Effectively predicting cyber‐attacks through isolation forest learning‐based outlier detection

Abstract Due to the popularity of Internet of Things devices, the exponential progress of computer networks, and a plethora of associated applications, cybersecurity has recently attracted much attention in light of today's security problems . As a result, detecting various cyber‐attacks within a network and developing an effective cyber‐attacks prediction model that plays a crucial part in today's defense has become increasingly critical. Modeling cyber‐attacks effectively, on the other hand, is challenging because modern security datasets hold a large number of dimensions of security features and may contain outliers . To accomplish this, we provide an approach for categorizing cyber‐attacks effectively through isolation forest learning‐based outlier detection. Additionally, we apply a variety of popular machine learning approaches to assess the…