Artificial intelligence analysis in cyber domain: A review

The application of Big Data Analytics is identified through the Cyber Research Alliance for cybersecurity as the foremost preference for future studies and advancement in the field of cybersecurity. In this study, we develop a repeatable procedure for detecting cyber-attacks in an accurate, scalable, and timely manner. An in-depth learning algorithm is utilized for training a neural network for detecting suspicious user activities. The proposed system architecture was implemented with the help of Splunk Enterprise Edition 6.42. A data set of average feature counts has been executed through a Splunk search command in 1-min intervals. All the data sets consisted of a minute trait total derived from a sparkling file. The attack patterns that were not anonymized or were indicative of the vulnerability of cyber-attack were denoted with yellow. The rule-based method dispensed a low quantity of irregular illustrations in contrast with the Partitioning Around Medoids method. The results in this study demonstrated that using a proportional collection of instances trained with the deep learning algorithm, a classified data set can accurately detect suspicious behavior. This method permits for the allocation of multiple log source types through a sliding time window and provides a scalable solution, which is a much-needed function.

网络安全领域的网络研究联盟将大数据分析的应用确定为网络安全领域未来研究和进步的首要选择。在本研究中，我们开发了一种可重复的程序，用于以准确、可扩展且及时的方式检测网络攻击。利用一种深度学习算法来训练一个神经网络，以检测可疑的用户活动。所提出的系统架构是在Splunk企业版6.42的帮助下实现的。一个平均特征计数的数据集以1分钟的间隔通过Splunk搜索命令执行。所有数据集都包含从一个动态文件中得出的一分钟特征总数。未匿名化或表明网络攻击脆弱性的攻击模式用黄色标注。与围绕中心点划分（Partitioning Around Medoids）方法相比，基于规则的方法产生的异常示例数量较少。本研究的结果表明，使用深度学习算法训练的按比例收集的实例，一个分类数据集能够准确地检测可疑行为。这种方法允许通过滑动时间窗口分配多种日志源类型，并提供一种可扩展的解决方案，这是一个非常必要的功能。