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Sharma RK, Issac B, Xin Q, Gadekallu TR, Nath K. Plant and Salamander Inspired Network Attack Detection and Data Recovery Model. SENSORS (BASEL, SWITZERLAND) 2023; 23:5562. [PMID: 37420729 DOI: 10.3390/s23125562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 04/24/2023] [Accepted: 06/01/2023] [Indexed: 07/09/2023]
Abstract
The number of users of the Internet has been continuously rising, with an estimated 5.1 billion users in 2023, which comprises around 64.7% of the total world population. This indicates the rise of more connected devices to the network. On average, 30,000 websites are hacked daily, and nearly 64% of companies worldwide experience at least one type of cyberattack. As per IDC's 2022 Ransomware study, two-thirds of global organizations were hit by a ransomware attack that year. This creates the desire for a more robust and evolutionary attack detection and recovery model. One aspect of the study is the bio-inspiration models. This is because of the natural ability of living organisms to withstand various odd circumstances and overcome them with an optimization strategy. In contrast to the limitations of machine learning models with the need for quality datasets and computational availability, bio-inspired models can perform in low computational environments, and their performances are designed to evolve naturally with time. This study concentrates on exploring the evolutionary defence mechanism in plants and understanding how plants react to any known external attacks and how the response mechanism changes to unknown attacks. This study also explores how regenerative models, such as salamander limb regeneration, could build a network recovery system where services could be automatically activated after a network attack, and data could be recovered automatically by the network after a ransomware-like attack. The performance of the proposed model is compared to open-source IDS Snort and data recovery systems such as Burp and Casandra.
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Affiliation(s)
- Rupam Kumar Sharma
- Department of Computer Science and Engineering, Rajiv Gandhi University, Itanagar 791112, India
| | - Biju Issac
- Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Qin Xin
- Faculty of Science and Technology, University of the Faroe Islands, Vestara Bryggja 15, FO-100 Tórshavn, Faroe Islands
| | - Thippa Reddy Gadekallu
- School of Information Technology and Engineering, Vellore Institute of Technology & Engineering, Vellore 632014, India
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos P.O. Box 36, Lebanon
- Zhongda Group, Haiyan County, Jiaxing 314312, China
- College of Information Science and Engineering, Jiaxing University, Jiaxing 314001, China
- Division of Research and Development, Lovely Professional University, Phagwara 144401, India
| | - Keshab Nath
- Department of Computer Science and Engineering, Indian Institute of Information Technology, Kottayam 686635, India
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Alkasassbeh M, Al-Haj Baddar S. Intrusion Detection Systems: A State-of-the-Art Taxonomy and Survey. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022. [DOI: 10.1007/s13369-022-07412-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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3
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Hybrid Feature Selection Method for Intrusion Detection Systems Based on an Improved Intelligent Water Drop Algorithm. CYBERNETICS AND INFORMATION TECHNOLOGIES 2022. [DOI: 10.2478/cait-2022-0040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Abstract
A critical task and a competitive research area is to secure networks against attacks. One of the most popular security solutions is Intrusion Detection Systems (IDS). Machine learning has been recently used by researchers to develop high performance IDS. One of the main challenges in developing intelligent IDS is Feature Selection (FS). In this manuscript, a hybrid FS for the IDS network is proposed based on an ensemble filter, and an improved Intelligent Water Drop (IWD) wrapper. The Improved version from IWD algorithm uses local search algorithm as an extra operator to increase the exploiting capability of the basic IWD algorithm. Experimental results on three benchmark datasets “UNSW-NB15”, “NLS-KDD”, and “KDDCUPP99” demonstrate the effectiveness of the proposed model for IDS versus some of the most recent IDS algorithms existing in the literature depending on “F-score”, “accuracy”, “FPR”, “TPR” and “the number of selected features” metrics.
