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Anomaly detection has been popular for a long time due to its ability to detect novel attacks. However, its practical deployment has been limited due to false positives. Taint-based techniques, on the other hand, can avoid false positives for many common exploits (e.g., code or script injection), but their applicability to a broader range of attacks (non-control data attacks, path traversals, race condition attacks, and other unknown attacks) is limited by the need for accurate policies on the use of tainted data. In this paper, the authors develop a new approach that combines the strengths of these approaches. Their combination is very effective, detecting attack types that have been problematic for taint-based techniques, while significantly cutting down the false positives experienced by anomaly detection.
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