A Novel Anomaly-Based Intrusion Detection Model Using PSOGWO-Optimized BP Neural Network and GA-Based Feature Selection

Sheikhi, Saeid; Kostakos, Panos

doi:10.3390/s22239318

Cited by 8 publications

(4 citation statements)

References 29 publications

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“…When a model has these traits, it overfits, leading the model to identify noise in the data rather than underlying patterns. To address this issue, correlation analysis was done in order to identify strongly linked traits and delete one of them [47]. To reduce the dimensionality of the dataset and improve the performance of the model, the redundant feature was removed when two features were substantially related to one another and one of them was redundant.…”

Section: Correlation and Dropping Redundant Featuresmentioning

confidence: 99%

Random Forest-Based NIDS: Advancing Network Threat Detection

Darweesh,

Abdelaziz,

Radwan

et al. 2024

Preprint

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Network Intrusion Detection Systems (NIDS) are critical for protecting computer networks from unauthorized activities. Traditional NIDS rely on rule-based signatures, which can be limiting in detecting emerging threats. This study investigates the effectiveness of the random forest classifier in advancing NIDS capabilities through machine learning. Using the CICIDS-2017 dataset, the data is preprocessed to enhance its quality by removing redundancies. The methodology involves rigorous testing and analysis of the random forest classifier's performance, focusing on accuracy and detection rates compared to other machine learning models. Results demonstrate that by optimizing class weights and leveraging 15 key features, the random forest classifier achieves an outstanding 99.8% accuracy across various attack types. This research highlights the potential of machine learning to significantly enhance NIDS effectiveness, offering a robust defense mechanism against evolving cybersecurity threats in modern networks.

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Section: Correlation and Dropping Redundant Featuresmentioning

confidence: 99%

Random Forest-Based NIDS: Advancing Network Threat Detection

Darweesh,

Abdelaziz,

Radwan

et al. 2024

Preprint

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“…A new anomaly-based IDS model was [17] combining PSO with the grey wolf optimisation (GWO) model. Initially, the acquired dataset was mined for highly correlated characteristics using the GA-based technique to enhance this model's detection capability.…”

Section: Literature Surveymentioning

confidence: 99%

“…In order to identify malicious actions in the network, the ML pipeline was hosted on a server alongside the trained DNN model and is accessible through API. However, the accuracy of the model relied heavily on the training, and system uncertainty was significant.A new anomaly-based IDS model was[17] combining PSO with the grey wolf optimisation (GWO) model. Initially, the acquired dataset was mined for highly correlated characteristics using the GA-based technique to enhance this model's detection capability.…”

mentioning

confidence: 99%

Multi-Valued Neutrosophic Convolutional LSTM for Intrusion Detection

2023

IJIES

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Cyber security is an essential area of study because of the positive effects that networks have on modern society. As it becomes simpler for cybercriminals to launch novel assaults, the scale and complexity of today's networks continue to grow. Therefore, it is crucial to create an efficient instruction detection system (IDS) capable of constantly monitoring network traffic for suspicious activity and issuing alerts as necessary. Several researchers have employed machine learning (ML) and deep learning (DL) techniques to create an effective IDS. Amongst, an effective IDS detection model convolutional neural network (CNN)-long short term memory (LSTM extracts temporal and geographical aspects of network traffic to lower the false alarm rate (FAR) and raise the detection rate (DR). However, the epistemic uncertainty in intrusion data affect the efficiency of CNN-LSTM model. This paper handle uncertainty information by proposing a multivalued neutrosophic convolutional LSTM (MVN-ConvLSTM) which extracts different deep features. MVN-ConvLSTM uses a neutrosophic set (NS) which considers truth (𝑇), indeterminacy (𝐼), and falsity (𝐹) memberships of every features. First, the intrusion features are mapped to NS domain as six sets, positive degree of truth-membership (𝑇 𝐴 ), positive degree of falsity membership (𝐹 𝐴 ), Positive degree of indeterminate towards falsity and indeterminate towards truth membership (𝐼 𝐴 ), negative degree of truth-membership (𝑇 𝐵 ), negative degree of falsity membership (𝐼 𝐵 ), and negative degree of indeterminate towards falsity and indeterminate towards truth membership (𝐼 𝐵 ). Next, an MVN-ConvLSTM is generated with four parallel paths, two of which take input from 𝑇 and two from 𝐼, and a suitable combination of these paths is used to produce the output. The weights of the neural network are updated in four different directions at once using a back propagation approach. The efficiency of MVN-ConvLSTM to handle uncertainty in intrusion dataset is proved by comparing conventional CNN models which provides lower FAR and high DR. Finally, the test results show that the MVN-ConvLSTM achieves accuracy value of 94.63%, 92.84% and 91.84% on three different datasets CIC-IDS2018, WSN-DS and UNSW-NB15.

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“…In recent years, artificial intelligence has gradually emerged, underpinning systems such as the BP neural network, RBF neural network and SVM, which have gradually been applied to predict the hazard of water and mud inrush disasters in tunnels [7][8][9][10][11][12]. Although these methods are all used to determine the hazard of water inrush in tunnels, the performances of the models are different.…”

Section: Introductionmentioning

confidence: 99%

Hazard Prediction of Water Inrush in Water-Rich Tunnels Based on Random Forest Algorithm

Zhang,

Niu,

Wan

et al. 2024

Applied Sciences

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To prevent large-scale water inrush accidents during the excavation process of a water-rich tunnel, a method, based on a random forest (RF) algorithm, for predicting the hazard level of water inrush is proposed. By analyzing hydrogeological conditions, six factors were selected as evaluating indicators, including stratigraphic lithology, inadequate geology, rock dip angle, negative terrain area ratio, surrounding rock grade, and hydrodynamic zonation. Through the statistical analysis of 232 accident sections, a dataset of water inrush accidents in water-rich tunnels was established. We preprocessed the dataset by detecting and replacing outliers, supplementing missing values, and standardizing the data. Using the RF model in machine learning, an intelligent prediction model for the hazard of water inrush in water-rich tunnels was established through the application of datasets and parameter optimization processing. At the same time, a support vector machine (SVM) model was selected for comparison and verification, and the prediction accuracy of the RF model reached 98%, which is higher than the 87% of the SVM. Finally, the model was validated by taking the water inrush accident in the Yuanliangshan tunnel as an example, and the predicted results have a high degree of consistency with the actual hazard level. This indicates that the RF model has good performance when predicting water inrush in water-rich tunnels and that it can provide a new means by which to predict the hazard of water inrush in water-rich tunnels.

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A Novel Anomaly-Based Intrusion Detection Model Using PSOGWO-Optimized BP Neural Network and GA-Based Feature Selection

Cited by 8 publications

References 29 publications

Random Forest-Based NIDS: Advancing Network Threat Detection

Random Forest-Based NIDS: Advancing Network Threat Detection

Multi-Valued Neutrosophic Convolutional LSTM for Intrusion Detection

Hazard Prediction of Water Inrush in Water-Rich Tunnels Based on Random Forest Algorithm

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