Modified genetic algorithm based feature subset selection in intrusion detection system

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.

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Securing the Network by the Intruder using Predictive Accession

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.e6256.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 1822-1825

Keyword(s):

Genetic Algorithm ◽

Intrusion Detection ◽

Private Information ◽

Intrusion Detection System ◽

Genetic Parameters ◽

Detection System ◽

Modified Genetic Algorithm ◽

Main Target ◽

High Level ◽

Intrusion Detection And Prevention

Today, it is very crucial to deliver a immense level security to protect highly delicate and private information. Intrusion Detection System is an essential technology in Network Security. So my aim is to use IDS system and meliorate the performance of the IDS. The main target of Intrusion detection and prevention systems (IDPS) is to determine the feasible incidents, logging clue about them and in report attempts. Intruder is the person who tries to interrupt the network. So, it is essential to present a high level security to defend extremely susceptible and confidential information. Key aim of this thesis is to secure the network from the intruders identifying them also predicting them for the next transactions within the network. For that modified genetic algorithm is used to rank the parents and produce better individuals for the prediction of the intruders and provide better security to the network. Also improve the performance of genetic parameters.

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Improving DDoS Attack Predection Performance using Ensambling Techniqes

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c6860.098319 ◽

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.

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Intrusion Detection System for Malicious Traffic Using Evolutionary Search Algorithm

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813999200821162547 ◽

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.

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

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.6.20458 ◽

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.

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