首页|Comparison of cognitively-inspired salience and feature importance techniques in intrusion detection datasets
Comparison of cognitively-inspired salience and feature importance techniques in intrusion detection datasets
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NETL
Spie-Int Soc Optical Engineering
We present a cognitively-inspired salience technique that utilizes our knowledge of human information processing mechanisms to determine the extent to which each feature is responsible for making a given decision in a given context。 While measures of model-based feature importance are prevalent in ML packages (e。g。， the fitted attribute feature_importances_ and permutation_importance function in the sklearn package)， there are fewer off-the-shelf techniques for class-based and decision-based importance。 Our measures of cognitive salience result from deriving the output of the cognitive mechanisms with respect to specific features or instances in the current situation。 Applying this technique to intrusion detection datasets (e。g。， UNSW-NB15， CICIDS2017) we can compute the salience of each feature for any given classification， including different feature salience for correct vs incorrect decisions。 By tracing (i。e。， clamping) our model's decisions to that of a specific classification technique or individual decision maker， we are able to provide measures of model-agnostic salience。 While extant feature importance techniques (e。g。， selectKbest， random forest) focus on which features improve overall model performance， cognitive salience instead considers how much a given feature influences the current decision。 This allows for more fine-grained analyses of model performance and has been used previously to introspect over the decisions of deep reinforcement learning algorithms and explain why RL agents failed at a StarCraft 2 task or which strategies they discovered in grid world multi-agent adversarial tasks， as well as introspecting over features in an insider attack scenario to identify how (and when) humans learn the relative value of each feature。 We report a comparison between cognitive salience and extant ML-based techniques。
Cognitive salienceintrusion detectionsalience
Robert H. Thomson、Edward A. Cranford、Gabe Tucker、Christian Lcbiere
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United States Military Academy, West Point, NY, 10996, USA
Institute for Human and Machine Cognition, 40 S Alcaniz St., Pensacola, FL, 32502, USA
The Ohio State University, 281 W Lane Ave, Columbus, OH, 43210, USA
Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA
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Conference on Assurance and Securtiy for AI-enabled Systems
NETL
Spie-Int Soc Optical Engineering
