Anomaly Detection and Classification in Cellular Networks Using Automatic Labeling Technique for Applying Supervised Learning

Mamun, S M Abdullah Al; Valimaki, Juha

doi:10.1016/j.procs.2018.10.328

Cited by 25 publications

(17 citation statements)

References 11 publications

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“…Hybrid approaches are often used for anomaly detection. For example, Al-Mamuna and Valimaki [ 20 ] proposed a two-stage approach to anomaly detection for quality control in cellular networks. The first stage was to create a one-class SVM model to find outliers in the dataset of key performance indicators (KPIs) from all the cells (sectors of each 2G/3G/4G/5G base station).…”

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

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“…It can recall information for past periods of time and it can cope with lags between critical points in time series. Within this framework, in [15] Recurrent neural networks including LSTM networks consist of repetitive sequences of neural network modules in chain arrangement as shown in Fig. 2.…”

Section: Long Short-term Memorymentioning

confidence: 99%

“…Likewise, in 4-category FAP anomaly classification we reached accuracies more than 80% on the average which is even better than the average results reached in [2] where more easily detectable macro BSs are also involved in. Moreover, our study does not require any data regarding neighboring cells as in [7], and does not require any KPI data pre-processing as in [15], thus has a potential on being operated in run time applications for relatively reduced complexity.…”

Section: Aggregation Decisionmentioning

confidence: 99%

Anomaly Detection in Multi Tiered Cellular Networks Using LSTM and 1D-CNN

Oğuz

Kalaycıoğlu

2021

Preprint

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Self Organizing Networks (SONs) are considered as one of the key features for automation of network management in new generation of mobile communications. The upcoming fifth generation (5G) mobile networks are likely to offer new advancements for SON solutions. In SON concept, self-healing is a prominent task which comes along with cell outage detection and cell outage compensation. 5G networks are supposed to have ultra-dense deployments which makes cell outage detection critical and harder for network maintenance. Therefore, by imitating the ultra-dense multi-tiered scenarios regarding 5G networks, this study investigates femtocell outage detection with the help of Long Short- Term Memory (LSTM) and one-dimensional Convolutional Neural Networks (1D-CNN) by means of time sequences of Key Performance Indicator (KPI) parameters generated in user equipments. In proposed scheme, probable anomalies in femto access points (FAP) are detected and classified within a predetermined time sequence intervals. On the average, in more than 80% of the cases the outage states of the femtocells are correctly predicted among healthyand anomalous states.

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“…But this neural network has one major drawback, namely the vanishing gradient problem [31] [32]. To address this issue, more complex architecture has been chosen, namely, a neural network with a long short-term memory (LSTM) [33]. However, even the LSTM neural network in its standard form does not fit, since the network is unidirectional, and because the arguments of the function can be affected by both earlier and later operations in the program.…”

Section: What Neural Network To Choose?mentioning

confidence: 99%

Deep learning based automatic software defects detection framework

Chrenousov¹,

Savchenko²,

Osadchyi³

et al. 2019

TACS

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We present the VulDetect, a source code vulnerability detection system. This system uses deep learning methods to organizate rules for deciding whether a code fragment is vulnerable. This approach is an improvement of the approach proposed in VulDeePecker. The model uses the AST representation of the source code. We compared vulnerability detection results of both systems on the Bitcoin Core project.

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Anomaly Detection and Classification in Cellular Networks Using Automatic Labeling Technique for Applying Supervised Learning

Cited by 25 publications

References 11 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

Anomaly Detection in Multi Tiered Cellular Networks Using LSTM and 1D-CNN

Deep learning based automatic software defects detection framework

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