Software-Based Platform for Education and Training of DDoS Attacks Using Virtual Networks

Fuertes, Walter; Tunala, Anabel; Moncayo, Ronnie; Meneses, Fausto; Toulkeridis, Theofilos

doi:10.1109/icssa.2017.19

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Early detection plays a fundamental role in preventing the fatal impact of DDoS attacks on server resources. One of the basic actions taken to prevent DDoS attacks is to install an Intrusion Detection System (IDS) on the server to monitor the flow of data packets that enter the internal network or vice versa [3]. Detection techniques in common IDS are far from perfect when compared to a variety of modern techniques and tools used by attackers because IDS still uses signature-based detection or anomaly-based detection models [4].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Distributed Denial of Service Attack Detection on Intrusion Detection System Regarding to Feature Selection

Muhammad¹,

Foozy

Azhari

2020

Int. J. Artif. Intell. Res

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Distributed Service Denial (DDoS) is a type of network attack, which each year increases in volume and intensity. DDoS attacks also form part of the major types of cyber security threats so far. Early detection plays a key role in avoiding the catastrophic effects on server infrastructure from DDoS attacks. Detection techniques in the traditional Intrusion Detection System (IDS) are far from perfect compared to a number of modern techniques and tools used by attackers, because the traditional IDS only uses signature-based detection or anomaly-based detection models and causes a lot of false positive flags, since the flow of computer network data packets has complex properties in terms of both size and source. Based on the deficiency in the ordinary IDS, this study aims to detect DDoS attacks by using machine learning techniques to enhance IDS policy development. According to the experiment the selection of features plays an important role in the precision of the detection results and in the performance of machine learning in classification problems. The combination of seven key selected dataset features used as an input neural network classifier in this study provides the highest accuracy value at 97.76%.

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Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Distributed Denial of Service Attack Detection on Intrusion Detection System Regarding to Feature Selection

Muhammad¹,

Foozy

Azhari

2020

Int. J. Artif. Intell. Res

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“…Deteksi dini memegang peran fundamental dalam mencegah dampak fatal serangan DDoS terhadap sumberdaya server. Salah satu tindakan dasar yang dilaksanakan untuk mencegah serangan DDoS adalah memasang Intrusion Detection System (IDS) pada server untuk memonitor aliran paket data yang masuk dalam jaringan internal ataupun sebaliknya (Fuertes et al, 2017). Teknik deteksi pada common IDS/IPS jauh dari sempurna apabila dibandingkan dengan variasi teknik dan tools modern yang digunakan oleh attacker karena IDS masih menggunakan model signature-based detection ataupun anomaly-based detection (Chowdhury, 2017).…”

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Impact of Feature Selection Methods on Machine Learning-based for Detecting DDoS Attacks : Literature Review

Faiz¹,

Somantri²,

Supriyono³

et al. 2022

JITE

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Cybersecurity attacks are becoming increasingly sophisticated and increasing with the development of technology so that they present threats to both the private and public sectors, especially Denial of Service (DoS) attacks and their variants which are often known as Distributed Denial of Service (DDoS). One way to minimize this attack is by using traditional mitigation solutions such as human-assisted network traffic analysis techniques but experiencing some limitations and performance problems. To overcome these limitations, Machine Learning (ML) has become one of the main techniques to enrich, complement and enhance the traditional security experience. The way ML works are based on the process of data collection, training and output. ML is influenced by several factors, one of which is feature engineering. In this study, we focus on the literature review of several recent studies which show that the feature selection process greatly impacts the level of accuracy of this ML. Datasets such as KDD, UNSW-NB15 and others also affect the level of accuracy of ML. Based on this literature review, this study can observe several feature engineering strategies with relevant impacts that can be chosen to improve ML solutions on DDoS attacks.

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Emulation of Denial-of-Service Attacks for Software Defined Networks

Großmann,

Weinmann

2024

Communications in Computer and Information Science

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Software-Based Platform for Education and Training of DDoS Attacks Using Virtual Networks

Cited by 3 publications

References 17 publications

Machine Learning-Based Distributed Denial of Service Attack Detection on Intrusion Detection System Regarding to Feature Selection

Machine Learning-Based Distributed Denial of Service Attack Detection on Intrusion Detection System Regarding to Feature Selection

Impact of Feature Selection Methods on Machine Learning-based for Detecting DDoS Attacks : Literature Review

Emulation of Denial-of-Service Attacks for Software Defined Networks

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