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Learning Rules from System Call Arguments and Sequences for Anomaly 20 Detection
Many approaches have been suggested and various systems been modeled to detect intrusions from anomalous behavior of system calls as a result of an attack. Though these techniques have been shown toExpand
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  • Open Access
Endothelial Krüppel-like factor 4 modulates pulmonary arterial hypertension.
Krüppel-like factor 4 (KLF4) is a transcription factor expressed in the vascular endothelium, where it promotes anti-inflammatory and anticoagulant states, and increases endothelial nitric oxideExpand
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  • Open Access
Weighting versus pruning in rule validation for detecting network and host anomalies
For intrusion detection, the LERAD algorithm learns a succinct set of comprehensible rules for detecting anomalies, which could be novel attacks. LERAD validates the learned rules on a separateExpand
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  • Open Access
On the Learning of System Call Attributes for Host-based Anomaly Detection
Traditional host-based anomaly detection systems model normal behavior of applications by analyzing system call sequences. The current sequence is then examined (using the model) for anomalousExpand
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  • Open Access
Learning Useful System Call Attributes for Anomaly Detection
Traditional host-based anomaly detection systems model normal behavior of applications by analyzing system call sequences. Current sequence is then examined (using the model) for anomalous behavior,Expand
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  • Open Access
MORPHEUS: motif oriented representations to purge hostile events from unlabeled sequences
Most of the prevalent anomaly detection systems use some training data to build models. These models are then utilized to capture any deviations resulting from possible intrusions. The efficacy ofExpand
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  • Open Access
Tracking User Mobility to Detect Suspicious Behavior
Popularity of mobile devices is accompanied by widespread security problems, such as MAC address spoofing in wireless networks. We propose a probabilistic approach to temporal anomaly detection usingExpand
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  • Open Access
A Case Study of Variation in Aluminum Smelting Cell Thermal State with Control Implications
The thermal state of an aluminum smelting cell is determined principally by the following parameters: electrolyte temperature and excess aluminum fluoride concentration. Despite the attempts toExpand
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Machine learning for host-based anomaly detection
Anomaly detection techniques complement, signature based methods for intrusion detection. Machine learning approaches are applied to anomaly detection for automated learning and detection.Expand
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  • Open Access