An Adaptive Intrusion Detection Model based on Machine Learning Techniques

Omar, Salima; Ngadi, Md. Asri; Jebur, Hamid H.

doi:10.5120/11971-6640

Cited by 10 publications

(5 citation statements)

References 22 publications

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“…In this section, related works with an emphasis on the ML approach for IDS were examined and discussed. The following are some of these studies: Omar et al [9] suggested an amalgam ML approach for intrusion detection that combines unsupervised and supervised classi cation algorithms and employs K-means, fuzzy C-means, and GSA clustering procedures to achieve identical patterns of a handler's operation. The proposed method's detection accuracy is then improved by combining an SVM with a gravitational search procedure as an amalgam categorization.…”

Section: Literature Reviewmentioning

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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Section: Literature Reviewmentioning

confidence: 99%

Particle Swarm Optimization Feature Extraction Technique for Intrusion Detection System

Ganesh

Sharma

Henge

2023

Preprint

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“…Omar et al [9] the supervised and unsupervised classification methods are combined, the same patterns of a handler's action are achieved using K-means, filtering, and Gia segmentation approaches. This is really a hybrid method to machine learning for intrusion detection.…”

Section: Literature Reviewmentioning

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.

show abstract

“…In this section, related works with an emphasis on the ML approach for IDS were examined and discussed. The following are some of these studies: Omar et al [9] suggested an amalgam ML approach for intrusion detection that combines unsupervised and supervised classification algorithms and employs K-means, fuzzy C-means, and GSA clustering procedures to achieve identical patterns of a handler's operation. The proposed method's detection accuracy is then improved by combining an SVM with a gravitational search procedure as an amalgam categorization.…”

Section: Literature Reviewmentioning

confidence: 99%

An Optimization Feature Extraction Technique using Intrusion Detection System

2023

IRJMETS

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As the alarming growth of computer connectivity and the significant number of computer-related applications increased lately, the challenge of achieving cyber-security has become progressively difficult. It also needs an adequate defense mechanism against a variety of cyber attacks. Detecting inconsistencies and threats in a computer network, as well as designing intrusion detection systems (IDS) that can help with cyber-security. A useful data-driven intrusion detection system has been developed using artificial intelligence (AI), particularly machine learning (ML) techniques. In this study, two different classification techniques for intrusion detection (ID) with each having its unique use cases were compared. Particle Swarm Optimization (PSO) algorithm was employed for dimensionality reduction before employing the two classifiers for the classification procedure. This study considered the classification techniques to categorize the network anomalies. The two classifiers employed are PSO + Decision Tree (PSO+DT) and PSO + K-Nearest Neighbor (PSO+KNN). The results of the detection techniques were verified using KDD-CUP 99 dataset. The usefulness of success indicators such as specificity, recall, f1-score, accuracy, precision, and consistency on cyber-security databases for different categories of cyber-attacks was employed on the result of the implementation. The two classifiers were also compared to deduce which of the classifier achieves exceedingly in terms of accuracy, detection rate (DR), and false-positive rate (FPR). Finally, the system was compared with the existing IDS. The results show that PSO+KNN outperformed the PSO+DT classifier algorithm in terms of identifying network anomalies.

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An Adaptive Intrusion Detection Model based on Machine Learning Techniques

Cited by 10 publications

References 22 publications

Particle Swarm Optimization Feature Extraction Technique for Intrusion Detection System

Particle Swarm Optimization Feature Extraction Technique for Intrusion Detection System

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

An Optimization Feature Extraction Technique using Intrusion Detection System

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