  The article deals with anomaly detection of Juniper router logs. Abnormal
Juniper router logs include logs that are usually different from the normal
operation, and they often reflect the abnormal operation of router devices. To
prevent router devices from being damaged and help administrator to grasp the
situation of error quickly, detecting abnormal operation soon is very
important. In this work, we present a new way to get important features from
log data of Juniper router devices and use machine learning method (basing on
One-Class SVM model) for anomaly detection. One-Class SVM model requires some
knowledge and comprehension about logs of Juniper router devices so that it can
analyze, interpret, and test the knowledge ac-quired. We collect log data from
a lot of real Juniper router devices and clas-sify them based on our knowledge.
Before these logs are used for training and testing the One-Class SVM model,
the feature extraction phase for these data was carried out. Finally, with the
proposed method, the system errors of the routers were dectected quickly and
accurately. This may help our com-pany to reduce the operation cost for the
router systems.
