Navigating the metric maze: a taxonomy of evaluation metrics for anomaly detection in time series

Sørbø, Sondre; Ruocco, Massimiliano

doi:10.1007/s10618-023-00988-8

Cited by 9 publications

(2 citation statements)

References 86 publications

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“…Out of total 972,781is normal and 812,814 is normal Distinct records and normal Reduction ratio is 16.44%. 3,925,650 include attack records, where 3,925,650 is attack distinct record and the ratio of attack reduction is 93.32%.the details are shown in the [84]. Statistics of redundant records in the KDD test set includes 311,027Original records where 77,289 record is Distinct and total Reduction ratio is about 75.15%.…”

Section: Nsl-kddmentioning

confidence: 99%

From Bytes to Insights: A Systematic Literature Review on Unraveling IDS Datasets for Enhanced Cybersecurity Understanding

Khanan,

Abdelgadir Mohamed,

Mohamed

et al. 2024

IEEE Access

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In the wake of the expanding digital realm, the imperative for robust cybersecurity measures has burgeoned significantly. This extensive investigation digs into the complicated realm of cybersecurity datasets, with the goal of improving our understanding and implementation of these critical tools. This study's comprehensive evaluation of 37 distinct datasets shows a complicated world in which no one dataset stands out as totally suitable for all uses. A precise balance must be struck between crucial dataset qualities such as diversity, authenticity, and usefulness. Using a complete assessment technique, this paper illuminates the challenges and possibilities that developers and researchers face in the field of cybersecurity datasets. Although some databases accurately identify certain forms of cyberattacks, their coverage may not include the whole range of cyber threats. On the other hand, datasets with a strong emphasis on accurate portrayal may forgo comprehensiveness or practical use. This intricacy is heightened by the dynamic and sophisticated nature of cyber threats, emphasizing the delicate balance required between accuracy and practicality. The study emphasizes the necessity of selecting datasets strategically and contextually for cybersecurity studies, with the goal of matching research objectives with the most appropriate dataset selections. Furthermore, it emphasizes the need of continual cooperation and innovation within the cybersecurity community in developing datasets that accurately represent the ever-changing nature of cyber threats. After analyzing 37 cybersecurity datasets, it is obvious that no one dataset can meet all of the field's unique demands, demonstrating the need of a flexible, adaptable, and developing dataset for intrusion detection systems (IDS). This inquiry offers a critical assessment of dataset characteristics and their related issues, providing essential insights for academics, professionals, and dataset creators, enabling the construction of a more resilient and adaptable cybersecurity infrastructure.

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Section: Nsl-kddmentioning

confidence: 99%

From Bytes to Insights: A Systematic Literature Review on Unraveling IDS Datasets for Enhanced Cybersecurity Understanding

Khanan,

Abdelgadir Mohamed,

Mohamed

et al. 2024

IEEE Access

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show abstract

“…These algorithms can analyze historical data, recognize patterns, and identify anomalies that may not be detectable using traditional methods [2,19]. It is necessary to create a taxonomy for various anomaly types to choose the appropriate techniques for anomaly detection [20].…”

Section: Introductionmentioning

confidence: 99%

Power electronics anomaly detection and diagnosis with machine learning and deep learning methods: A survey

Hossein Rahimighazvini,

Zeyad Khashroum,

Maryam Bahrami

et al. 2024

Int. J. Sci. Res. Arch.

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Power electronics pertains to the conception, regulation, and utilization of electronic power circuits to proficiently administer and transform electrical energy. Power electronics play a crucial role in maintaining the reliability, efficiency, and security of complex production systems. Also, increasingly important in various applications such as renewable energy systems, electric vehicles, and industrial automation. However, modern power electronics systems are vulnerable to both cyber and physical anomalies due to the integration of information and communication technologies. So far, different methods have been used to detect abnormalities. This survey provides an overview of the state-of-the-art in anomaly detection in power electronics using machine learning and deep learning methods. It highlights the potential of these techniques in addressing the growing complexity and vulnerability of power electronics systems.

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SoftED: Metrics for soft evaluation of time series event detection

Salles,

Lima,

Reis

et al. 2024

Computers & Industrial Engineering

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Navigating the metric maze: a taxonomy of evaluation metrics for anomaly detection in time series

Cited by 9 publications

References 86 publications

From Bytes to Insights: A Systematic Literature Review on Unraveling IDS Datasets for Enhanced Cybersecurity Understanding

From Bytes to Insights: A Systematic Literature Review on Unraveling IDS Datasets for Enhanced Cybersecurity Understanding

Power electronics anomaly detection and diagnosis with machine learning and deep learning methods: A survey

SoftED: Metrics for soft evaluation of time series event detection

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