Semi-supervised Statistical Approach for Network Anomaly Detection

Aissa, Naila Belhadj; Gabsi, Mohamed

doi:10.1016/j.procs.2016.04.228

Cited by 32 publications

(11 citation statements)

References 10 publications

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“…The proposed strategy uses statistical methods to implement anomaly detection [34][35][36][37][38]. The underlying rationale is to avoid methodologies that involve training processes.…”

Section: Detecting Aging Phenomena Via Anomaly Detectionmentioning

confidence: 99%

Unsupervised Monitoring System for Predictive Maintenance of High Voltage Apparatus

et al. 2020

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The online monitoring of a high voltage apparatus is a crucial aspect for a predictive maintenance program. Partial discharges (PDs) phenomena affect the insulation system of an electrical machine and—in the long term—can lead to a breakdown, with a consequent, significant economic loss; wind turbines provide an excellent example. Embedded solutions are therefore required to monitor the insulation status. The paper presents an online system that adopts unsupervised methodologies for assessing the condition of the monitored machine in real time. The monitoring process does not rely on any prior knowledge about the apparatus; nonetheless, the method can identify the relevant drifts in the machine status. In addition, the system is specifically designed to run on low-cost embedded devices.

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“…The proposed strategy uses statistical methods to implement anomaly detection [34][35][36][37][38]. The underlying rationale is to avoid methodologies that involve training processes.…”

Section: Detecting Aging Phenomena Via Anomaly Detectionmentioning

confidence: 99%

Unsupervised Monitoring System for Predictive Maintenance of High Voltage Apparatus

et al. 2020

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show abstract

“…One of the major approaches to detecting cyber-attacks is intrusion detection. According to [4], intrusion detection is the process of identifying an intrusion or attack signature in a continuous flow of connections. Intrusion detection is achieved with the use of intrusion detection systems.…”

Section: Cyber-attack Detection Approachesmentioning

confidence: 99%

“…Similarly, [4] claims that semi-supervised machine learning approach models normal behaviour with the help of a pre-labelled dataset. Consequently, semi-supervised learning combines the power of both supervised and unsupervised learning approaches in the process of building a model for classifying new instances of a dataset.…”

Section: Semi-supervised Learning Approachmentioning

confidence: 99%

“…Furthermore, [4] proposed a two-stage semi-supervised statistical approach for detecting network anomalies. The technique builds a probabilistic model using prelabeled normal instances.…”

Section: Semi-supervised Learning Approachmentioning

confidence: 99%

“…This model is then used to evaluate deviation from the normal behaviour using a predetermined threshold. The second stage uses an iterative process to reduce the false alarm rate leveraging a similarity distance and dispersion rate of the initial classifications of the probabilistic model [4].…”

Section: Semi-supervised Learning Approachmentioning

confidence: 99%

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