SCADE: Scalable Framework for Anomaly Detection in High-Performance System
| Autor: | Vinay, Vaishali, Mangal, Anjali |
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| Rok vydání: | 2024 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: Updated title and abstract for broader scope. Submitted to ACM CODASPY (The 15th ACM Conference on Data and Application Security and Privacy) Conference |
| Databáze: | arXiv |
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