SCADE: Scalable Framework for Anomaly Detection in High-Performance System
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913602077720576 |
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| author | Vinay, Vaishali Mangal, Anjali |
| author_facet | Vinay, Vaishali Mangal, Anjali |
| contents | As command-line interfaces remain integral to high-performance computing environments, the risk of exploitation through stealthy and complex command-line abuse grows. Conventional security solutions struggle to detect these anomalies due to their context-specific nature, lack of labeled data, and the prevalence of sophisticated attacks like Living-off-the-Land (LOL). To address this gap, we introduce the Scalable Command-Line Anomaly Detection Engine (SCADE), a framework that combines global statistical models with local context-specific analysis for unsupervised anomaly detection. SCADE leverages novel statistical methods, including BM25 and Log Entropy, alongside dynamic thresholding to adaptively detect rare, malicious command-line patterns in low signal-to-noise ratio (SNR) environments. Experimental results show that SCADE achieves above 98% SNR in identifying anomalous behavior while minimizing false positives. Designed for scalability and precision, SCADE provides an innovative, metadata-enriched approach to anomaly detection, offering a robust solution for cybersecurity in high-computation environments. This work presents SCADE's architecture, detection methodology, and its potential for enhancing anomaly detection in enterprise systems. We argue that SCADE represents a significant advancement in unsupervised anomaly detection, offering a robust, adaptive framework for security analysts and researchers seeking to enhance detection accuracy in high-computation environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_04259 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SCADE: Scalable Framework for Anomaly Detection in High-Performance System Vinay, Vaishali Mangal, Anjali Cryptography and Security Machine Learning As command-line interfaces remain integral to high-performance computing environments, the risk of exploitation through stealthy and complex command-line abuse grows. Conventional security solutions struggle to detect these anomalies due to their context-specific nature, lack of labeled data, and the prevalence of sophisticated attacks like Living-off-the-Land (LOL). To address this gap, we introduce the Scalable Command-Line Anomaly Detection Engine (SCADE), a framework that combines global statistical models with local context-specific analysis for unsupervised anomaly detection. SCADE leverages novel statistical methods, including BM25 and Log Entropy, alongside dynamic thresholding to adaptively detect rare, malicious command-line patterns in low signal-to-noise ratio (SNR) environments. Experimental results show that SCADE achieves above 98% SNR in identifying anomalous behavior while minimizing false positives. Designed for scalability and precision, SCADE provides an innovative, metadata-enriched approach to anomaly detection, offering a robust solution for cybersecurity in high-computation environments. This work presents SCADE's architecture, detection methodology, and its potential for enhancing anomaly detection in enterprise systems. We argue that SCADE represents a significant advancement in unsupervised anomaly detection, offering a robust, adaptive framework for security analysts and researchers seeking to enhance detection accuracy in high-computation environments. |
| title | SCADE: Scalable Framework for Anomaly Detection in High-Performance System |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2412.04259 |