

2019 ◽  
Vol 13 (3) ◽  
pp. 31-47 ◽  
Author(s):  
Alok Kumar Shukla ◽  
Pradeep Singh
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Detection System ◽  
Classification Performance ◽  
Support Vector ◽  
Web Traffic ◽  
Data Set ◽  
Optimal Subset ◽  
Benchmark Datasets ◽  
Kdd Cup 99

The duplicate and insignificant features present in the data set to cause a long-term problem in the classification of network or web traffic. The insignificant features not only decrease the classification performance but also prevent a classifier from making accurate decisions, exclusively when substantial volumes of data are managed. In this article, the author introduced an ensemble feature selection (EFS) technique, where multiple homogeneous feature selection (FS) methods are combined to choose the optimal subset of relevant and non-redundant features. An intrusion detection system, named support vector machine-based IDS (SVM-IDS), is prompted using the feature selected by the proposed method. The SVM-IDS performance is evaluated using two benchmark datasets of intrusion detection, including KDD Cup 99 and NSL-KDD. Our proposed method provided more significant features for SVM-IDS and compared with the other state-of-the-art methods. The experimental results demonstrate that proposed method achieves a maximum accuracy as 98.95% in KDD Cup 99 data set and 98.12% in the NSL-KDD data set.

scholarly journals An Efficient Intrusion Detection System Using Improved Bias Based Convolutional Neural Network Classifier

2021 ◽  
Vol 12 (6) ◽  
pp. 2468-2482
Author(s):  
Mathiyalagan R, Pamela Vinitha Eric
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Detection System ◽  
Modern Society ◽  
Relevance Vector Machine ◽  
Dragonfly Algorithm ◽  
Detection Systems

Today’s modern society has faced many challenges due to the rapid digitization and growing number of hackers, which makes the networking-based systems to become a target place for intruders. The attacks may allure the users, and it compromised the whole system and makes the security the biggest challenge. In this regard, the best way to combat the issues is by exploring new ways to defend the network against threats. More recently, Intrusion Detection Systems (IDS) is a key enabling technology in maintaining the novel network security. Indeed, some existing systems utilize Improved Relevance Vector Machine (IVRM) classifier for performing intrusion detection in network-based systems.  In this work, feature selection is done by using Gaussian Firefly Algorithm and Improved Relevance Vector Machine (IRVM) based classification is performed according to the selected features. However, for large-scale intrusion dataset, the intrusion detection is not robust; hence, it leads to high attack rates. The proposed system designed an Improved Bias based Convolutional Neural Network (ICNN) for high attack intrusion detection.  For embracing high-security factors and enhanced protection, the proposed system performs three phases, such as preprocessing, feature selection, and classification. The first phase employs the KDD dataset and Kalman filtering method followed by feature selection utilizes Inertia Weight based Dragonfly Algorithm (IWDA) and finally identified the intrusion attacks using Improved Bias based Convolutional Neural Network (IBCNN) classifier. In this work, a novel model performed with the KDD dataset. The suggested method evaluated in terms of accuracy, f-measure, recall, and precision for examining performance compared with existing systems.

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