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A novel method for intrusion detection in computer networks by identifying multivariate outliers and ReliefF feature selection. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07402-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Fuzzy Local Information and Bhattacharya-Based C-Means Clustering and Optimized Deep Learning in Spark Framework for Intrusion Detection. ELECTRONICS 2022. [DOI: 10.3390/electronics11111675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Strong network connections make the risk of malicious activities emerge faster while dealing with big data. An intrusion detection system (IDS) can be utilized for alerting suitable entities when hazardous actions are occurring. Most of the techniques used to classify intrusions lack the techniques executed with big data. This paper devised an optimization-driven deep learning technique for detecting the intrusion using the Spark model. The input data is fed to the data partitioning phase wherein the partitioning of data is done using the proposed fuzzy local information and Bhattacharya-based C-means (FLIBCM). The proposed FLIBCM was devised by combining Bhattacharya distance and fuzzy local information C-Means (FLICM). The feature selection was achieved with classwise info gained to select imperative features. The data augmentation was done with oversampling to make it apposite for further processing. The detection of intrusion was done using a deep Maxout network (DMN), which was trained using the proposed student psychology water cycle caviar (SPWCC) obtained by combining the water cycle algorithm (WCA), the conditional autoregressive value at risk by regression quantiles (CAViaR), and the student psychology-based optimization algorithm (SPBO). The proposed SPWCC-based DMN offered enhanced performance with the highest accuracy of 97.6%, sensitivity of 98%, and specificity of 97%.
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Kumar N, Kumar U. Artificial intelligence for classification and regression tree based feature selection method for network intrusion detection system in various telecommunication technologies. Comput Intell 2022. [DOI: 10.1111/coin.12500] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Neeraj Kumar
- Computer Science & Engineering Birla Institute of Technology, Mesra Ranchi Jharkhand India
| | - Upendra Kumar
- Department of Computer Science & Engineering Birla Institute of Technology, Mesra Ranchi Jharkhand India
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Wang W, Jian S, Tan Y, Wu Q, Huang C. Representation learning-based network intrusion detection system by capturing explicit and implicit feature interactions. Comput Secur 2022. [DOI: 10.1016/j.cose.2021.102537] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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8
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Halim Z, Yousaf MN, Waqas M, Sulaiman M, Abbas G, Hussain M, Ahmad I, Hanif M. An effective genetic algorithm-based feature selection method for intrusion detection systems. Comput Secur 2021. [DOI: 10.1016/j.cose.2021.102448] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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9
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Alazzam H, Sharieh A, Sabri KE. A lightweight intelligent network intrusion detection system using OCSVM and Pigeon inspired optimizer. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02621-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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10
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A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions. Artif Intell Rev 2021. [DOI: 10.1007/s10462-021-10037-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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11
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Kumar S, Pal AK, Islam SKH, Hammoudeh M. Secure and efficient image retrieval through invariant features selection in insecure cloud environments. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06054-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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12
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Zhou C, Song J, Zhou S, Zhang Z, Xing J. COVID-19 Detection Based on Image Regrouping and Resnet-SVM Using Chest X-Ray Images. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:81902-81912. [PMID: 34812395 PMCID: PMC8545189 DOI: 10.1109/access.2021.3086229] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 05/31/2021] [Indexed: 06/01/2023]
Abstract
As the COVID-19 spread worldwide, countries around the world are actively taking measures to fight against the epidemic. To prevent the spread of it, a high sensitivity and efficient method for COVID-19 detection is necessary. By analyzing the COVID-19 chest X-ray images, a combination method of image regrouping and ResNet-SVM was proposed in this study. The lung region was segmented from the original chest X-ray images and divided into small pieces, and then the small pieces of lung region were regrouped into a regular image randomly. Furthermore the regrouped images were fed into the deep residual encoder block for feature extraction. Finally the extracted features were as input into support vector machine for recognition. The visual attention was introduced in the novel method, which paid more attention to the features of COVID-19 without the interference of shapes, rib and other related noises. The experimental results showed that the proposed method achieved 93% accuracy without large number of training data, outperformed the existing COVID-19 detection models.
