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

翻译：机器学习和数据挖掘技术被用于增强任何网络的安全性。研究人员利用机器学习进行模式检测、异常检测、动态策略设置等。这些方法使程序能够从数据中学习，并在无需人工干预的情况下做出决策，但需要消耗大量的训练时间和计算能力。本文讨论了一种基于多个数据参数预测网络中即将发生攻击的新技术。该数据集在实时实现中是连续的。所提出的模型包括数据集预处理、训练以及随后的测试阶段。基于测试阶段的结果，选择最佳模型，并提取可能导致攻击的事件类别。事件统计数据用于攻击预测。