Four Decades of Data Mining in Network and Systems Management

Kalegele, Khamisi; Sasai, Kazuto; Takahashi, Hideyuki; Kobashi, Gén; Kinoshita, Tetsuo

doi:10.1109/tkde.2015.2426713

Cited by 15 publications

(6 citation statements)

References 90 publications

(179 reference statements)

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“…One of the possible solutions to address changing operational conditions and low frequency of faults is to incorporate the domain knowledge in the pattern detection process. For example, Kalegele et al [ 29 ] proposed utilisation of the domain knowledge of network management software systems by ETL (extract, transform, load) units during data mining. This enabled the unit to improve the value of the insight generated from system operational data.…”

Section: Related Workmentioning

confidence: 99%

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A Knowledge-Based Cognitive Architecture Supported by Machine Learning Algorithms for Interpretable Monitoring of Large-Scale Satellite Networks

Oyekan

Hutabarat

Turner

et al. 2021

Sensors

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Cyber–physical systems such as satellite telecommunications networks generate vast amounts of data and currently, very crude data processing is used to extract salient information. Only a small subset of data is used reactively by operators for troubleshooting and finding problems. Sometimes, problematic events in the network may go undetected for weeks before they are reported. This becomes even more challenging as the size of the network grows due to the continuous proliferation of Internet of Things type devices. To overcome these challenges, this research proposes a knowledge-based cognitive architecture supported by machine learning algorithms for monitoring satellite network traffic. The architecture is capable of supporting and augmenting infrastructure engineers in finding and understanding the causes of faults in network through the fusion of the results of machine learning models and rules derived from human domain experience. The system is characterised by (1) the flexibility to add new or extend existing machine learning algorithms to meet the user needs, (2) an enhanced pattern recognition and prediction through the support of machine learning algorithms and the expert knowledge on satellite infrastructure, (3) the ability to adapt to changing conditions of the satellite network, and (4) the ability to augment satellite engineers through interpretable results. An industrial real-life satellite case study is provided to demonstrate how the architecture could be used. A single blind experimental methodology was used to validate the results generated by our approach.

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Section: Related Workmentioning

confidence: 99%

“… Mind map of the concepts from the literature review used for anomaly detection [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. …”

Section: Figurementioning

confidence: 99%

A Knowledge-Based Cognitive Architecture Supported by Machine Learning Algorithms for Interpretable Monitoring of Large-Scale Satellite Networks

Oyekan

Hutabarat

Turner

et al. 2021

Sensors

View full text Add to dashboard Cite

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“…Since the 1990s, sophisticated intrusion detection systems have been made with the help of data mining. Data mining techniques in general, and machine learning techniques in particular, must be applied in five steps: selection, preprocessing, transformation, mining, and interpretation [3], [4]. Out of all the ways to find intrusions using data mining, these three important steps are the hardest.…”

Section: Introductionmentioning

confidence: 99%

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

Jasim

Gaata

2022

Bulletin EEI

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Network attacks of the distributed denial of service (DDoS) form are used to disrupt server replies and services. It is popular because it is easy to set up and challenging to detect. We can identify DDoS attacks on network traffic in a variety of ways. However, the most effective methods for detecting and identifying a DDoS attack are machine learning approaches. This attack is considered to be among the most dangerous internet threats. In order for supervised machine learning algorithms to function, there needs to be tagged network traffic data sets. On the other hand, an unsupervised method uses network traffic analysis to find assaults. In this research, the K-Means clustering algorithm was developed as a semi-supervised approach for DDoS classification. The proposed algorithm is trained and tested with the CICIDS2017 dataset. After using the proposed hybrid feature selection methods and applying multiple training, testing, and carefully sorting DDoS traffic through a series of experiments, the optimum 2 centroids were found to be DDoS and normal. The generated centroids can be used to classify network traffic. So the proposed method succeeded to cluster the network traffic to safe and theat.

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“…Machine learning approaches are typically employed in the four primary steps in the learning process (preprocessing, mining, transformation, and interpretation) [4], [5]. The multidimensional challenge's [6] complexity makes it difficult for classification techniques to accurately find PortScan attack [7].…”

mentioning

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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Four Decades of Data Mining in Network and Systems Management

Cited by 15 publications

References 90 publications

A Knowledge-Based Cognitive Architecture Supported by Machine Learning Algorithms for Interpretable Monitoring of Large-Scale Satellite Networks

A Knowledge-Based Cognitive Architecture Supported by Machine Learning Algorithms for Interpretable Monitoring of Large-Scale Satellite Networks

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

Machine learning-based PortScan attacks detection using OneR classifier

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