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Affiliation(s)
- Changjian Zhou
- Key Laboratory of Agricultural Microbiology of Heilongjiang ProvinceNortheast Agricultural UniversityHarbin150030China
- Department of Modern Educational TechnologyNortheast Agricultural UniversityHarbin150030China
| | - Jia Song
- Key Laboratory of Agricultural Microbiology of Heilongjiang ProvinceNortheast Agricultural UniversityHarbin150030China
| | - Sihan Zhou
- College of Electrical and InformationNortheast Agricultural UniversityHarbin150030China
| | - Zhiyao Zhang
- College of Electrical and InformationNortheast Agricultural UniversityHarbin150030China
| | - Jinge Xing
- Department of Modern Educational TechnologyNortheast Agricultural UniversityHarbin150030China
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Sekhar R, Sasirekha K, Raja PS, Thangavel K. A novel GPU based intrusion detection system using deep autoencoder with Fruitfly optimization. SN APPLIED SCIENCES 2021. [DOI: 10.1007/s42452-021-04579-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Abstract
Intrusion Detection Systems (IDSs) have received more attention to safeguarding the vital information in a network system of an organization. Generally, the hackers are easily entering into a secured network through loopholes and smart attacks. In such situation, predicting attacks from normal packets is tedious, much challenging, time consuming and highly technical. As a result, different algorithms with varying learning and training capacity have been explored in the literature. However, the existing Intrusion Detection methods could not meet the desired performance requirements. Hence, this work proposes a new Intrusion Detection technique using Deep Autoencoder with Fruitfly Optimization. Initially, missing values in the dataset have been imputed with the Fuzzy C-Means Rough Parameter (FCMRP) algorithm which handles the imprecision in datasets with the exploit of fuzzy and rough sets while preserving crucial information. Then, robust features are extracted from Autoencoder with multiple hidden layers. Finally, the obtained features are fed to Back Propagation Neural Network (BPN) to classify the attacks. Furthermore, the neurons in the hidden layers of Deep Autoencoder are optimized with population based Fruitfly Optimization algorithm. Experiments have been conducted on NSL_KDD and UNSW-NB15 dataset. The computational results of the proposed intrusion detection system using deep autoencoder with BPN are compared with Naive Bayes, Support Vector Machine (SVM), Radial Basis Function Network (RBFN), BPN, and Autoencoder with Softmax.
Article Highlights
A hybridized model using Deep Autoencoder with Fruitfly Optimization is introduced to classify the attacks.
Missing values have been imputed with the Fuzzy C-Means Rough Parameter method.
The discriminate features are extracted using Deep Autoencoder with more hidden layers.
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Diao L, Hu P. Deep learning and multimodal target recognition of complex and ambiguous words in automated English learning system. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-189543] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
On the basis of convolution neural network, deep learning algorithm can make the convolution layer convolute the input image to complete the hierarchical expression of feature information, which makes pattern recognition more simple and accurate. Now, in the theory of multimodal discourse analysis, the nonverbal features in communication are studied as a symbol system similar to language. In this paper, the author analyzes the deep learning complexity and multimodal target recognition application in English education system. Multimodal teaching gradually has its practical significance in the process of rich teaching resources. The large-scale application of multimedia technology in college English classroom is conducive to the construction of a real language environment. The simulation results show that the multi-layer and one-dimensional convolution structure of the product neural network can effectively complete many natural language problems, including the tagging of lexical and semantic roles, and thus effectively improve the accuracy of natural language processing. Multimodal teaching mode helps to memorize vocabulary images more deeply. 84% of students think that multi-modal teaching mode is closer to life. Meanwhile, multimedia teaching display is more acceptable. College English teachers should renew their teaching concepts and adapt themselves to the new teaching mode.
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Affiliation(s)
- Lijing Diao
- Cangzhou Normal University, Cangzhou, Hebei, China
| | - Ping Hu
- Cangzhou Normal University, Cangzhou, Hebei, China
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15
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Gu J, Lu S. An effective intrusion detection approach using SVM with naïve Bayes feature embedding. Comput Secur 2021. [DOI: 10.1016/j.cose.2020.102158] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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16
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Intrusion Detection System in the Advanced Metering Infrastructure: A Cross-Layer Feature-Fusion CNN-LSTM-Based Approach. SENSORS 2021; 21:s21020626. [PMID: 33477451 PMCID: PMC7830526 DOI: 10.3390/s21020626] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 01/14/2021] [Accepted: 01/14/2021] [Indexed: 11/16/2022]
Abstract
Among the key components of a smart grid, advanced metering infrastructure (AMI) has become the preferred target for network intrusion due to its bidirectional communication and Internet connection. Intrusion detection systems (IDSs) can monitor abnormal information in the AMI network, so they are an important means by which to solve network intrusion. However, the existing methods exhibit a poor ability to detect intrusions in AMI, because they cannot comprehensively consider the temporal and global characteristics of intrusion information. To solve these problems, an AMI intrusion detection model based on the cross-layer feature fusion of a convolutional neural networks (CNN) and long short-term memory (LSTM) networks is proposed in the present work. The model is composed of CNN and LSTM components connected in the form of a cross-layer; the CNN component recognizes regional features to obtain global features, while the LSTM component obtain periodic features by memory function. The two types of features are aggregated to obtain comprehensive features with multi-domain characteristics, which can more accurately identify intrusion information in AMI. Experiments based on the KDD Cup 99 and NSL-KDD datasets demonstrate that the proposed cross-layer feature-fusion CNN-LSTM model is superior to other existing methods.
