Comparison of Data Mining Algorithm: PSO-KNN, PSO-RF, and PSO-DT to Measure Attack Detection Accuracy Levels on Intrusion Detection System

Budilaksono, Sularso; Riyadi, Andri Agung; Azhari, Lukman; Saputra, Dedi Dwi; Suwarno, Muhammad Anno; Suwartane, I Gede Agus; Jupriyanto,; Ramadhan, Andika; Utomo, Agus Prasetyo; Fauzi, Achmad

doi:10.1088/1742-6596/1471/1/012019

Cited by 9 publications

(6 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The major problem is the increase in exponentially the search space with features numbers in the data set [8]. k-nearest neighbor (kNN) [9] is among the most widely used methods in classification systems [10], [11], [12], [13]. Swarm intelligence (SI) algorithms can successfully solve several problems.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Slime Mould Algorithm Combined With K-Nearest Neighbor for Medical Classification Tasks

et al. 2021

View full text Add to dashboard Cite

Growing science and medical technologies have produced a massive amount of knowledge on different scales of biological systems. By processing various amounts of medical data, these technologies will increase the quality of disease detection and enhance the usability of health information systems. The integration of machine learning in computer-based diagnostic systems facilitates the early detection of diseases, enabling more productive treatments and prolonged survival rates. The slime mould algorithm (SMA) may have drawbacks, such as being trapped in minimal local regions and having an unbalanced exploitation and exploration phase. To overcome these limitations, this paper proposes ISMA, an improved version of the slime mould algorithm (SMA) hybridized with the opposition-based learning (OBL) strategy based on the k-nearest neighbor (kNN) classifier for the classification approach. Opposition-based learning improves global exploratory ability while avoiding premature convergence. The experimental results revealed the superiority of the proposed ISMA-kNN in various classification evaluation metrics, including accuracy, sensitivity, specificity, precision, F-score, G-mean, computational time, and feature selection (FS) size compared with the tunicate swarm algorithm (TSA), the marine predators algorithm (MPA), the chimp optimization algorithm (ChOA), the moth-flame optimization (MFO) algorithm, the whale optimization algorithm (WOA), the sine cosine algorithm (SCA), and the original SMA algorithm. Performance tests were run on the same maximum number of function evaluations (FEs) on nine UCI benchmark disease data sets with different feature sizes.

show abstract

Section: Introductionmentioning

confidence: 99%

An Efficient Slime Mould Algorithm Combined With K-Nearest Neighbor for Medical Classification Tasks

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The dataset consists of 41 features and a label to specify either normal or attack (and type of attack). The training phase involves 41 features and 494023 instances whereas the testing phase involves 41 features and 148206 instances [31]. Table 1 shows the training and testing instances for the KDD-CUP 99 dataset employed in this study.…”

Section: Dataset Descriptionmentioning

confidence: 99%

Particle Swarm Optimization Feature Extraction Technique for Intrusion Detection System

Ganesh

Sharma

Henge

2023

Preprint

View full text Add to dashboard Cite

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.

show abstract

“…The information includes feature sets and a label indicating whether it is an attack or a common attribute (and type of attack). In the learning phase, 41 characteristics and 494023 cases are utilised, whereas in the testing phase, 41 features and 148206 cases have been used [31]…”

Section: Dataset Descriptionmentioning

confidence: 99%

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

Sivagaminathan

Sharma

Henge

2023

Preprint

View full text Add to dashboard Cite

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

Comparison of Data Mining Algorithm: PSO-KNN, PSO-RF, and PSO-DT to Measure Attack Detection Accuracy Levels on Intrusion Detection System

Cited by 9 publications

References 5 publications

An Efficient Slime Mould Algorithm Combined With K-Nearest Neighbor for Medical Classification Tasks

An Efficient Slime Mould Algorithm Combined With K-Nearest Neighbor for Medical Classification Tasks

Particle Swarm Optimization Feature Extraction Technique for Intrusion Detection System

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

Contact Info

Product

Resources

About