Feature Reduction and Classifications Techniques for Intrusion Detection System

2020 ◽  
Author(s):  
Gulab Sah ◽  
Subhasish Banerjee
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Feature Reduction

scholarly journals Feature Selection Models Based on Hybrid Firefly Algorithm with Mutation Operator for Network Intrusion Detection

2021 ◽  
Vol 14 (1) ◽  
pp. 192-202
Author(s):  
Karrar Alwan ◽  
◽  
Ahmed AbuEl-Atta ◽  
Hala Zayed ◽  
◽  
...  
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Firefly Algorithm ◽  
Detection System ◽  
Binary Classification ◽  
Feature Reduction ◽  
Detection Accuracy ◽  
Multi Classification ◽  
Modified Firefly Algorithm

Accurate intrusion detection is necessary to preserve network security. However, developing efficient intrusion detection system is a complex problem due to the nonlinear nature of the intrusion attempts, the unpredictable behaviour of network traffic, and the large number features in the problem space. Hence, selecting the most effective and discriminating feature is highly important. Additionally, eliminating irrelevant features can improve the detection accuracy as well as reduce the learning time of machine learning algorithms. However, feature reduction is an NPhard problem. Therefore, several metaheuristics have been employed to determine the most effective feature subset within reasonable time. In this paper, two intrusion detection models are built based on a modified version of the firefly algorithm to achieve the feature selection task. The first and, the second models have been used for binary and multiclass classification, respectively. The modified firefly algorithm employed a mutation operation to avoid trapping into local optima through enhancing the exploration capabilities of the original firefly. The significance of the selected features is evaluated using a Naïve Bayes classifier over a benchmark standard dataset, which contains different types of attacks. The obtained results revealed the superiority of the modified firefly algorithm against the original firefly algorithm in terms of the classification accuracy and the number of selected features under different scenarios. Additionally, the results assured the superiority of the proposed intrusion detection system against other recently proposed systems in both binary classification and multi-classification scenarios. The proposed system has 96.51% and 96.942% detection accuracy in binary classification and multi-classification, respectively. Moreover, the proposed system reduced the number of attributes from 41 to 9 for binary classification and to 10 for multi-classification.

scholarly journals Multilevel Intrusion Detection System with Affinity Clustering and Ensemble SVM

2020 ◽  
pp. 522-529
Author(s):  
Sadhana Patidar ◽  
Priyanka Parihar ◽  
Chetan Agrawal
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Model Performance ◽  
Feature Reduction ◽  
Support Vector ◽  
Learning Tools ◽  
Security Issues ◽  
Proposed Model

Now-a-days with growing applications over internet increases the security issues over network. Many security applications are designed to cope with such security concerns but still it required more attention to improve speed as well accuracy. With advancement of technologies there is also evolution of new threats or attacks in network. So, it is required to design such detection system that can handle new threats in network. One of the network security tools is intrusion detection system which is used to detect malicious data packets. Machine learning tool is also used to improve efficiency of network-based intrusion detection system. In this paper, an intrusion detection system is proposed with an application of machine learning tools. The proposed model integrates feature reduction, affinity clustering and multilevel Ensemble Support Vector Machine. The proposed model performance is analyzed over two datasets i.e. NSL-KDD and UNSW-NB 15 dataset and achieved approx. 12% of efficiency over other existing work.

scholarly journals A Review of Feature Reduction in Intrusion Detection System Based on Artificial Immune System and Neural Network

2013 ◽  
Vol 9 (3) ◽  
pp. 1127-1133
Author(s):  
Uma Vishwakarma ◽  
Prof. Anurag Jain ◽  
Prof. Akriti Jain
Keyword(s):  
Neural Network ◽  
Immune System ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Artificial Immune System ◽  
Detection System ◽  
Reduction Process ◽  
Feature Reduction ◽  
Dynamic Feature ◽  
Artificial Immune

Feature reduction plays an important role in intrusion detection system. The large amount of feature in network as well as host data effect the performance of intrusion detection method. Various authors are research proposed a method of intrusion detection based on machine learning approach and neural network approach, but all of these methods lacks in large number of feature attribute in intrusion data. In this paper we discuss its various method of feature reduction using artificial immune system and neural network. Artificial immune system is biological inspired system work as mathematical model for feature reduction process. The neural network well knows optimization technique in other field. In this paper we used neural network as feature reduction process. The feature reduction process reduces feature of intrusion data those are not involved in security threats and attacks such as TCP protocol, UDP protocol and ICMP message protocol. This reduces feature-set of intrusion improve the classification rate of intrusion detection and improve the speed performance of the intrusion detection system. The current research going on fixed and static number of feature reduction, we proposed an automatic and dynamic feature reduction technique using PCNN network.

scholarly journals Feature Reduction using Lasso Hybrid Algorithm in Wireless Intrusion Detection System

2019 ◽  
Vol 8 (11) ◽  
pp. 1476-1483
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Vital Role ◽  
Feature Reduction ◽  
False Alarms ◽  
Security Breaches ◽  
Number Of False Alarms ◽  
Machine Learning Model

To maintain the integrity and protection of networks, intrusion detection systems play a vital role. Growth of wireless networks turned the globe to perform all pecuniary tasks online resulting a lot of security breaches in the network. One of the common breaches happening in network is the intruders who eventually tries to bypass the adopted security framework. Every day new intrusions arises and new solutions as well, however the research in making the intrusion detection system intelligent holds energetic. Today most of the systems are becoming intelligent by adopting machine learning and artificial intelligence algorithms. Success of building an efficient machine learning model to make intelligent intrusion detection system is relied on the effective features considered for classification and prediction. Thus, feature reduction is an integral part for discarding irrelevant and redundant features to produce a computationally decisive system that can identify defects with high accuracy. This implementation is an attempt to identify the smaller feature set possible for the well adopted wireless intrusion detection dataset AWID. Here, we proposed a LASSO based implementation to produce a smaller decisive set of features. Incorporation of Lasso on feature reduction not only provides a smaller set of features, but also allow to adopt prediction algorithms inside Lasso resulting lesser number of false alarms as well

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