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Kejia S, Parvin H, Qasem SN, Tuan BA, Pho KH. A classification model based on svm and fuzzy rough set for network intrusion detection. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-191621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Intrusion Detection Systems (IDS) are designed to provide security into computer networks. Different classification models such as Support Vector Machine (SVM) has been successfully applied on the network data. Meanwhile, the extension or improvement of the current models using prototype selection simultaneous with their training phase is crucial due to the serious inefficacies during training (i.e. learning overhead). This paper introduces an improved model for prototype selection. Applying proposed prototype selection along with SVM classification model increases attack discovery rate. In this article, we use fuzzy rough sets theory (FRST) for prototype selection to enhance SVM in intrusion detection. Testing and evaluation of the proposed IDS have been mainly performed on NSL-KDD dataset as a refined version of KDD-CUP99. Experimentations indicate that the proposed IDS outperforms the basic and simple IDSs and modern IDSs in terms of precision, recall, and accuracy rate.
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Affiliation(s)
- Shen Kejia
- The Second Affiliated Hospital of the Second Military Medical University, Shanghai City, China
| | - Hamid Parvin
- Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
- Faculty of Information Technology, Duy Tan University, Da Nang, Vietnam
- Department of Computer Science, Nourabad Mamasani Branch, Islamic Azad University, Mamasani, Iran
| | - Sultan Noman Qasem
- Computer Science Department, College of Computer and Information Sciences, AI Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
- Computer Science Department, Faculty of Applied Science, Taiz University, Taiz, Yemen
| | - Bui Anh Tuan
- Department of Mathematics Education, Teachers College, Can Tho University, Can Tho City, Vietnam
| | - Kim-Hung Pho
- Fractional Calculus, Optimization and Algebra Research Group, Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Hosseinzadeh M, Rahmani AM, Vo B, Bidaki M, Masdari M, Zangakani M. Improving security using SVM-based anomaly detection: issues and challenges. Soft comput 2020. [DOI: 10.1007/s00500-020-05373-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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19
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Nasiri JA, Mir AM. An enhanced KNN-based twin support vector machine with stable learning rules. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-04740-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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20
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Kushwah GS, Ranga V. Voting extreme learning machine based distributed denial of service attack detection in cloud computing. JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 2020. [DOI: 10.1016/j.jisa.2020.102532] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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21
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A Feature Selection Model for Network Intrusion Detection System Based on PSO, GWO, FFA and GA Algorithms. Symmetry (Basel) 2020. [DOI: 10.3390/sym12061046] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The network intrusion detection system (NIDS) aims to identify virulent action in a network. It aims to do that through investigating the traffic network behavior. The approaches of data mining and machine learning (ML) are extensively used in the NIDS to discover anomalies. Regarding feature selection, it plays a significant role in improving the performance of NIDSs. That is because anomaly detection employs a great number of features that require much time. Therefore, the feature selection approach affects the time needed to investigate the traffic behavior and improve the accuracy level. The researcher of the present study aimed to propose a feature selection model for NIDSs. This model is based on the particle swarm optimization (PSO), grey wolf optimizer (GWO), firefly optimization (FFA) and genetic algorithm (GA). The proposed model aims at improving the performance of NIDSs. The proposed model deploys wrapper-based methods with the GA, PSO, GWO and FFA algorithms for selecting features using Anaconda Python Open Source, and deploys filtering-based methods for the mutual information (MI) of the GA, PSO, GWO and FFA algorithms that produced 13 sets of rules. The features derived from the proposed model are evaluated based on the support vector machine (SVM) and J48 ML classifiers and the UNSW-NB15 dataset. Based on the experiment, Rule 13 (R13) reduces the features into 30 features. Rule 12 (R12) reduces the features into 13 features. Rule 13 and Rule 12 offer the best results in terms of F-measure, accuracy and sensitivity. The genetic algorithm (GA) shows good results in terms of True Positive Rate (TPR) and False Negative Rate (FNR). As for Rules 11, 9 and 8, they show good results in terms of False Positive Rate (FPR), while PSO shows good results in terms of precision and True Negative Rate (TNR). It was found that the intrusion detection system with fewer features will increase accuracy. The proposed feature selection model for NIDS is rule-based pattern recognition to discover computer network attack which is in the scope of Symmetry journal.
