Intrusion Detection System Based on Fast Hierarchical Deep Convolutional Neural Network

Mendonça, Robson V.; Teodoro, Arthur A. M.; Rosa, Renata Lopes; Saadi, Muhammad; Carrillo, Dick; Nardelli, Pedro H. J.

doi:10.1109/access.2021.3074664

Cited by 88 publications

(28 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The relationship between class variables s defines the Bayes theorem' and feature vectors t 1 and t 2 in Eq. ( 1)-( 5), [17,18].…”

Section: Proposed Methodologymentioning

confidence: 99%

Perspicacious Apprehension of HDTbNB Algorithm Opposed to Security Contravention

Shyla¹,

Bhatnagar²

2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

The exponential pace of the spread of the digital world has served as one of the assisting forces to generate an enormous amount of information flowing over the network. The data will always remain under the threat of technological suffering where intruders and hackers consistently try to breach the security systems by gaining personal information insights. In this paper, the authors proposed the HDTbNB (Hybrid Decision Tree-based Naïve Bayes) algorithm to find the essential features without data scaling to maximize the model's performance by reducing the false alarm rate and training period to reduce zero frequency with enhanced accuracy of IDS (Intrusion Detection System) and to further analyze the performance execution of distinct machine learning algorithms as Naïve Bayes, Decision Tree, K-Nearest Neighbors and Logistic Regression over KDD 99 dataset. The performance of algorithm is evaluated by making a comparative analysis of computed parameters as accuracy, macro average, and weighted average. The findings were concluded as a percentage increase in accuracy, precision, sensitivity, specificity, and a decrease in misclassification as 9.3%, 6.4%, 12.5%, 5.2% and 81%.

show abstract

“…The relationship between class variables s defines the Bayes theorem' and feature vectors t 1 and t 2 in Eq. ( 1)-( 5), [17,18].…”

Section: Proposed Methodologymentioning

confidence: 99%

Perspicacious Apprehension of HDTbNB Algorithm Opposed to Security Contravention

Shyla¹,

Bhatnagar²

2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…Deep-learning methods [62][63][64] have been used for several applications [65,66], such as classification, detection, and recognition of images. For Single Image Super-Resolution (SISR), a framework is proposed in [67], which learns the mapping from LR to HR image using three layers, patch representation, non-linear mapping and reconstruction.…”

Section: Framework Using Deep Learning Algorithmsmentioning

confidence: 99%

Light Field Image Quality Enhancement by a Lightweight Deformable Deep Learning Framework for Intelligent Transportation Systems

et al. 2021

Self Cite

View full text Add to dashboard Cite

Light field (LF) imaging has multi-view properties that help to create many applications that include auto-refocusing, depth estimation and 3D reconstruction of images, which are required particularly for intelligent transportation systems (ITSs). However, cameras can present a limited angular resolution, becoming a bottleneck in vision applications. Thus, there is a challenge to incorporate angular data due to disparities in the LF images. In recent years, different machine learning algorithms have been applied to both image processing and ITS research areas for different purposes. In this work, a Lightweight Deformable Deep Learning Framework is implemented, in which the problem of disparity into LF images is treated. To this end, an angular alignment module and a soft activation function into the Convolutional Neural Network (CNN) are implemented. For performance assessment, the proposed solution is compared with recent state-of-the-art methods using different LF datasets, each one with specific characteristics. Experimental results demonstrated that the proposed solution achieved a better performance than the other methods. The image quality results obtained outperform state-of-the-art LF image reconstruction methods. Furthermore, our model presents a lower computational complexity, decreasing the execution time.

show abstract

“…Attack type can be identified based on characteristics extract from the security attacks to prevent the attack in the network. An effective and fast security identification system is required in the network to secure the network [2]. Networks of wireless sensor networks (WSN) and internet of things (IoT) requires data security and need to secure the infrastructure from intrusions.…”

Section: Introductionmentioning

confidence: 99%

Autoencoder – Support Vector Machine – Grasshopper Optimization for Intrusion Detection System

2022

IJIES

View full text Add to dashboard Cite

An intrusion detection system monitors the networks and identifies the malware or suspicious activity in the network. Machine learning techniques were applied in the Intrusion detection system to improve its efficiency in the identifications. Imbalance data problem in intrusion detection affects the performance of identification and deep learning methods have overfitting problems. The autoencodersupport vector machinegrasshopper optimization (AE-SVM-GO) model is proposed to overcome the limitation of the overfitting problem in intrusion detection. The hybrid technique of AE-SVM-GO is applied to solve imbalance data problem and overfitting problem in intrusion detection. The autoencoder model is applied to generate the instances of minority classes to balance the dataset. The Grasshopper optimization performance hyper-parameter optimization in the SVM model to learn the features to adaptively set the parameter in classification. Four datasets such as UNSW-NB15, CICIDS2017, NSL-KDD, and Kyoto 2006+ dataset were used to test the proposed AE-SVM-GO model. The proposed AE-SVM-GO model has 95.3 % accuracy, whereas the existing convolutional recurrent neural network (CRNN) and SVM-naïve bayes model has 76.82 %, and 93.75 % accuracy respectively.

show abstract

Intrusion Detection System Based on Fast Hierarchical Deep Convolutional Neural Network

Cited by 88 publications

References 51 publications

Perspicacious Apprehension of HDTbNB Algorithm Opposed to Security Contravention

Perspicacious Apprehension of HDTbNB Algorithm Opposed to Security Contravention

Light Field Image Quality Enhancement by a Lightweight Deformable Deep Learning Framework for Intelligent Transportation Systems

Autoencoder – Support Vector Machine – Grasshopper Optimization for Intrusion Detection System

Contact Info

Product

Resources

About