Early detection of network element outages based on customer trouble calls

Deljac, Željko; Randić, Mirko; Krčelić, Gordan

doi:10.1016/j.dss.2015.02.014

Cited by 16 publications

(9 citation statements)

References 33 publications

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“…The authors stated that successfully identified Denial of Service (DoS) attacks in the network traffic. Furthermore, the authors in [11] combined four well-established detection approaches: timebased detection, Neyman-Pearson detection, Bayesian Network decision approach and early detection, to detect outages on telecommunication network. Finally, a non-linear correlation approach based on mutual information has been proposed by the authors in [12] in order to reduce the false positive alarms in the intrusion detection systems.…”

Section: Related Workmentioning

confidence: 99%

Combining Statistical and Machine Learning Techniques in IoT Anomaly Detection for Smart Homes

Spanos

Giannoutakis

Votis

et al. 2019

2019 IEEE 24th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)

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In this paper, a security solution is proposed for IoT smart homes based on constructing behavioral device templates. These templates are being calculated by combining statistical and machine learning techniques according to their network behavior, captured within a smart home. The statistical metrics generated are being processed in order to produce the appropriate features, which are then used for constructing clusters of devices. The main idea relies on the fact that during an abnormal event, the device will be moved away from the center of the cluster, generating an alert that can be further used for proposing mitigation actions. The methodology followed in the proposed approach is given in detail, while validation is performed on a real smart home dataset. This work is part of a transparent cyber security framework developed under EU H2020 Project GHOST.

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Section: Related Workmentioning

confidence: 99%

Combining Statistical and Machine Learning Techniques in IoT Anomaly Detection for Smart Homes

Spanos

Giannoutakis

Votis

et al. 2019

2019 IEEE 24th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)

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“…Depending on the situation there are numerous ways to analyze and detect anomalies [26]; classification based, clustering-based, or using statistical methods and information theory. Deljac et al in [26], [25], where they used a Bayesian network for the outage detection in telecommunication networks. Clustering-based techniques regard unsupervised learning engines, mainly for unlabeled data.…”

Section: Anomaly Detectionmentioning

confidence: 99%

Towards the Identification of Context in 5G Infrastructures

Symvoulidis

Tsoumas

Kyriazis

2019

Advances in Intelligent Systems and Computing

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The evolution of communication networks, bringing the fifth generation (5G) of mobile communications in the foreground, gives the vertical industries opportunities that were not possible until now. Flexible network and computing infrastructure management can be achieved, hence bringing more freedom to the service providers, to maximize the performance of their computing resources. Still, challenges regarding the orchestration of these resources may arise. For this reason, an engine that can recognize possible factors that affect the use of these resources and come up with solutions when needed in real-time, is required. In this paper we present a novel Complex Event Processing that is enriched with Machine Learning capabilities in order to be fully adaptive to its environment, as a solution for monitoring application components deployed in 5G infrastructures. The proposed engine utilizes Incremental DBSCAN to identify the normal behavior of the deployed services and adjust the rules accordingly.

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“…How to detect and defend against cyber attacks is a common concern for both the industry and the academia. There are some academic achievements made in machine learning based intrusion detection technology such as Bayesian, SVM, RBM, ANN [ 5–11 ] , and other important network defense frameworks such as ASA and NIST provide ideas for improving network security from different perspectives. However, with the frequent outbreak of cyber attacks, we found that a lot of computer resources are necessary for some cyber attacks, such as the distributed denial of service (DDoS), which is difficult for individuals to conduct.…”

Section: Introductionmentioning

confidence: 99%

HiAtGang: How to Mine the Gangs Hidden Behind DDoS Attacks

Zhu

QIU

Rao

et al. 2022

Chinese J of Electronics

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Identifying and determining behaviors of attack gangs is not only an advanced stage of the network security event tracing and analysis, but also a core step of large‐scale combat and punishment of cyber attacks. Most of the work in the field of distributed denial of service (DDoS) attack analysis has focused on DDoS attack detection, and a part of the work involves the research of DDoS attack sourcing. We find that very little work has been done on the mining and analysis of DDoS attack gangs. DDoS attack gangs naturally have the attributes of human community relations. We propose a framework named HiAtGang, in which we define the concept of the gang detection in DDoS attacks and introduce the community analysis technology into DDoS attack gang analysis. Different attacker clustering algorithms are compared and analyzed. Based on analysis results of massive DDoS attack events that recorded by CNCERT/CC (The National Computer Network Emergency Response Technical Team/Coordination Center of China), the effective gang mining and attribute calibration have been achieved. More than 250 DDoS attack gangs have been successfully tracked. Our research fills the gaps in the field of the DDoS attack gang detection and has supported CNCERT/CC in publishing “Analysis Report on DDoS Attack Resources” for three consecutive years and achieved a good practical effect on combating DDoS attack crimes.

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Early detection of network element outages based on customer trouble calls

Cited by 16 publications

References 33 publications

Combining Statistical and Machine Learning Techniques in IoT Anomaly Detection for Smart Homes

Combining Statistical and Machine Learning Techniques in IoT Anomaly Detection for Smart Homes

Towards the Identification of Context in 5G Infrastructures

HiAtGang: How to Mine the Gangs Hidden Behind DDoS Attacks

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