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Ayo FE, Folorunso SO, Abayomi-Alli AA, Adekunle AO, Awotunde JB. Network intrusion detection based on deep learning model optimized with rule-based hybrid feature selection. INFORMATION SECURITY JOURNAL: A GLOBAL PERSPECTIVE 2020. [DOI: 10.1080/19393555.2020.1767240] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Femi Emmanuel Ayo
- Department of Physical and Computer Sciences, McPherson University, Seriki Sotayo, Nigeria
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Abstract
Machine-learning techniques have received popularity in the intrusion-detection systems in recent years. Moreover, the quality of datasets plays a crucial role in the development of a proper machine-learning approach. Therefore, an appropriate feature-selection method could be considered to be an influential factor in improving the quality of datasets, which leads to high-performance intrusion-detection systems. In this paper, a hybrid multi-objective approach is proposed to detect attacks in a network efficiently. Initially, a multi-objective genetic method (NSGAII), as well as an artificial neural network (ANN), are run simultaneously to extract feature subsets. We modified the NSGAII approach maintaining the diversity control in this evolutionary algorithm. Next, a Random Forest approach, as an ensemble method, is used to evaluate the efficiency of the feature subsets. Results of the experiments show that using the proposed framework leads to better outcomes, which could be considered to be promising results compared to the solutions found in the literature.
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RDTIDS: Rules and Decision Tree-Based Intrusion Detection System for Internet-of-Things Networks. FUTURE INTERNET 2020. [DOI: 10.3390/fi12030044] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
This paper proposes a novel intrusion detection system (IDS), named RDTIDS, for Internet-of-Things (IoT) networks. The RDTIDS combines different classifier approaches which are based on decision tree and rules-based concepts, namely, REP Tree, JRip algorithm and Forest PA. Specifically, the first and second method take as inputs features of the data set, and classify the network traffic as Attack/Benign. The third classifier uses features of the initial data set in addition to the outputs of the first and the second classifier as inputs. The experimental results obtained by analyzing the proposed IDS using the CICIDS2017 dataset and BoT-IoT dataset, attest their superiority in terms of accuracy, detection rate, false alarm rate and time overhead as compared to state of the art existing schemes.
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Tavolato P, Schölnast H, Tavolato-Wötzl C. Analytical modelling of cyber-physical systems: applying kinetic gas theory to anomaly detection in networks. JOURNAL OF COMPUTER VIROLOGY AND HACKING TECHNIQUES 2020. [DOI: 10.1007/s11416-020-00349-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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26
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A new approach for intrusion detection system based on training multilayer perceptron by using enhanced Bat algorithm. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04655-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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27
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Zhang F, Wu TY, Pan JS, Ding G, Li Z. Human motion recognition based on SVM in VR art media interaction environment. HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES 2019. [DOI: 10.1186/s13673-019-0203-8] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Abstract
In order to solve the problem of human motion recognition in multimedia interaction scenarios in virtual reality environment, a motion classification and recognition algorithm based on linear decision and support vector machine (SVM) is proposed. Firstly, the kernel function is introduced into the linear discriminant analysis for nonlinear projection to map the training samples into a high-dimensional subspace to obtain the best classification feature vector, which effectively solves the nonlinear problem and expands the sample difference. The genetic algorithm is used to realize the parameter search optimization of SVM, which makes full use of the advantages of genetic algorithm in multi-dimensional space optimization. The test results show that compared with other classification recognition algorithms, the proposed method has a good classification effect on multiple performance indicators of human motion recognition and has higher recognition accuracy and better robustness.
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30
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A novel support vector regression algorithm incorporated with prior knowledge and error compensation for small datasets. Neural Comput Appl 2019. [DOI: 10.1007/s00521-018-03981-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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31
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A novel architecture combined with optimal parameters for back propagation neural networks applied to anomaly network intrusion detection. Comput Secur 2018. [DOI: 10.1016/j.cose.2018.01.023] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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32
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