A new intrusion detection system based on Moth–Flame Optimizer algorithm

Alazab, Moutaz; Khurma, Ruba Abu; Awajan, Albara; Camacho, David

doi:10.1016/j.eswa.2022.118439

Cited by 38 publications

(17 citation statements)

References 47 publications

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“…In recent years, a new direction was utilized for the IDS by employing the power of the metaheuristic optimization algorithms adopted in different and complex engineering and optimization problems, including IDS. For example, Alazab et al [12] employed the moth-flame optimizer algorithm to build an IDS method. The MFO was a feature selection method that enhanced the classifier's performance (Decision Tree).…”

Section: Introductionmentioning

confidence: 99%

Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural Networks

Fatani

Dahou

Elaziz

et al. 2023

Sensors

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Intrusion detection systems (IDS) play a crucial role in securing networks and identifying malicious activity. This is a critical problem in cyber security. In recent years, metaheuristic optimization algorithms and deep learning techniques have been applied to IDS to improve their accuracy and efficiency. Generally, optimization algorithms can be used to boost the performance of IDS models. Deep learning methods, such as convolutional neural networks, have also been used to improve the ability of IDS to detect and classify intrusions. In this paper, we propose a new IDS model based on the combination of deep learning and optimization methods. First, a feature extraction method based on CNNs is developed. Then, a new feature selection method is used based on a modified version of Growth Optimizer (GO), called MGO. We use the Whale Optimization Algorithm (WOA) to boost the search process of the GO. Extensive evaluation and comparisons have been conducted to assess the quality of the suggested method using public datasets of cloud and Internet of Things (IoT) environments. The applied techniques have shown promising results in identifying previously unknown attacks with high accuracy rates. The MGO performed better than several previous methods in all experimental comparisons.

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Section: Introductionmentioning

confidence: 99%

Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural Networks

Fatani

Dahou

Elaziz

et al. 2023

Sensors

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show abstract

“…This study used the ratio of 70:30 for the training and the testing [51]. The 30% testing dataset is then passed into the proposed classi ers which are DT and KNN after which they classify the dataset into normal or anomalies [52,53]. Performance evaluation is conducted for the classi ers using the confusion matrix values to get the values for the performance metrics which include, accuracy, detection rate, precision, FPR, and so on.…”

Section: Proposed Systemmentioning

confidence: 99%

Particle Swarm Optimization Feature Extraction Technique for Intrusion Detection System

Ganesh

Sharma

Henge

2023

Preprint

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The task of ensuring cyber-security has grown increasingly challenging as the alarming expansion of computer connectivity and the large number of computer-related applications has expanded recently. It also requires a sufficient protection system against a variety of cyberattacks. Detecting discrepancies and risks in a computer network, as well as creating intrusion detection systems (IDS) to aid in cyber-security. Artificial intelligence (AI), specifically machine learning (ML) approaches, were used to create a practical data-driven intrusion detection system. Two alternative intrusion detection (ID) classification approaches were compared in this study, each with its own set of use cases. Before using the two classifiers for classification, the Particle Swarm Optimization (PSO) approach was used to reduce dimensionality. The classification approaches used to characterise network anomalies were studied in this study. PSO + ANN (Artificial neural network), PSO + Decision Tree (PSO+DT) and PSO + K-Nearest Neighbor (PSO+KNN) are the three classifiers used. The detection approaches' results were confirmed using the KDD-CUP 99 dataset. On the result of the implementation, success indicators like as specificity, recall, f1-score, accuracy, precision, and consistency were used on cyber-security databases for different types of cyber-attacks. The accuracy, detection rate (DR), and false-positive rate of the two classifiers were also compared to see which one outperforms the other (FPR). Finally, the system was compared to the IDS that was already in place. In terms of detecting network anomalies, the results reveal that PSO+ANN outperforms the PSO+KNN and PSO+DT classifier algorithms.

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“…For training and assessment, this study used a 70:30 ratio [51]. The proposed classifier, DT and KNN, are then fed the 30% test dataset, and they categorise the set of data as normal or anomalous [52,53]. In order to recalculate the values for performance indicators like correctness, greater accuracy, and other performance metrics, the co -variance values are employed in the measuring performance of the classifiers.…”

Section: Proposed Systemmentioning

confidence: 99%

Mechanism for Extracting Features Using Particle Swarm Optimization for Intrusion Detection Systems

Sivagaminathan

Sharma

Henge

2023

Preprint

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The task of ensuring cyber-security has grown increasingly challenging given the concerning expansion of Computing connection and furthermore, there are a large number of computer-related applications available. It also needs a strong defense mechanism towards various cyber-attacks. Identifying irregularities and dangers in such a computer Security measures (IDS) have been established to aid with information security. Particularly, ML approaches are a subset of artificial intelligence (ai). (AI), a useful data-driven anti - malware system was developed. Two alternative intrusion detection (ID) classification reaches were compared in this study, each with its own set of use cases. Before using the two classifiers for classification, the Particle Swarm Optimization (PSO) approach ware used for reduce dimensionality. The classification meets used to characterize network anomalies were studied in this study. PSO + ANN (Artificial neural network), PSO plus Decision Tree and PSO plus K-Nearest Neighbor are the three classifiers used. The Knowledge discovery in databases 99 datasets was used to corroborate the identification techniques' findings. On the result of the implementation, successful metrics like as the following metrics were used to analyze cyber-security databases for various kinds of cyber-attacks: specific, recall, f1-score, correctness, accuracy, and constancy. The two's respective precision, detection rate (DR), and totally bogus rate were also compared to see which one outperforms the other (FPR). The solution was then contrasted with the IDS that was already in place. In terms of detecting network anomalies, The outcomes show that PSO + ANN outperforms the PSO + KNN and PSO + DT classifier algorithms.

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A new intrusion detection system based on Moth–Flame Optimizer algorithm

Cited by 38 publications

References 47 publications

Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural Networks

Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural Networks

Particle Swarm Optimization Feature Extraction Technique for Intrusion Detection System

Mechanism for Extracting Features Using Particle Swarm Optimization for Intrusion Detection Systems

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