Intrusion Detection System Using Bayesian Network and Feature Subset Selection

2017 ◽  
Author(s):  
M.A. Jabbar ◽  
Rajanikanth Aluvalu ◽  
S. Sai Satyanarayana Reddy
Keyword(s):  
Intrusion Detection ◽  
Bayesian Network ◽  
Intrusion Detection System ◽  
Detection System ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Feature Subset

scholarly journals Knowledgeable Handling of Impreciseness in Feature Subset Selection using Intuitionistic Fuzzy Mutual Information of Intrusion Detection System

2019 ◽  
Vol 8 (12) ◽  
pp. 1539-1544
Keyword(s):  
Mutual Information ◽  
Intrusion Detection ◽  
Network Traffic ◽  
Intrusion Detection System ◽  
Detection System ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Classification Model ◽  
Feature Subset ◽  
Intuitionistic Fuzzy

One of the most promising areas of domain in research field is security because of its exponential usage in everyday commercial activities. Due to prevalence diffusion of network connectivity, there is a high demand for protection against cyber-attack which necessitates the importance of intrusion detection system as a significant tool for network security. There are many intrusion detection models available to classify the network traffic s either normal or attack type. Because of huge volume of network traffic data, these classifier techniques fail to attain high detection rate with less false alarms. To overcome the above problem, this paper introduces the potential feature subset selection model using Intuitionistic Fuzzy Mutual Information (IFMI). This model efficiently selects the optimal set of attributes without loss of information even in presence of impreciseness among attributes. This is achieved by representing each attribute in the dataset in terms of degree of membership, non-membership and hesitation. To validate the performance of the IFMI its reduced feature subset is used for classification using random forest classifier. After analyzing the feature subset, the simulation results proved that the proposed model has improved the performance of classifier for predicting the network intrusion attempts. It also helps the classification model to achieve high classification rate and reduced false alarm rate in an optimized way.

scholarly journals Improving DDoS Attack Predection Performance using Ensambling Techniqes

2019 ◽  
Vol 8 (3) ◽  
pp. 4760-4763
Keyword(s):  
Random Forest ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Feature Subset Selection ◽  
Intrusion Detection Systems ◽  
Support Vector ◽  
Feature Subset ◽  
Detection Systems ◽  
Ddos Attack

This paper proposes are utilizing support vector machine (SVM), Neural networks and decision tree C5 algorithms for anticipating undesirable data's. To dispose of DoS attack we have the intrusion detection systems however we have to keep up the exhibition of the intrusion detection systems. Along these lines, we propose a novel model for intrusion detection system in cloud platform utilizing random forest classifier and XG Boost model. Random Forest (RF) is a group classifier and performs all around contrasted with other conventional classifiers for viable classification of attacks. Intrusion detection system is made quick and effective by utilization of ideal feature subset selection utilizing IG. In this paper, we showed DDoS anomaly detection on the open Cloud DDoS attack datasets utilizing Random forest and Gradient Boosting (GB) machine learning (ML) model.

Intrusion Detection System for Malicious Traffic Using Evolutionary Search Algorithm

2020 ◽  
Vol 13 ◽  
Author(s):  
Samar Al-Saqqa ◽  
Mustafa Al-Fayoumi ◽  
Malik Qasaimeh
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Search Algorithm ◽  
Detection System ◽  
Feature Subset Selection ◽  
Intrusion Detection Systems ◽  
Feature Subset ◽  
Evolutionary Search ◽  
Detection Systems

Introduction: Intrusion detection systems play a key role in system security by identifying potential attacks and giving appropriate responses. As new attacks are always emerging, intrusion detection systems must adapt to these attacks, and more work is continuously needed to develop and propose new methods and techniques that can improve efficient and effective adaptive intrusion systems. Feature selection is one of the challenging areas that need more work because of its importance and impact on the performance of intrusion detection systems. This paper applies evolutionary search algorithm in feature subset selection for intrusion detection systems. Methods: The evolutionary search algorithm for the feature subset selection is applied and two classifiers are used, Naïve Bayes and decision tree J48, to evaluate system performance before and after features selection. NSL-KDD dataset and its subsets are used in all evaluation experiments. Results: The results show that feature selection using the evolutionary search algorithm enhances the intrusion detection system with respect to detection accuracy and detection of unknown attacks. Furthermore, time performance is achieved by reducing training time, which is reflected positively in overall system performance. Discussion: The evolutionary search applied to select IDS algorithm features can be developed by modifying and enhancing mutation and crossover operators and applying new enhanced techniques in the selection process, which can give better results and enhance the performance of intrusion detection for rare and complicated attacks. Conclusion: The evolutionary search algorithm is applied to find the best subset of features for the intrusion detection system. In conclusion, it is a promising approach to be used as a feature selection method for intrusion detection. The results showed better performance for the intrusion detection system in terms of accuracy and detection rate.

scholarly journals A Novel Intrusion Detection System Using Artificial Neural Networks and Feature Subset Selection

2018 ◽  
Vol 7 (4.6) ◽  
pp. 181
Author(s):  
L. Haripriya ◽  
M. A. Jabbar ◽  
B. Seetharamulu
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection Rate ◽  
Detection System ◽  
Feature Subset Selection ◽  
Machine Learning Techniques ◽  
Feature Subset ◽  
Back Propagation Algorithm ◽  
Data Set ◽  
Artificial Neural

The growth of internet and network technologies has been increasing day by day.With the increase of these technologies, attacks and intrusions are also increasing. The prevention of these attacks has become an task. Intrusion Detection System (IDS) provides prevention against these attacks. Data Mining and Machine Learning techniques are used for IDS to reduce error rate and to improve accuracy and detection rate. In this paper, we proposed a novel Artificial Neural Network (ANN) classifier using Back propagation algorithm to model IDS. ANN is widely used supervised classifier for IDS. The performance of our model is evaluated by conducting experiments on KYOTO data set which is refined version of KDD99 data set. Empirical results show that proposed model is efficient with high detection rate and accuracy.  

scholarly journals Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Mohammad Aljanabi ◽  
Mohd Arfian Ismail ◽  
Vitaly Mezhuyev
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Parameter Tuning ◽  
Feature Subset Selection ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Feature Subset

Many optimisation-based intrusion detection algorithms have been developed and are widely used for intrusion identification. This condition is attributed to the increasing number of audit data features and the decreasing performance of human-based smart intrusion detection systems regarding classification accuracy, false alarm rate, and classification time. Feature selection and classifier parameter tuning are important factors that affect the performance of any intrusion detection system. In this paper, an improved intrusion detection algorithm for multiclass classification was presented and discussed in detail. The proposed method combined the improved teaching-learning-based optimisation (ITLBO) algorithm, improved parallel JAYA (IPJAYA) algorithm, and support vector machine. ITLBO with supervised machine learning (ML) technique was used for feature subset selection (FSS). The selection of the least number of features without causing an effect on the result accuracy in FSS is a multiobjective optimisation problem. This work proposes ITLBO as an FSS mechanism, and its algorithm-specific, parameterless concept (no parameter tuning is required during optimisation) was explored. IPJAYA in this study was used to update the C and gamma parameters of the support vector machine (SVM). Several experiments were performed on the prominent intrusion ML dataset, where significant enhancements were observed with the suggested ITLBO-IPJAYA-SVM algorithm compared with the classical TLBO and JAYA algorithms.

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