Saldırı Tespit Sistemi İçin İstifleme Topluluk Öğrenme Yaklaşımı

Uçar, Murat; Uçar, Emine; İncetaş, Mürsel Ozan

doi:10.29130/dubited.737211

Cited by 4 publications

(2 citation statements)

References 28 publications

(37 reference statements)

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“…The algorithms used are Decision Tree (DT), K Nearest Neighbor (KNN), Neural Network and Logistic Regression (LR) which are considered as base learner models and Support Vector Machine (SVM) which are considered as meta learner. Through the results, it is shown that stacking helps to improve the performance of intrusion detection systems, as the proposed DT-LR-ANN + SVM model achieved an accuracy of 84.32% (Ucar et al, 2021(Ucar et al, :1331(Ucar et al, -1336.…”

Section: Related Workmentioning

confidence: 99%

Training Multi-Layer Deep Neural Networks Using Hybrid Algorithms to Detect Network Intrusions

Alfarra¹

2021

TurkishStudies

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One of the challenges facing security analysts and administrators is to enable intrusion detection in network systems, preventing intruders from entering the network. Network intrusion detection is usually signature-based and plays an effective role in detecting only known attacks. However, there are concerns about unknown attacks and the difficulty arises here. Which requires creating a model that can detect network attacks by discovering anomalies in the network. Therefore, in this article, an approach to detecting attacks is presented by monitoring the behavior of data and training it to distinguish between network traffic, which is normal traffic, and traffic that is classified as an attack on the network. The known database was used NSL-KDD, a dataset that monitors network traffic including various types of network attacks and detects normal traffic, it also contains numerous patterns and features. It includes 41 features for each traffic and is divided into five classifications. The four types are classified as types of network attacks and the fifth as normal traffic. One of the difficulties and problems is the existence of a wide variety of features. This requires finding a way to extract features that can have a significant impact on the results to achieve greater accuracy in classifying the type of attack. For this reason, principal component analysis PCA technology has been used to reduce feature sizes. Six models were created which are hybrid algorithms to optimize the performance of a multi-layer neural network by training their weights to classify the attack type in the network. A two hidden layer deep neural network was designed, and their weights were trained by designing six hybrid models. These models are hybrid algorithms based on four optimization algorithms which are Genetic Algorithm, Artificial bee colony algorithm, firefly algorithm, and Jaya algorithm. Several metrics were used to evaluate the performance of the proposed models. This paper contributed to finding an effective network intrusion detection methodology and developing a deep neural network by building effective optimization algorithms. Where the proposed algorithms performed well with accuracy ranging from 77.177% to 85.077%. The proposed algorithms also achieved a low MSE loss rate close to zero with an MSE value of 9.99868e-05. Also, precision values range from 82 to 97%, and most of the proposed algorithms achieved high rates when calculating the sensitivity and specificity. The proposed models have contributed to finding a high-performance methodology for detecting intrusion on the network and contributed to proving that the hybrid algorithms used are effective and help to the optimization can be used in other studies.

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Section: Related Workmentioning

confidence: 99%

Training Multi-Layer Deep Neural Networks Using Hybrid Algorithms to Detect Network Intrusions

Alfarra¹

2021

TurkishStudies

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“…For an optimal classification result, UÇAR et al (13) they tested several algorithms as base predictors and the SVM model is used as meta-estimator for the different stacked models, the 4 stacked models are tested with NSLKDDTest+ and NSLKDDTest 21 data, the best prediction rate is scored for the model based on DT,ANN, LR as base predictors and SVM as meta-estimator, they achieved 90 % in the best case.…”

Section: Previous Work Of Machine Learning Applications In Intrusion ...mentioning

confidence: 99%

A proposed method for detecting network intrusion using an ensemble learning (stacking -voting) approach with unbalanced data

Anouar Bachar,

Omar EL Bannay

2024

Data and Metadata

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The use of computer networks has become necessary in most human activities. However, these networks are exposed to potential threats affecting the confidentiality, integrity, and availability of data. Nowadays, the security of computer networks is based on tools and software such as antivirus software. Among the techniques used for machine protection, firewalls, data encryption, etc., were mentioned. These techniques constitute the first phase of computer network security. However, they remain limited and do not allow for full network protection. In this paper, a Network Intrusion Detection System (NIDS) was proposed for binary classification. This model was based on ensemble learning techniques, where the base models were carefully selected in a first layer. Several machine learning algorithms were individually studied to choose the best ones based on multiple metrics, including calculation speed. The SMOTE technique was used to balance the data, and cross-validation was employed to mitigate overfitting issues. Regarding the approaches used in this research, a stacking and voting model was employed, trained, and tested on a UNSW-NB15 dataset. The stacking classifier achieved a higher accuracy of 96%, while the voting approach attained 95.6%.

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Performance Evaluation of Ensemble Classifiers for Anomaly Detection in IoT Environment

Vardhan,

Kumar,

Awasthi

2024

Lecture Notes in Electrical Engineering

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Saldırı Tespit Sistemi İçin İstifleme Topluluk Öğrenme Yaklaşımı

Cited by 4 publications

References 28 publications

Training Multi-Layer Deep Neural Networks Using Hybrid Algorithms to Detect Network Intrusions

Training Multi-Layer Deep Neural Networks Using Hybrid Algorithms to Detect Network Intrusions

A proposed method for detecting network intrusion using an ensemble learning (stacking -voting) approach with unbalanced data

Performance Evaluation of Ensemble Classifiers for Anomaly Detection in IoT Environment

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