An approach to spacecraft anomaly detection problem using kernel feature space

Abstract Reed–Xiaoli detector (RXD) is recognized as the benchmark algorithm for image anomaly detection; however, it presents known limitations, namely the dependence over the image following a multivariate Gaussian model, the estimation and inversion of a high-dimensional covariance matrix, and the inability to effectively include spatial awareness in its evaluation. In this work, a novel graph-based solution to the image anomaly detection problem is proposed; leveraging the graph Fourier transform, we are able to overcome some of RXD’s limitations while reducing computational cost at the same time. Tests over both hyperspectral and medical images, using both synthetic and real anomalies, prove the proposed technique is able to obtain significant gains over performance by other algorithms in the state of the art.

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Effective Noise Removal and Unified Model of Hybrid Feature Space Optimization for Automated Cardiac Anomaly Detection Using Phonocardiogarm Signals

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp.2018.8461765 ◽

2018 ◽

Cited By ~ 1

Author(s):

Arijit Ukil ◽

Soma Bandyopadhyay ◽

Chetanya Puri ◽

Rituraj Singh ◽

Arpan Pal

Keyword(s):

Anomaly Detection ◽

Feature Space ◽

Noise Removal ◽

Unified Model ◽

Cardiac Anomaly

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Framing Early Alert of Struggling Students as an Anomaly Detection Problem: An Exploration

Proceedings of the 13th International Conference on Computer Supported Education ◽

10.5220/0010471900260035 ◽

2021 ◽

Author(s):

Eitel Lauría

Keyword(s):

Anomaly Detection ◽

Struggling Students ◽

Detection Problem ◽

Early Alert

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Detecting potential reference list manipulation within a citation network

10.21203/rs.3.rs-812039/v1 ◽

2021 ◽

Author(s):

Jonathan Wren ◽

Constantin Georgescu

Keyword(s):

Anomaly Detection ◽

Peer Review ◽

Gini Index ◽

Review Process ◽

Peer Review Process ◽

Citation Network ◽

Reference List ◽

Detection Problem ◽

Zipf's Law ◽

Academic Influence

Abstract Although citations are used as a quantifiable, objective metric of academic influence, references could be added to a paper to inflate the perceived influence of a body of research. This reference list manipulation (RLM) could take place during the peer-review process, or prior to it. Surveys have estimated how many people may have been affected by coercive RLM at one time or another, but it is not known how many authors engage in RLM, nor to what degree. By examining a subset of active, highly published authors (n = 20,803) in PubMed, we find the frequency of non-self citations (NSC) to one author coming from one paper approximates Zipf’s law, permitting the task to be approached statistically. Framed as an anomaly detection problem, higher confidence is gained the more outlier status is correlated across dimensions relative to non-outliers. We find the NSC Gini Index correlates highly with anomalous patterns across multiple RLM-related distributions. Between 81 (FDR < 0.05) and 231 (FDR < 0.10) authors are outliers on the curve, suggestive of chronic, repeated RLM. Approximately 16% of all authors may have engaged in RLM to some degree. Authors who use 18% or more of their references for self-citation are significantly more likely to have NSC Gini distortions, suggesting a potential willingness to coerce others to cite them.

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Towards Bio-Inspired Anomaly Detection Using the Cursory Dendritic Cell Algorithm

Algorithms ◽

10.3390/a15010001 ◽

2021 ◽

Vol 15 (1) ◽

pp. 1

Author(s):

Carlos Pinto ◽

Rui Pinto ◽

Gil Gonçalves

Keyword(s):

Dendritic Cell ◽

Anomaly Detection ◽

Production Systems ◽

Detection System ◽

Artificial Immune Systems ◽

Object Oriented ◽

Research Area ◽

Detection Problem ◽

Network Intrusion ◽

Dendritic Cell Algorithm

The autonomous and adaptable identification of anomalies in industrial contexts, particularly in the physical processes of Cyber-Physical Production Systems (CPPS), requires using critical technologies to identify failures correctly. Most of the existing solutions in the anomaly detection research area do not consider such systems’ dynamics. Due to the complexity and multidimensionality of CPPS, a scalable, adaptable, and rapid anomaly detection system is needed, considering the new design specifications of Industry 4.0 solutions. Immune-based models, such as the Dendritic Cell Algorithm (DCA), may provide a rich source of inspiration for detecting anomalies, since the anomaly detection problem in CPPS greatly resembles the functionality of the biological dendritic cells in defending the human body from hazardous pathogens. This paper tackles DCA limitations that may compromise its usage in anomaly detection applications, such as the manual characterization of safe and danger signals, data analysis not suitable for online classification, and the lack of an object-oriented implementation of the algorithm. The proposed approach, the Cursory Dendritic Cell Algorithm (CDCA), is a novel variation of the DCA, developed to be flexible and monitor physical industrial processes continually while detecting anomalies in an online fashion. This work’s contribution is threefold. First, it provides a comprehensive review of Artificial Immune Systems (AIS), focusing on AIS applied to the anomaly detection problem. Then, a new object-oriented architecture for the DCA implementation is described, enabling the modularity and abstraction of the algorithm stages into different classes (modules). Finally, the CDCA for the anomaly detection problem is proposed. The CDCA was successfully validated in two industrial-oriented dataset benchmarks for physical anomaly and network intrusion detection, the Skoltech Anomaly Benchmark (SKAB) and M2M using OPC UA. When compared to other algorithms, the proposed approach exhibits promising classification results. It was placed fourth on the SKAB scoreboard and presented a competitive performance with the incremental Dendritic Cell Algorithm (iDCA).

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