Research on Anomaly Detection Method Based on DBSCAN Clustering Algorithm

Deng, Dingsheng

doi:10.1109/isctt51595.2020.00083

Cited by 14 publications

(6 citation statements)

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“…Deng [13] compares DBSCAN and K-means clustering algorithms in the field outlier detection. It evaluates the efficiency and performance of DBSCAN clustering.…”

Section: Related Workmentioning

confidence: 99%

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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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“…Deng [13] compares DBSCAN and K-means clustering algorithms in the field outlier detection. It evaluates the efficiency and performance of DBSCAN clustering.…”

Section: Related Workmentioning

confidence: 99%

“…4. The first method [13] utilizes the DBSCAN clustering algorithm to identify anomalies in a fixed dataset, where the value of eps is manually set for the entire dataset. This method achieved a detection accuracy of 80%.…”

Section: Comparative Analysismentioning

confidence: 99%

Adaptive DBSCAN with Grey Wolf Optimizer for Botnet Detection

2023

IJIES

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“…This clustering method only aggregates areas with a sufficiently large density. Therefore this algorithm can effectively combat outliers [27,28]. The prototype clustering algorithm is more sensitive to outliers, and too many outliers in the power system operation data will have a greater impact on the clustering effect of k-Means++ [30].…”

Section: Dbscan Clustering Algorithmmentioning

confidence: 99%

“…This clustering method only aggregates areas with a sufficiently large density. Therefore this algorithm can effectively combat outliers [27, 28]. …”

Section: Algorithm Selection and Evaluation Analysismentioning

confidence: 99%

Multi‐level refined power system operation mode analysis: A data‐driven approach

Bai

Meng

et al. 2022

IET Generation Trans & Dist

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The high renewable penetration will cause the power system operation mode (PSOM) to change frequently. At present, the selection of PSOM mainly depends on the experience of relevant staff. However, selecting PSOM based on experience is difficult to comprehensively and elaborately analyze the possible PSOMs within a dispatching cycle, which inevitably invites troubles to the power system planning and dispatching. In this paper, a data‐driven analysis method is proposed, which integrates the techniques of preprocessing, dimensionality reduction, clustering, and data visualization of high‐dimensional data sets of power system operation. Simultaneously, qualitative and quantitative methods are used to analyze, evaluate the PSOM and their development laws. Finally, to verify the effectiveness of the proposed multi‐level refinement extraction method of PSOM, the normal power system operation mode (NPSOM), transition power system operation mode (TPSOM), extreme power system operation mode (EPSOM) are obtained and analyzed according to the real operation data of the urban power system. The abundant analysis results show that the proposed fine‐grained extraction method of PSOM is compelling, facilitating the power grid enterprise operation and planning.

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“…Anomaly detection is called unsupervised learning and is based on an assumption that the features of data anomalies are significantly different from those of normal instances. Among data scientists the process is also called outlier detection and machine learning domain offers a several different approaches for outlier detection (like, for example, densitybased [5], classification-based using Decision Trees [6] or neural networks [14]).…”

Section: Introductionmentioning

confidence: 99%

Multi-Label Classification for AIS Data Anomaly Detection Using Wavelet Transform

Szarmach

Czarnowski

2022

IEEE Access

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Thanks to the Automatic Identification System (AIS), ships and other maritime equipment are able to communicate with each other, for example, by sending information about their position. This solution allows for early collision detection when two or more ships are on a collision course. In the newer version of AIS, a satellite infrastructure is used to extend the communication range. Unfortunately, satellite AIS deals with so-called packet collision effect: since there is a problem with synchronizing AIS data coming from multiple terrestrial areas, a single satellite may receive several AIS messages at the same time and be unable to correctly process them, causing the data to get lost or garbled. In this article, a machine learning based framework for detecting the incorrect AIS data is presented. In this approach, after the first stage (clustering), a dedicated anomaly detection algorithm searches for damaged AIS messages and conducts multi-label classification (with Random Forest and wavelet transform) to decide which fields of such message requires further correction. The results of measuring the effectiveness of the proposed approach using real AIS data are presented.

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Research on Anomaly Detection Method Based on DBSCAN Clustering Algorithm

Cited by 14 publications

References 0 publications

Adaptive DBSCAN with Grey Wolf Optimizer for Botnet Detection

Adaptive DBSCAN with Grey Wolf Optimizer for Botnet Detection

Multi‐level refined power system operation mode analysis: A data‐driven approach

Multi-Label Classification for AIS Data Anomaly Detection Using Wavelet Transform

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