K-Means clustering-based semi-supervised for DDoS attacks classification

Jasim, Mahdi Nsaif; Gaata, Methaq Talib

doi:10.11591/eei.v11i6.4353

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…This method achieved a detection accuracy of 80%. The second method [24] employs the k-Means clustering algorithm along with a set of classifiers to create a semi-supervised approach for detecting DDOS attacks. Three different centroids were manually selected for k-Means.…”

Section: Comparative Analysismentioning

confidence: 99%

Adaptive DBSCAN with Grey Wolf Optimizer for Botnet Detection

2023

IJIES

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As the number of devices linked to the Internet (IOT devices) has dramatically increased, botnet attacks are becoming one of the most serious threats on the Internet. Many studies have been proposed for botnet detection based on machine learning. However, most of these existing studies focus on offline botnet detection using supervised machine learning methods. Since botnet attacks are committed in real time, they require online detection. Also, classification may not be practical for IOT applications such as botnet detection for many reasons that will be discussed in this paper. In order to overcome this limitation in the existing models, we propose an online botnet detection technique using unsupervised hybrid DBSCAN-GWO architecture. In this model, DBSCAN's eps parameter is generated automatically for each data stream using grey wolf optimizer which searches for the optimum eps value to give the best clustering quality for each data stream adaptively. After finding clusters in each data stream, a comparison is made between the clusters depending on the difference between their values to find the botnet clusters for each data stream adaptively. This model is evaluated using N_BaIot datasets of six different IOT devices. The results show the efficiency of DBSCAN-GWO model in detecting botnet data in all datasets compared to the regular DBSCAN with 3 different eps values and OPTICS clustering algorithms, as the best accuracy reaches 98%, which is also compared to a number of existing techniques which are the semi-supervised K-means clustering algorithm of 79.60%, DBSCAN clustering algorithm of 80%, and clustering-based semi-supervised machine learning approach of 96.66% for detecting anomalies and DDOS attacks.

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Section: Comparative Analysismentioning

confidence: 99%

Adaptive DBSCAN with Grey Wolf Optimizer for Botnet Detection

2023

IJIES

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“…Reduced dimensionality and features that might not have strong discriminatory power for the classification task are achieved through this filtering process. In ( 1) is used to calculate the variance (V) of each characteristic [14]:…”

Section: The Variance Features Filteringmentioning

confidence: 99%

“…Experiment findings with publicly available datasets indicate that our method can detect slow port scans in 10 Gbps high-speed network with excellent accuracy and low memory consumption. 3 [14] For the classification of DDoS, a semi-supervised approach based on the K-means clustering algorithm was created. The proposed algorithm was tested and trained using the CICIDS2017 dataset.…”

Section: Appendix Table 1 Related Workmentioning

confidence: 99%

Machine learning-based PortScan attacks detection using OneR classifier

Kareem,

Jawad Kadhim Abood,

Ibrahim

2023

Bulletin EEI

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PortScan attacks are a common security threat in computer networks, where an attacker systematically scans a range of network ports on a target system to identify potential vulnerabilities. Detecting such attacks in a timely and accurate manner is crucial to ensure network security. Attackers can determine whether a port is open by sending a detective message to it, which helps them find potential vulnerabilities. However, the best methods for spotting and identifying port scanner attacks are those that use machine learning. One of the most dangerous online threats is PortScan attack, according to experts. The research is work on detection while improving detection accuracy. Dataset containing tags from network traffic is used to train machine learning techniques for classification. The JRip algorithm is trained and tested using the CICIDS2017 dataset. As a consequence, the best performance results for JRip-based detection schemes were 99.84%, 99.80%, 99.80%, and 0.09 ms for accuracy, precision, recall, F-score, and detection overhead, respectively. Finally, the comparison with current models demonstrated our model's proficiency and advantage with increased attack discovery speed.

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DDoS attack detection techniques in IoT networks: a survey

Pakmehr,

Aßmuth,

Taheri

et al. 2024

Cluster Comput

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The Internet of Things (IoT) is a rapidly emerging technology that has become more valuable and vital in our daily lives. This technology enables connection and communication between objects and devices and allows these objects to exchange information and perform intelligent operations with each other. However, due to the scale of the network, the heterogeneity of the network, the insecurity of many of these devices, and privacy protection, it faces several challenges. In the last decade, distributed DDoS attacks in IoT networks have become one of the growing challenges that require serious attention and investigation. DDoS attacks take advantage of the limited resources available on IoT devices, which disrupts the functionality of IoT-connected applications and services. This article comprehensively examines the effects of DDoS attacks in the context of the IoT, which cause significant harm to existing systems. Also, this paper investigates several solutions to identify and deal with this type of attack. Finally, this study suggests a broad line of research in the field of IoT security, dedicated to examining how to adapt to current challenges and predicting future trends.

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K-Means clustering-based semi-supervised for DDoS attacks classification

Cited by 6 publications

References 16 publications

Adaptive DBSCAN with Grey Wolf Optimizer for Botnet Detection

Adaptive DBSCAN with Grey Wolf Optimizer for Botnet Detection

Machine learning-based PortScan attacks detection using OneR classifier

DDoS attack detection techniques in IoT networks: a survey

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