Journal ArticleDOI
Comparative study for feature selection algorithms in intrusion detection system
K. Anusha,E. Sathiyamoorthy +1 more
TLDR
This paper identifies the best feature selection algorithm to select the important and useful features from the network dataset by identifying the optimal feature selection methods for intrusion detection.Abstract:
The Intrusion Detection System (IDS) deals with the huge amount of network data that includes redundant and irrelevant features causing slow training and testing procedure, higher resource usage and poor detection ratio. Feature selection is a vital preprocessing step in intrusion detection. Hence, feature selec-tion is an essential issue in intrusion detection and need to be addressed by selec-ting the appropriate feature selection algorithm. A major challenge to select the optimal feature selection methods can precisely calculate the relevance of fea-tures to the detection process and the redundancy among features. In this paper, we study the concepts and algorithms used for feature selection algorithms in the IDS. We conclude this paper by identifying the best feature selection algorithm to select the important and useful features from the network dataset.read more
Citations
More filters
Journal ArticleDOI
A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection
TL;DR: A detailed investigation and analysis of various machine learning techniques have been carried out for finding the cause of problems associated with variousMachine learning techniques in detecting intrusive activities and future directions are provided for attack detection using machinelearning techniques.
Journal ArticleDOI
Hybridizing genetic algorithm and grey wolf optimizer to advance an intelligent and lightweight intrusion detection system for IoT wireless networks
TL;DR: This paper presents a lightweight machine learning-based intrusion detection technique with high performance for resource limited IoT wireless networks namely, IoT intrusion detection system (IoTIDS), based on hybridization of genetic algorithm (GA) and grey wolf optimizer (GWO).
Journal ArticleDOI
An Approach for Optimizing Ensemble Intrusion Detection Systems
Deris Stiawan,Ahmad Heryanto,Ali Bardadi,Dian Palupi Rini,Imam Much Ibnu Subroto,Kurniabudi,Mohd. Yazid Idris,Abdul Hanan Abdullah,Bedine Kerim,Rahmat Budiarto +9 more
TL;DR: In this paper, an approach for generating optimized ensemble IDS is developed to find the best relevant selected features that can be used as important features in a new IDS dataset, i.e., information gain (IG), Gain Ratio (GR), Symmetrical Uncertainty (SU), Relief-F (R-F), One-R (OR) and Chi-square (CS).
Journal ArticleDOI
Multivariate correlation coefficient and mutual information-based feature selection in intrusion detection
TL;DR: Experimental results on the KDDcup99 and Network Security Laboratory-Knowledge Discovery and Data Mining datasets showed that the proposed feature selection methods have a higher detection and accuracy and lower false-positive rate compared with the Pairwise linear correlation coefficient and the pairwise MI employed in several previous algorithms.
A cross-comparison of feature selection algorithms on multiple cyber security data-sets.
TL;DR: Recommendations of potential machine learning and feature selection algorithms that can be used to obtain a desirable level of accuracy whilst significantly reducing the total processing time are developed.
References
More filters
Proceedings ArticleDOI
A detailed analysis of the KDD CUP 99 data set
TL;DR: A new data set is proposed, NSL-KDD, which consists of selected records of the complete KDD data set and does not suffer from any of mentioned shortcomings.
Proceedings ArticleDOI
Evaluating intrusion detection systems: the 1998 DARPA off-line intrusion detection evaluation
Richard P. Lippmann,D. J. Fried,Isaac Graf,J.W. Haines,K. R. Kendall,D. J. McClung,D. J. Weber,Seth Webster,D. Wyschogrod,Robert K. Cunningham,M.A. Zissman +10 more
TL;DR: In this paper, an intrusion detection evaluation test bed was developed which generated normal traffic similar to that on a government site containing 100's of users on 1000's of hosts, and more than 300 instances of 38 different automated attacks were launched against victim UNIX hosts in seven weeks of training data and two weeks of test data.
Proceedings ArticleDOI
Cost-based modeling for fraud and intrusion detection: results from the JAM project
TL;DR: There is clear evidence that state-of-the-art commercial fraud detection systems can be substantially improved in stopping losses due to fraud by combining multiple models of fraudulent transaction shared among banks.
Journal ArticleDOI
A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems
TL;DR: A new feature-selection approach based on the cuttlefish optimization algorithm which is used for intrusion detection systems (IDSs) gives a higher detection rate and accuracy rate with a lower false alarm rate, when compared with the obtained results using all features.
Journal ArticleDOI
An Implementation of Intrusion Detection System Using Genetic Algorithm
TL;DR: An Intrusion Detection System (IDS) by applying genetic algorithm (GA) to efficiently detect various types of network intrusions is presented in this paper, which uses evolution theory to information evolution in order to filter the traffic data and thus reduce the complexity.
Related Papers (5)
Genetic algorithm with different feature selection method for intrusion detection
Nimmy Cleetus,K A Dhanya +1 more