An Overview of Anomaly Detection

Sample, Char; Schaffer, Kim

doi:10.1109/mitp.2013.7

Cited by 13 publications

(6 citation statements)

References 3 publications

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“…An NIDS gathers information from various network sources and does automatic analysis of signs of any intrusions or misuses and triggers alarms for the network administrators. Figure [1] shows the taxonomy of an NIDS. For any IDS sensors detect intrusions, analysis of the intrusion is done and appropriate response is generated.…”

Section: Network Intrusion Detection Systemsmentioning

confidence: 99%

“…We have enlisted few of the important works here: suffer from scalability issues when applied to large network traffic traces [1], [35].…”

Section: Network Based Anomaly Detectionmentioning

confidence: 99%

“…Due to its infrastructure weaknesses and architecture vulnerabilities on one hand and its humongous success on the other hand it has become a heaven for malicious users. Network intrusions or cyber intrusions have become one of the top most problems for system administrators, network engineers as well as researchers [1]. The need for better solutions combined with interestingness of this area has led to development and deployment of intrusion detection algorithms in general and Network Intrusion Detection Systems (NIDSs) in particular.…”

Section: Introductionmentioning

confidence: 99%

“…Anomaly detection is based on tagging of all network traffic behavior patterns that are abnormal for an entity [1], whereas signature based detection is based on tagging an event anomalous that has resemblance to previously profiled signature of a known intrusion. The weakness of signature based detection systems is that they always require a profile to be matched for detecting an intrusion or attack whereas anomaly based detection systems declare anything going beyond a threshold as a possible candidate for an attack or anomaly.…”

Section: Introductionmentioning

confidence: 99%

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Study of Self-Similarity for Detection of Rate-based Network Anomalies

Kaur¹,

Saxena²,

Gupta³

2017

IJSIA

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Section: Network Intrusion Detection Systemsmentioning

confidence: 99%

“…We have enlisted few of the important works here: suffer from scalability issues when applied to large network traffic traces [1], [35].…”

Section: Network Based Anomaly Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Study of Self-Similarity for Detection of Rate-based Network Anomalies

Kaur¹,

Saxena²,

Gupta³

2017

IJSIA

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“…With cloud computing taking many of its concepts from grid computing and cluster technology, research was carried out into anomaly detection and the different approaches that are currently being used in existing technologies [9] [4]. Investigating different areas that need to be considered from metrics selection [10] [11] to machine learning [12] [13].…”

Section: Related Workmentioning

confidence: 99%

Truesource: A True Performance for Hierarchical Cloud Monitoring

Currie

McConnell

Parr

et al. 2014

2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing

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Monitoring application performance over IT infrastructure is common practise for cloud providers, and is the foundation for ensuring the health of systems and quality of service for users. However, with the architecture of cloud computing allowing the allocation and provision of resources to be set at several layers, the scope for potential performance issues is increased and as a result increases the time and effort required to detect any issues. Cloud implementations often mean a reduction in IT staff, who are traditionally responsible for problem solving. Therefore monitoring must become automatic, dynamic and proactive compared to responsive and traditional 'fire fighting' approaches. In this paper, we look at the hierarchy of cloud computing and its effects on monitoring. With Cloud computing having several levels where resources can be allocated, as a result these can also form restrictions on the system, this increases the number of problem areas that support teams have to monitor, to avoid adverse effects on other parts of the cloud architecture where conflicts for resources can occur between applications and at different segments of the hierarchy.

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Identifying data streams anomalies by evolving spiking restricted Boltzmann machines

Xing

Demertzis

Yang

2019

Neural Comput & Applic

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Data streams are characterized by high volatility, and they drastically change in an unpredictable way over time. In the typical case, newer data are the most important, as the concept of aging is based on their timing. These flows require realtime processing in order to extract meaningful information that will allow for essential and targeted responses to changing circumstances. Knowledge mining is a real-time process performed on a subset of the data streams, which contains a small but recent part of the observations. Timely security requirements call for further quest of optimal approaches, capable of improving the reliability and the accuracy of the employed classifiers. This research introduces a real-time evolving spiking restricted Boltzmann machine approach, for efficient anomaly detection in data streams. Testing has proved that the proposed algorithm maximizes the classification accuracy and at the same time minimizes the computational resources requirements. A comparative analysis has shown that it outperforms other data flow analysis algorithms.Keywords Big Data Á Data streams analysis Á Evolving spiking neural networks Á Restricted Boltzmann machines Á Deep learning Á Real-time anomaly detection

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An Overview of Anomaly Detection

Cited by 13 publications

References 3 publications

Study of Self-Similarity for Detection of Rate-based Network Anomalies

Study of Self-Similarity for Detection of Rate-based Network Anomalies

Truesource: A True Performance for Hierarchical Cloud Monitoring

Identifying data streams anomalies by evolving spiking restricted Boltzmann machines

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