Practical machine learning for cloud intrusion detection: Challenges and the way forward
RSS Kumar, A Wicker, M Swann - … of the 10th ACM workshop on artificial …, 2017 - dl.acm.org
Operationalizing machine learning based security detections is extremely challenging,
especially in a continuously evolving cloud environment. Conventional anomaly detection
does not produce satisfactory results for analysts that are investigating security incidents in
the cloud. Model evaluation alone presents its own set of problems due to a lack of
benchmark datasets. When deploying these detections, we must deal with model
compliance, localization, and data silo issues, among many others. We pose the problem of" …
especially in a continuously evolving cloud environment. Conventional anomaly detection
does not produce satisfactory results for analysts that are investigating security incidents in
the cloud. Model evaluation alone presents its own set of problems due to a lack of
benchmark datasets. When deploying these detections, we must deal with model
compliance, localization, and data silo issues, among many others. We pose the problem of" …
Practical Machine Learning for Cloud Intrusion Detection: Challenges and the Way Forward
R Shankar Siva Kumar, A Wicker, M Swann - arXiv e-prints, 2017 - ui.adsabs.harvard.edu
Operationalizing machine learning based security detections is extremely challenging,
especially in a continuously evolving cloud environment. Conventional anomaly detection
does not produce satisfactory results for analysts that are investigating security incidents in
the cloud. Model evaluation alone presents its own set of problems due to a lack of
benchmark datasets. When deploying these detections, we must deal with model
compliance, localization, and data silo issues, among many others. We pose the problem of" …
especially in a continuously evolving cloud environment. Conventional anomaly detection
does not produce satisfactory results for analysts that are investigating security incidents in
the cloud. Model evaluation alone presents its own set of problems due to a lack of
benchmark datasets. When deploying these detections, we must deal with model
compliance, localization, and data silo issues, among many others. We pose the problem of" …
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