scholarly journals An Intrusion Detection System Using Modified-Firefly Algorithm in Cloud Environment

2021 ◽  
Vol 13 (2) ◽  
pp. 77-93
Author(s):  
Partha Ghosh ◽  
Dipankar Sarkar ◽  
Joy Sharma ◽  
Santanu Phadikar
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Firefly Algorithm ◽  
Detection System ◽  
Feature Selection Method ◽  
Training Time ◽  
Cloud Data ◽  
Redundant Data ◽  
Modified Firefly Algorithm

The present era is being dominated by cloud computing technology which provides services to the users as per demand over the internet. Satisfying the needs of huge people makes the technology prone to activities which come up as a threat. Intrusion detection system (IDS) is an effective method of providing data security to the information stored in the cloud which works by analyzing the network traffic and informs in case of any malicious activities. In order to control high amount of data stored in cloud, data is stored as per relevance leading to distributed computing. To remove redundant data, the authors have implemented data mining process such as feature selection which is used to generate an optimum subset of features from a dataset. In this paper, the proposed IDS provides security working upon the idea of feature selection. The authors have prepared a modified-firefly algorithm which acts as a proficient feature selection method and enables the NSL-KDD dataset to consume less storage space by reducing dimensions as well as less training time with greater classification accuracy.

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 IDSFS: A Signature Based Intrusion Detection System with High Pertinent Feature Selection Method

2019 ◽  
Vol 8 (2) ◽  
pp. 25-31
Author(s):  
S. Latha ◽  
Sinthu Janita Prakash
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
False Positive Rate ◽  
Feature Selection Method ◽  
Selection Method ◽  
Positive Rate ◽  
Frame Work ◽  
Pertinent Feature

Securing a network from the attackers is a challenging task at present as many users involve in variety of computer networks. To protect any individual host in a network or the entire network, some security system must be implemented. In this case, the Intrusion Detection System (IDS) is essential to protect the network from the intruders. The IDS have to deal with a lot of network packets with different characteristics. A signature-based IDS is a potential tool to understand former attacks and to define suitable method to conquest it in variety of applications. This research article elucidates the objective of IDS with a mechanism which combines the network and host-based IDS. The benchmark dataset for DARPA is considered to generate the IDS mechanism. In this paper, a frame work IDSFS – a signature-based IDS with high pertinent feature selection method is framed. This frame work consists of earlier proposed Feature Selection method (HPFSM), Artificial Neural Network for classification of nodes or packets in the network, then the signatures or attack rules are configured by implementing Association Rule mining algorithm and finally the rules are restructured using a pattern matching algorithm-Aho-Corasick to ease the rule checking. The metrics like number of features, classification accuracy, False Positive Rate (FPR), Precision, Number of rules, Running Time and Memory consumption are checked and proved the proposed frame work’s efficiency.

A Comparative Study on the Feature Selection Techniques for Intrusion Detection System

2019 ◽  
Vol 8 (1) ◽  
pp. 42-47
Author(s):  
D. Selvamani ◽  
V. Selvi
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Comparative Study ◽  
Intrusion Detection System ◽  
Detection System ◽  
Feature Selection Method ◽  
Support Vector ◽  
Network Intrusion ◽  
Chi Square Analysis ◽  
Feature Selection Techniques

The Intrusion Detection System (IDS) can be used broadly for securing the network. Intrusion detection systems (IDS) are typically positioned laterally through former protecting safety automation, like access control and verification, as a subsequent line of resistance that guards data classifications. Feature selection is employed to diminish the number of features in various applications where data has more than hundreds of attributes. Essential or relevant attribute recognition has converted a vital job to utilize data mining algorithms efficiently in today world situations. This article describes the comparative study on the Information Gain, Gain Ratio, Symmetrical Uncertainty, Chi-Square analysis feature selection techniques with different Classification methods like Artificial Neural Network, Naïve Bayes and Support Vector Machine. In this article, different performance metrics has utilized to choose the appropriate Feature Selection method for better data classification in IDS.

scholarly journals Wrapper-Based Feature Selection Analysis For Semi-Supervised Anomaly Based Intrusion Detection System

2020 ◽  
Vol 5 (2) ◽  
pp. 32
Author(s):  
Andreas Jonathan Silaban ◽  
Satria Mandala ◽  
Erwid Jadied Mustofa
Keyword(s):  
Logistic Regression ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Feature Selection Method ◽  
Past Research ◽  
Selection Method ◽  
Improve Accuracy ◽  
Wrapper Feature Selection

<p>Intrusion Detection System (IDS) plays as a role in detecting various types of attacks on computer networks. IDS identifies attacks based on a classification data network. The result of accuracy was weak in past research. To solve this problem, this research proposes using a wrapper feature selection method to improve accuracy detection. Wrapper-Feature selection works in the preprocessing stage to eliminate features. Then it will be clustering using a semi-supervised method. The semi-supervised method divided into two steps. There are supervised random forest and unsupervised using Kmeans. The results of each supervised and unsupervised will be ensembling using linear and logistic regression. The combination of wrapper and semi-supervised will get the maximum result.</p>

Sign in / Sign up

Export Citation Format

Share Document