Adaptive Jaya Optimization Technique for Feature Selection in NSL-KDD Data Set of Intrusion Detection System

2019 ◽  
Author(s):  
Thupakula Bhaskar ◽  
Tryambak Hiwarkar ◽  
K. Ramanjaneyulu
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Optimization Technique ◽  
Data Set

Intrusion Detection Using Back Propagation Neural Network and Quick Reduct Algorithms

2018 ◽  
pp. 317-325
Author(s):  
S. Vijaya Rani ◽  
G. N. K Suresh Babu
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Data Sets ◽  
Effective Technique ◽  
Data Set ◽  
Quick Reduct

It is a big challenge to safeguard a network and data due to various network threats and attacks in a network system. Intrusion detection system is an effective technique to negotiate the issues of network security by utilizing various network classifiers. It detects malicious attacks. The data sets available in the study of intrusion detection system were DARPA, KDD 1999 cup, NSL_KDD, DEFCON, ISCX-UNB, KDD 1999 cup data set is the best and old data set for research purpose on intrusion detection. The data is preprocessed, normalized and trained by BPN algorithm. Further the normalized data is discretized using Entropy discretization and feature selection carried out by quick reduct methods. After feature selection, the concerned feature from normalized data is processed through BPN for better accuracy and efficiency of the system.

scholarly journals A novel ensemble modeling for intrusion detection system

2020 ◽  
Vol 10 (2) ◽  
pp. 1963
Author(s):  
Pullagura Indira Priyadarsini ◽  
G. Anuradha
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Nearest Neighbor ◽  
Detection System ◽  
Distance Functions ◽  
Classification Model ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Set

Vast increase in data through internet services has made computer systems more vulnerable and difficult to protect from malicious attacks. Intrusion detection systems (IDSs) must be more potent in monitoring intrusions. Therefore an effectual Intrusion Detection system architecture is built which employs a facile classification model and generates low false alarm rates and high accuracy. Noticeably, IDS endure enormous amounts of data traffic that contain redundant and irrelevant features, which affect the performance of the IDS negatively. Despite good feature selection approaches leads to a reduction of unrelated and redundant features and attain better classification accuracy in IDS. This paper proposes a novel ensemble model for IDS based on two algorithms Fuzzy Ensemble Feature selection (FEFS) and Fusion of Multiple Classifier (FMC). FEFS is a unification of five feature scores. These scores are obtained by using feature-class distance functions. Aggregation is done using fuzzy union operation. On the other hand, the FMC is the fusion of three classifiers. It works based on Ensemble decisive function. Experiments were made on KDD cup 99 data set have shown that our proposed system works superior to well-known methods such as Support Vector Machines (SVMs), K-Nearest Neighbor (KNN) and Artificial Neural Networks (ANNs). Our examinations ensured clearly the prominence of using ensemble methodology for modeling IDSs. And hence our system is robust and efficient.

An intelligent flow-based and signature-based IDS for SDNs using ensemble feature selection and a multi-layer machine learning-based classifier

2020 ◽  
pp. 1-20
Author(s):  
K. Muthamil Sudar ◽  
P. Deepalakshmi
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Network Architecture ◽  
Detection System ◽  
Software Defined Networking ◽  
Control Plane ◽  
Control Logic ◽  
Data Plane

Software-defined networking is a new paradigm that overcomes problems associated with traditional network architecture by separating the control logic from data plane devices. It also enhances performance by providing a highly-programmable interface that adapts to dynamic changes in network policies. As software-defined networking controllers are prone to single-point failures, providing security is one of the biggest challenges in this framework. This paper intends to provide an intrusion detection mechanism in both the control plane and data plane to secure the controller and forwarding devices respectively. In the control plane, we imposed a flow-based intrusion detection system that inspects every new incoming flow towards the controller. In the data plane, we assigned a signature-based intrusion detection system to inspect traffic between Open Flow switches using port mirroring to analyse and detect malicious activity. Our flow-based system works with the help of trained, multi-layer machine learning-based classifier, while our signature-based system works with rule-based classifiers using the Snort intrusion detection system. The ensemble feature selection technique we adopted in the flow-based system helps to identify the prominent features and hasten the classification process. Our proposed work ensures a high level of security in the Software-defined networking environment by working simultaneously in both control plane and data plane.

