Auto-Associative Neural Techniques for Intrusion Detection Systems

Herrero, Álvaro; Corchado, Emilio; Gastaldo, Paolo; Picasso, Francesco; Zunino, Rodolfo

doi:10.1109/isie.2007.4374898

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…Such heterogeneous sensors measure the internal and external temperatures and central processing unit (CPU) fan speed at each server. The bottom tier of our framework analyzes the relationship between the sensed data and workload information on a server using auto-associative neural networks (AANNs) 2 to detect small-scale thermal anomalies and also to perform a preliminary classification of anomalies based on the cause -misconfiguration or fan failure (the CRAC fan and/or server fans). Then, the top tier aggregates the detection results from different servers and determines whether there are small-or large-scale thermal anomalies.…”

Section: Introductionmentioning

confidence: 99%

Thermal anomaly detection in datacenters

Yang

Lee

Pompili

et al. 2011

Proceedings of the Institution of Mechanical Engineers, Part C:

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The high density of servers in datacenters generates a large amount of heat, resulting in the high possibility of thermally anomalous events, i.e. computer room air conditioner fan failure, server fan failure, and workload misconfiguration. As such anomalous events increase the cost of maintaining computing and cooling components, they need to be detected, localized, and classified for taking appropriate remedial actions. In this article, a hierarchical neural network framework is proposed to detect small- (server level) and large-scale (datacenter level) thermal anomalies. This novel framework, which is organized into two tiers, analyzes the data sensed by heterogeneous sensors such as sensors built in the servers and external sensors (Telosb). The proposed solution employs a neural network to learn about (a) the relationship among sensing values (i.e. internal, external, and fan speed) and (b) the relationship between the sensing values and workload information. Then, the bottom tier of our framework detects thermal anomalies, whereas the top tier localizes and classifies them. Our solution outperforms other anomaly-detection methods based on regression model, support vector machine, and self-organizing map, as shown by the experimental results.

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Section: Introductionmentioning

confidence: 99%

Thermal anomaly detection in datacenters

Yang

Lee

Pompili

et al. 2011

Proceedings of the Institution of Mechanical Engineers, Part C:

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Anomaly-Based Intrusion Detection Using Bayesian Networks

Tylman

2008

2008 Third International Conference on Dependability of Computer Systems DepCoS-RELCOMEX

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Deep Learning Approaches for Video Compression: A Bibliometric Analysis

Bidwe

Mishra

Patil

et al. 2022

BDCC

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Every data and kind of data need a physical drive to store it. There has been an explosion in the volume of images, video, and other similar data types circulated over the internet. Users using the internet expect intelligible data, even under the pressure of multiple resource constraints such as bandwidth bottleneck and noisy channels. Therefore, data compression is becoming a fundamental problem in wider engineering communities. There has been some related work on data compression using neural networks. Various machine learning approaches are currently applied in data compression techniques and tested to obtain better lossy and lossless compression results. A very efficient and variety of research is already available for image compression. However, this is not the case for video compression. Because of the explosion of big data and the excess use of cameras in various places globally, around 82% of the data generated involve videos. Proposed approaches have used Deep Neural Networks (DNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), and various variants of Autoencoders (AEs) are used in their approaches. All newly proposed methods aim to increase performance (reducing bitrate up to 50% at the same data quality and complexity). This paper presents a bibliometric analysis and literature survey of all Deep Learning (DL) methods used in video compression in recent years. Scopus and Web of Science are well-known research databases. The results retrieved from them are used for this analytical study. Two types of analysis are performed on the extracted documents. They include quantitative and qualitative results. In quantitative analysis, records are analyzed based on their citations, keywords, source of publication, and country of publication. The qualitative analysis provides information on DL-based approaches for video compression, as well as the advantages, disadvantages, and challenges of using them.

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Auto-Associative Neural Techniques for Intrusion Detection Systems

Cited by 3 publications

References 27 publications

Thermal anomaly detection in datacenters

Thermal anomaly detection in datacenters

Anomaly-Based Intrusion Detection Using Bayesian Networks

Deep Learning Approaches for Video Compression: A Bibliometric Analysis

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