Enhance Intrusion Detection (IDS) System Using Deep SDAE to Increase Effectiveness of Dimensional Reduction in Machine Learning and Deep Learning

doi:10.22266/ijies2022.0831.13

Cited by 7 publications

(10 citation statements)

References 29 publications

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“…In [37], the authors presented an enhanced intrusion detection model using deep SDAE to rise the efficiency of dimensional reduction in their model. They train their model with different learning algorithms RNN-LSTM, Decision Trees, Naïve Bayes, and SVMs over the NSL-KDD dataset.…”

Section: Ensemble Deep Learning Classificationmentioning

confidence: 99%

“…To evaluate the performance of the proposed model, simulation experiments have been conducted based on a group of benchmark datasets. According to [36][37], datasets such as KDD99, NSL-KDD, RPL-NIDDS17, and ISCX, realized a limited number of attacks, which are also outdated. Therefore, we have adopted N-BaIoT and UNSW-NB15 as a recent powerful benchmark datasets for evaluating our proposed model.…”

Section: Simulation Experimentsmentioning

confidence: 99%

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An Efficient Intrusion Detection Approach Using Ensemble Deep Learning models for IoT

2023

IJIES

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The internet of things (IoT) has gained great importance due to its applicability in various daily life applications and its flexible and scalable framework. The wide and spreading use of IoT in the last few years has attracted intruders, who were able to take advantage of the vulnerabilities of any IoT framework due to the absence of robust security protocols. This discourages current and probable investors. Out-of-date intrusion detection models are mainly developed to support information technology systems using built-in, predefined patterns or highly imbalanced datasets. Over the past decade, deep learning models have outperformed traditional machine learning models in attack detection tasks. The biggest challenge in detecting zero-day attacks is determining the best deep-learning classifier. Numerous research initiatives have combined ensemble learning to improve performance, avoid overfitting, and minimize errors. In this work, to address this research gap, we propose a new enhanced meta-learning ensemble deep learning model based on stacking that combines the baseline deep learning models using two tiers of meta-classifiers. Then, conducting several experiments on two recent huge-size different IoT benchmark datasets to evaluate the performance of the proposed model. Different baseline deep learning classifiers are trained for each dataset, and their performance is compared to the proposed ensemble model. The findings show that the proposed ensemble model significantly enhances the classification accuracy of baseline deep-learning models.

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Section: Ensemble Deep Learning Classificationmentioning

confidence: 99%

Section: Simulation Experimentsmentioning

confidence: 99%

An Efficient Intrusion Detection Approach Using Ensemble Deep Learning models for IoT

2023

IJIES

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“…In this study, attention approach used to advance document context of product document representation. In several network security research, involvement of deep learning achieved effectiveness in attack detection [23]. The adoption of attention mechanism in the study success to increase product document understanding.…”

Section: Introductionmentioning

confidence: 98%

Enhance Rating Prediction for E-commerce Recommender System Using Hybridization of SDAE, Attention Mechanism and Probabilistic Matrix Factorization

2022

IJIES

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E-commerce is essential application in world wide. In everyday live, we cannot escape from e-commerce transaction. E-commerce requires intelligent machine to deliver product information to customer. Intelligent machine popular called recommender system developed by matrix factorization. Rating is representation of customer expression for satisfied product or service. Unfortunately, number of ratings is too sparse due to majority customer lazy to give rating for e-commerce product. Number of sparse rating matrix have impact in matrix factorization in rating prediction. Moreover, extreme sparse rating has impact degrade performance significantly. Many researchers consider to enhance matrix factorization using customer and product information such as customer demographics information, customer testimony, product review, product description, and etc. In this research, we consider to incorporating Stack Denoising Auto Encoder (SDAE), attention mechanism aims to enhance product review document understanding representation and matrix factorization based on Probabilistic Matric Factorization (PMF) to produce rating prediction. According to experiment report, our model superior over previous work based on hybrid model between PMF, Convolutional Neural Network (CNN) and SDAE that popular called PHD in 1%, and superior over hybrid model between PMF, Long Short Term Memory (LSTM) and SDAE that popular called DDL-PMF in 0.9%, and achieved significantly in 8% over traditional PMF based on RMSE evaluation metrics.

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“…Moreover, the scope and depth of security technologies for computers and networks continue to increase in response to the evolving nature of the threats they face. The intrusion detection system is the most crucial of these aspects [1]. Intrusion detection systems (IDSs) are essential to any comprehensive security architecture.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, numerous deep learning strategies have been applied to intrusion detection due to the constant growth of big data and the rise in computing capacity. Since deep learning can effectively process large data sets and extract meaningful features from unprocessed data, it has become a focal point of study for many academics, involving traditional machine learning and RNN model [3] [1].…”

Section: Introductionmentioning

confidence: 99%

IDSX-Attention: Intrusion detection system (IDS) based hybrid MADE-SDAE and LSTM-Attention mechanism

Hanafi

Pranolo

Mao

et al. 2023

Int. J. Adv. Intell. Informatics

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An Intrusion Detection System (IDS) is essential for automatically monitoring cyber-attack activity. Adopting machine learning to develop automatic cyber attack detection has become an important research topic in the last decade. Deep learning is a popular machine learning algorithm recently applied in IDS applications. The adoption of complex layer algorithms in the term of deep learning has been applied in the last five years to increase IDS detection effectiveness. Unfortunately, most deep learning models generate a large number of false negatives, leading to dominant mistake detection that can affect the performance of IDS applications. This paper aims to integrate a statistical model to remove outliers in pre-processing, SDAE, responsible for reducing data dimensionality, and LSTM-Attention, responsible for producing attack classification tasks. The model was implemented into the NSL-KDD dataset and evaluated using Accuracy, F1, Recall, and Confusion metrics measures. The results showed that the proposed IDSX-Attention outperformed the baseline model, SDAE, LSTM, PCA-LSTM, and Mutual Information (MI)-LSTM, achieving more than a 2% improvement on average. This study demonstrates the potential of the proposed IDSX-Attention, particularly as a deep learning approach, in enhancing the effectiveness of IDS and addressing the challenges in cyber threat detection. It highlights the importance of integrating statistical models, deep learning, and dimensionality reduction mechanisms to improve IDS detection. Further research can explore the integration of other deep learning algorithms and datasets to validate the proposed model's effectiveness and improve the performance of IDS.

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Enhance Intrusion Detection (IDS) System Using Deep SDAE to Increase Effectiveness of Dimensional Reduction in Machine Learning and Deep Learning

Cited by 7 publications

References 29 publications

An Efficient Intrusion Detection Approach Using Ensemble Deep Learning models for IoT

An Efficient Intrusion Detection Approach Using Ensemble Deep Learning models for IoT

Enhance Rating Prediction for E-commerce Recommender System Using Hybridization of SDAE, Attention Mechanism and Probabilistic Matrix Factorization

IDSX-Attention: Intrusion detection system (IDS) based hybrid MADE-SDAE and LSTM-Attention mechanism

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