Use the ensemble methods when detecting DoS attacks in Network Intrusion Detection Systems

Thanh, Hoàng Ngoc; Lăng, Trần Văn

doi:10.4108/eai.29-11-2019.163484

Cited by 9 publications

(4 citation statements)

References 9 publications

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“…This allows them to generate traffic that is harmful to the system. Despite the fact that individual bot computers only transmit a little amount of bandwidth, the cumulative impact of this traffic could potentially degrade the availability of a service [15]. The protocols that are part of the Open Systems Interconnection (OSI) reference model are another target for perpetrators of cyberattacks [16].…”

Section: What Is Distributed Denial Of Service Attackmentioning

confidence: 99%

Advancements in Time Series-Based Detection Systems for Distributed Denial-of-Service (DDoS) Attacks: A Comprehensive Review

Qasim,

NSAIF

2024

BJN

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Distributed denial-of-service assaults, often known as DDoS attacks, pose a significant danger to the stability and security of the internet, particularly in light of the increasing number of devices that are linked to the internet. Intelligent detection systems are absolutely necessary in order to lessen the impact of distributed denial of service assaults. In this study, a comprehensive overview of recent research on intelligent approaches, such as Machine Learning (ML), Deep Learning (DL), and Artificial Intelligence (AI), is presented. The review focuses on the application of these techniques in the detection of Distributed Denial of Service (DDoS) assaults. In addition to providing a taxonomy and conceptual framework for DDoS mitigation, the study places particular emphasis on the application of time series data analysis for the detection of distributed denial of service attacks. A number of different intelligent techniques are investigated in this paper. Some of these techniques include clustering, deep reinforcement learning, graph neural networks, support vector machines, and others. For the purpose of performance evaluation, real datasets are utilized, and prospective future research areas in this area are explored.

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Section: What Is Distributed Denial Of Service Attackmentioning

confidence: 99%

Advancements in Time Series-Based Detection Systems for Distributed Denial-of-Service (DDoS) Attacks: A Comprehensive Review

Qasim,

NSAIF

2024

BJN

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“…Machine learning has surfaced as a potentially advantageous technique in anomaly detection of DDoS attacks. Most popular individual machine learning algorithms have been widely utilized for detecting and classifying DDoS attacks, however [12]- [14]. These algorithms are susceptible to errors caused by variance and bias.…”

Section: Theorical Basismentioning

confidence: 99%

Distributed denial of service attacks classification system using features selection and ensemble techniques

Bagdadi,

Messabih

2024

IJEECS

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Distributed denial-of-service (DDoS) attacks are expanding threat to online services and websites. These attacks overwhelm targets with traffic from multiple sources to exhaust resources and make services unavailable. The frequency of DDoS attacks exhibits an ongoing upward trajectory over time. This persistent escalation highlights the need for effective countermeasures. While machine learning approaches have been extensively investigated for binary classification of DDoS attacks, multi-class classification has received comparatively less examination in the literature despite its greater practical utility. In this paper, we propose an intrusion detection system for detecting and classifying DDoS attacks, based on two main axes: feature selection for selecting the best relevant features and ensemble learning technique for improving performance by combining weak learners. The proposed model has been trained and evaluated on the CICDDoS2019 dataset. Experimental evaluation demonstrates improved performance using a subset of 16 relevant features identified, with a test accuracy of 82.35% attained for discriminating between the 12 classes represented in the dataset. By aggregating attacks sharing common characteristics resulting in 7 classes, the approach achieves surpassing 97% accuracy. Additionally, a binary classification delineating benign and DDoS attacks attain 99.90% accuracy.

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“…Penggunaan beberapa algoritma machine learning dengan metode ensemble methods dapat memberikan hasil akurasi yang lebih baik dibandingkan menggunakan single klasifikasi. Hal ini dapat dilihat dari hasil penelitian dengan judul Use The Ensemble Methods When Detecting DoS Attacks in Network Intrusion Detection [6].…”

Section: Tinjauan Literatur 21 Pengembangan Intrusion Detection Systemsunclassified

Analisa Performa Penggunaan Feature Selection untuk Mendeteksi Intrusion Detection Systems dengan Algoritma Random Forest Classifier

2021

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Semakin penting koneksi data melalui Internet membuat kebutuhan akan keamanan jaringan data semakin meningkat. Salah satu tools yang penting adalah Intrusion detection systems (IDS). Salah satu hal yang menjadi masalah dari penggunaan IDS adalah performan kecepatan untuk mendeteksi data yang semakin banyak dalam waktu yang singkat. Dalam penelitian ini kami akan melakukan analisa perbandingan performa IDS menggunakan features selection dengan algoritma Random Forest Classifier yang disimulasikan pada dataset UNSW-NB15, yaitu dataset simulasi serangan pada jaringan network yang dikembangan oleh Nour Moustafa & Jill Slay dari University of New South Wales pada Australian Defence Force Academy. Tujuan dari penelitian ini adalah mempercepat waktu proses Intrusion detection systems dengan machile learning. Penelitian dilakukan dengan 2 tahap, yaitu tahap pertama tanpa features selection dan tahap kedua dengan features selection ExtraTreesClassifier. Masing-masing tahap dilakukan dengan beberapa kali pengujian dengan persentasi testing dan training data yang berbeda. Hasil penelitian menunjukan bahwa penggunaan features selection dapat mempercepat waktu proses pendeteksian dengan menggunakan Random Forest Classifier, walaupun ada sedikit penurun akurasi dibawah 1%.

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Use the ensemble methods when detecting DoS attacks in Network Intrusion Detection Systems

Cited by 9 publications

References 9 publications

Advancements in Time Series-Based Detection Systems for Distributed Denial-of-Service (DDoS) Attacks: A Comprehensive Review

Advancements in Time Series-Based Detection Systems for Distributed Denial-of-Service (DDoS) Attacks: A Comprehensive Review

Distributed denial of service attacks classification system using features selection and ensemble techniques

Analisa Performa Penggunaan Feature Selection untuk Mendeteksi Intrusion Detection Systems dengan Algoritma Random Forest Classifier

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