  Machine learning and data mining techniques are utiized for enhancement of
the security of any network. Researchers used machine learning for pattern
detection, anomaly detection, dynamic policy setting, etc. The methods allow
the program to learn from data and make decisions without human intervention,
consuming a huge training period and computation power. This paper discusses a
novel technique to predict an upcoming attack in a network based on several
data parameters. The dataset is continuous in real-time implementation. The
proposed model comprises dataset pre-processing, and training, followed by the
testing phase. Based on the results of the testing phase, the best model is
selected using which, event class which may lead to an attack is extracted. The
event statistics are used for attack
