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Data stream clustering: A survey

A survey of data stream clustering algorithms is presented, providing a thorough discussion of the main design components of state-of-the-art algorithms and an overview of the usually employed experimental methodologies.
ACM (opens in a new tab)

Novelty detection in data streams

Different aspects of novelty detection in data streams, like the offline and online phases, the number of classes considered at each phase, the use of ensemble versus a single classifier, supervised and unsupervised approaches for the learning task, and how to deal with recurring classes are presented.
Springer (opens in a new tab)

MINAS: multiclass learning algorithm for novelty detection in data streams

This work proposes MINAS, an algorithm for ND in data streams, which deals with ND as a multiclass task, and presents a set of experiments carried out comparing MINAS and the main novelty detection algorithms found in the literature, using artificial and real data sets.
Springer (opens in a new tab)

Novelty detection algorithm for data streams multi-class problems

A new algorithm to address novelty detection in data streams multi-class problems, the MINAS algorithm is presented and experimental results show that MINAS is able to discover novelties in multi- class problems.
ACM (opens in a new tab)

Analysis of nematodes in coffee crops at different altitudes using aerial images

A methodology capable of detecting the presence of the nematode pest in coffee crops and also analyzing the behavior of this pest in several altitudes using aerial images is described.
IEEE (opens in a new tab)

IDSA-IoT: An Intrusion Detection System Architecture for IoT Networks

Results show that it is possible to identify new attack patterns soon after their emergence and to adapt the models in an efficient way and proposes IDSA-IoT, a novel implementation architecture, which combines the use of resources at the edge of the network and a public cloud.
IEEE (opens in a new tab)

Novelty detection for multi-label stream classification under extreme verification latency

Publisher (opens in a new tab)

Feature-Based Time Series Classification for Service Request Opening Prediction in the Telecom Industry

A machine learning (ML) based approach to reduce the number of SROs using feature-based time series extracted from network equipment’s signals, modeling the problem as a binary classification task.
Publisher (opens in a new tab)

Novelty Detection for Multi-Label Stream Classification

A new method called MultI-label learNing Algorithm for Data Streams with Binary Relevance transformation (MINAS-BR), which was able to adapt to different concept drift and concept evolution scenarios, obtaining superior or competitive performances in comparison to literature baselines.
IEEE (opens in a new tab)

Intrusion Detection over Network Packets using Data Stream Classification Algorithms

It is determined that inspecting packets individually renders similar performance to examining flows in several situations and that only a subset of the packets’ headers is sufficient for the classification.
IEEE (opens in a new tab)
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