Insider threat detection of ransomware using AutoML
R. Bhuvaneswari, Enaganti Karun Kumar, Annadanam Padmasini, Kavita Varma · 2023
Insider threats are one of the biggest issues that modern-day organizations and many large-scale companies face. The inside threats are caused by the insiders who are authorized individuals, also may have proper access to sensitive and secret information, and may be aware of the weaknesses in the implemented systems and operational procedures. And, it is confirmed that these insider threats have already shown their great damaging power in securing important information and because of this, organizations require more proactive and modern-day solutions to support existing cybersecurity tools in order to detect and prevent them. Conventional interruption recognition frameworks neglect to be powerful in insider threats because of the absence of a wide range of information for insider ways of behaving. All things considered; a more complex and standard technique is expected to have a more profound comprehension of the ways that insiders speak with the information framework. Ransomware hackers appear to be adopting new methods in response to tightening security. Automated machine learning has been utilised in this work for detection of ransomware and we also included the ensembling method for a few algorithms that are suitable for the given dataset.