Review on Log-Based Anomaly Detection Techniques

Raut, Pooja; Mishra, Akanksha; Rao, S. N. Thirumala; Kawoor, Saloni; Shelke, Sushila; Deore, Mahendra; Kumar, Vivek

doi:10.1007/978-981-16-7657-4_72

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…Recent research includes also the combination of supervised and unsupervised machine learning with domain knowledge, reducing the number of alerts by predicting anomalous log events based on that domain expertise [12]. It also includes the refinement of the algorithms from the perspective of anomaly scoring and anomaly decision using self-attention neural networks and data augmentation [13], and the improvement of the model within the training data selection, data grouping, class distribution, data noise, and early detection ability [11].…”

Section: Introductionmentioning

confidence: 99%

Runtime security monitoring by an interplay between rule matching and deep learning-based anomaly detection on logs

Antic

Costa

Černivec

et al. 2023

2023 19th International Conference on the Design of Reliable Communication Networks (DRCN)

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In the era of digital transformation the increasing vulnerability of infrastructure and applications is often tied to the lack of technical capability and the improved intelligence of the attackers. In this paper, we discuss the complementarity between static security monitoring of rule matching and an application of self-supervised machine-learning to cybersecurity. Moreover, we analyse the context and challenges of supply chain resilience and smart logistics. Furthermore, we put this interplay between the two complementary methods in the context of a self-learning and self-healing approach.

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Section: Introductionmentioning

confidence: 99%