An Efficient NIDPS with Improved Salp Swarm Feature Optimization Method

Alabrah, Amerah

doi:10.3390/app13127002

Cited by 5 publications

(4 citation statements)

References 40 publications

(45 reference statements)

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“…This approach was tested on the UNSW-NB15 and Kyoto datasets. Amerah [35] proposed a network intrusion detection and prevention system (NIDPS) framework, which commences with the normalization of the dataset, using the standard deviation and mean of feature columns. Subsequently, an improved salp swarm algorithm (ISSA) is utilized for automated feature selection.…”

Section: Related Workmentioning

confidence: 99%

Building a Cloud-IDS by Hybrid Bio-Inspired Feature Selection Algorithms Along With Random Forest Model

Bakro,

Kumar,

Husain

et al. 2024

IEEE Access

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The adoption of cloud computing has become increasingly widespread across various domains. However, the inherent security vulnerabilities of cloud computing pose significant risks to its overall safety. Consequently, intrusion detection systems (IDS) play a pivotal role in identifying malicious activities within a cloud system. The considerable volume of network traffic data may contain redundant and irrelevant features that can impact the classification performance of the classifier. In addition, the complexity and time consumption increase while processing such a substantial volume of data in the cloud intrusion detection process. To enhance the performance of the IDS, this study proposes a hybrid feature selection approach, combining two bio-inspired algorithms, namely the grasshopper optimization algorithm (GOA) and the genetic algorithm (GA). The combination of these two algorithms ensures a more efficient search for optimal solutions. A random forest (RF) classifier is trained using those optimal features. Moreover, the proposal addresses the challenge of imbalanced data by employing a hybrid approach: over-sampling the minority classes using an adaptive synthetic (ADASYN) algorithm, while implementing random under-sampling (RUS) for the majority class as needed. This integrated strategy significantly influences each category, enhancing the true positive rate (TPR) while minimizing the false positive rate (FPR), thus improving the overall system performance. The proposed approach was evaluated using three datasets: UNSW-NB15, CIC-DDoS2019, and CIC Bell DNS EXF 2021. The recorded accuracies for these datasets were 98%, 99%, and 92%, respectively. The hybrid feature selection-based IDS demonstrated superior performance in multi-class classification, along with exemplary results for individual classes within the datasets. The proposed strategy exhibited a marked superiority with the random forest classifier, especially when compared to other classifiers including SVM, LR, FLN, LSTM, AlexNet, DNN, DBN, DT, and XGBoost. Moreover, this performance remained consistent and commendable even when benchmarked against contemporary state-of-the-art methodologies across multiple evaluation metrics.

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Section: Related Workmentioning

confidence: 99%

Building a Cloud-IDS by Hybrid Bio-Inspired Feature Selection Algorithms Along With Random Forest Model

Bakro,

Kumar,

Husain

et al. 2024

IEEE Access

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“…This reflects the traffic compositions and online intrusions of a particular time, which can be reproducible, modified, and extensible. To be able to do this, training a deep learning model to identify anomalies from a given dataset is one of the initial stages of data preprocessing which we will be looking at in the preceding sections (Alabrah, 2023;Bakro et al, 2023). Anomaly-based systems are the most effective IDSs, due to the application of machine learning algorithms embedded within the system.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Metrics of an Intrusion Detection System Through Window-Based Deep Learning Models

Isiaka

2023

JDSIS

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Intrusion and Prevention technologies perform reliably in harsh conditions by fortifying many of the world’s highest security sites with few defects in high performance. This paper aims to contribute by designing an Intrusion/Preventive System using a window-based convolutional neural network (CNN), an integrated recurrent neural network (RNN) and autoencoders (AutoE) to detect and test the performance of the intrusion detection system. The data packets were converted to images where the pixels were used as input. The CNN architecture shows a three (3) layer model with high predictive performance. The result shows high performance on CNN as compared to both RNN and AutoE, CNN seems to resist overfitting more than the rest of the models. The future perspective would be to test the model on other standard methods such as support vector machine (SVM) and Dynamic Control Systems.

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“…Ahmed et al [26] demonstrated a unique thermoelectric cooling system installed on the roof of a car. They created an abstract, time-varying thermal model and ran an analysis on it.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling of Artificial Intelligence-Based Automated Climate Control with Energy Consumption Using Optimal Ensemble Learning on a Pixel Non-Uniformity Metro System

Justin,

Saleh,

Lashin

et al. 2023

Sustainability

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Climate control in a pixel non-uniformity metro system includes regulating the air, humidity, and temperature quality within metro trains and stations to ensure passenger comfort and safety. The climate control system in a PNU metro system combines intelligent algorithms, energy-efficient practices, and advanced technologies to make a healthy and comfortable environment for passengers while reducing energy consumption. The proposed an automated climate control using an improved salp swarm algorithm with an optimal ensemble learning technique examines the underlying factors, including indoor air temperature, wind direction, indoor air relative humidity, light sensor 1 (wavelength), return air relative humidity, supply air temperature, wind speed, supply air relative humidity, airflow rate, and return air temperature. Moreover, this new proposed technique applies ISSA to elect an optimal set of features. Then, the climate control process takes place using an ensemble learning approach comprising long short-term memory, gated recurrent unit, and recurrent neural network. Lastly, the Harris hawks optimization algorithm can be employed to adjust the hyperparameters related to the ensemble learning models. The extensive results demonstrated the supremacy of the proposed algorithms over other approaches to the climate control process on PNU metro systems.

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An Efficient NIDPS with Improved Salp Swarm Feature Optimization Method

Cited by 5 publications

References 40 publications

Building a Cloud-IDS by Hybrid Bio-Inspired Feature Selection Algorithms Along With Random Forest Model

Building a Cloud-IDS by Hybrid Bio-Inspired Feature Selection Algorithms Along With Random Forest Model

Performance Metrics of an Intrusion Detection System Through Window-Based Deep Learning Models

Modeling of Artificial Intelligence-Based Automated Climate Control with Energy Consumption Using Optimal Ensemble Learning on a Pixel Non-Uniformity Metro System

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