Anomaly Detection in IoT Using Extended Isolation Forest

Panja, Subir; Patowary, Nituraj; Saha, Shyama Prasad; Nag, Amitava

doi:10.1007/978-3-031-22485-0_1

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) Isolation Forest1

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2023

2024

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Cited by 3 publications

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“…The IsolationForest 'isolates' observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature [14]. The overall working of isolation forest is as summed up in Figure 1 [13].…”

Section: ) Isolation Forestmentioning

confidence: 99%

Hybrid Model using LOF and iForest Algorithms for Detection of Insider Threats

Mahajan

2023

Preprint

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<p>Insider threats, is one of the most challenging threats in cyberspace, usually responsible for causing significant loss to organizations.The topic of insider threats has long been studied and many detection techniques were proposed to deal with insider threats. This paper focuses on using different anomaly detection algorithms- Locality Outlier Factor Algorithm and Isolation forest Algorithm and does a comparative analysis between their performance. A hybrid model incorporating advantages of both LOF Algorithm and IF Algorithm is proposed in this paper which gives better performance than the individual models for detecting insider threats. The hybrid model was able to achieve whooping 99.99\% accuracy while detecting insider threats.</p>

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Section: ) Isolation Forestmentioning

confidence: 99%