scholarly journals DCNN-IDS : Deep Convolutional Neural Network based Intrusion Detection System

2020 ◽  
Author(s):  
Sriram Srinivasan ◽  
Shashank A ◽  
vinayakumar R ◽  
Soman KP
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Convolutional Neural Network ◽  
Intrusion Detection System ◽  
Input Data ◽  
Detection System ◽  
Deep Convolutional Neural Network ◽  
Traffic Data ◽  
Data Set ◽  
Research Problems

In the present era, cyberspace is growing tremendously and the intrusion detection system (IDS) plays a key role in it to ensure information security. The IDS, which works in network and host level, should be capable of identifying various malicious attacks. The job of network-based IDS is to differentiate between normal and malicious traffic data and raise an alert in case of an attack. Apart from the traditional signature and anomaly-based approaches, many researchers have employed various deep learning (DL) techniques for detecting intrusion as DL models are capable of extracting salient features automatically from the input data. The application of deep convolutional neural network (DCNN), which is utilized quite often for solving research problems in image processing and vision fields, is not explored much for IDS. In this paper, a DCNN architecture for IDS which is trained on KDDCUP 99 data set is proposed. This work also shows that the DCNN-IDS model performs superior when compared with other existing works.

scholarly journals A feature selection algorithm combining information gain and multi-objective genetic search for intrusion detection system

2021 ◽  
Vol 336 ◽  
pp. 08008
Author(s):  
Tao Xie
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection Rate ◽  
Information Gain ◽  
Detection System ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Genetic Search ◽  
Multi Objective

In order to improve the detection rate and speed of intrusion detection system, this paper proposes a feature selection algorithm. The algorithm uses information gain to rank the features in descending order, and then uses a multi-objective genetic algorithm to gradually search the ranking features to find the optimal feature combination. We classified the Kddcup98 dataset into five classes, DOS, PROBE, R2L, and U2R, and conducted numerous experiments on each class. Experimental results show that for each class of attack, the proposed algorithm can not only speed up the feature selection, but also significantly improve the detection rate of the algorithm.

scholarly journals Scrutinizing Attacks and Evaluating Performance Appraisal Parameters via Feature Selection in Intrusion Detection System

2021 ◽  
Author(s):  
Navroop Kaur ◽  
Meenakshi Bansal ◽  
Sukhwinder Singh S
Keyword(s):  
Feature Selection ◽  
Performance Evaluation ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Cyber Attacks ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Evaluation Parameters

Abstract In modern times the firewall and antivirus packages are not good enough to protect the organization from numerous cyber attacks. Computer IDS (Intrusion Detection System) is a crucial aspect that contributes to the success of an organization. IDS is a software application responsible for scanning organization networks for suspicious activities and policy rupturing. IDS ensures the secure and reliable functioning of the network within an organization. IDS underwent huge transformations since its origin to cope up with the advancing computer crimes. The primary motive of IDS has been to augment the competence of detecting the attacks without endangering the performance of the network. The research paper elaborates on different types and different functions performed by the IDS. The NSL KDD dataset has been considered for training and testing. The seven prominent classifiers LR (Logistic Regression), NB (Naïve Bayes), DT (Decision Tree), AB (AdaBoost), RF (Random Forest), kNN (k Nearest Neighbor), and SVM (Support Vector Machine) have been studied along with their pros and cons and the feature selection have been imposed to enhance the reading of performance evaluation parameters (Accuracy, Precision, Recall, and F1Score). The paper elaborates a detailed flowchart and algorithm depicting the procedure to perform feature selection using XGB (Extreme Gradient Booster) for four categories of attacks: DoS (Denial of Service), Probe, R2L (Remote to Local Attack), and U2R (User to Root Attack). The selected features have been ranked as per their occurrence. The implementation have been conducted at five different ratios of 60-40%, 70-30%, 90-10%, 50-50%, and 80-20%. Different classifiers scored best for different performance evaluation parameters at different ratios. NB scored with the best Accuracy and Recall values. DT and RF consistently performed with high accuracy. NB, SVM, and kNN achieved good F1Score.

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.

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