Smiliotopoulos, C.; Barmpatsalou , K.; Kambourakis, G. Revisiting the Detection of Lateral Movement through Sysmon. Appl. Sci.2022, 12, 7746.
Smiliotopoulos, C.; Barmpatsalou , K.; Kambourakis, G. Revisiting the Detection of Lateral Movement through Sysmon. Appl. Sci. 2022, 12, 7746.
Smiliotopoulos, C.; Barmpatsalou , K.; Kambourakis, G. Revisiting the Detection of Lateral Movement through Sysmon. Appl. Sci.2022, 12, 7746.
Smiliotopoulos, C.; Barmpatsalou , K.; Kambourakis, G. Revisiting the Detection of Lateral Movement through Sysmon. Appl. Sci. 2022, 12, 7746.
Abstract
This work attempts to answer in a clear way the following key questions regarding the optimal initialization of the Sysmon tool, towards the identification of Lateral Movement in the MS Windows ecosystem. First, from an expert’s standpoint and with reference to the relevant literature, what are the criteria of determining the possibly optimal initialization features of the Sysmon’s event monitoring tool, which are also applicable as custom rules within the config.xml configuration file? Second, based on the identified features, how can a functional configuration file, able to identify as many LM variants as possible, be generated? To answer these questions, we relied on the MITRE ATT&CK knowledge base of adversary tactics and techniques, and focused on the execution of the nine commonest LM methods. The conducted experiments, performed on a properly configured testbed, suggested a great number of interrelated networking features, that were implemented as custom rules in the Sysmon’s config.xml file. Moreover, by capitalizing on the rich corpus of the 870K Sysmon logs collected, we create and evaluate in terms of TP and FP rates an extensible Python .evtx file analyzer, dubbed PeX, which can be used towards automatizing the parsing and scrutiny of such voluminous files. Both the .evtx logs dataset and the developed PeX tool are provided publicly for further propelling future research in this interesting and rapidly evolving field.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
