@article{oai:kyutech.repo.nii.ac.jp:00007755,
 author = {Huseynov,  Huseyn and Saadawi,  Tarek and Kourai, Kenichi and 光来, 健一},
 issue = {312},
 journal = {Lecture Notes in Networks and Systems},
 month = {Aug},
 note = {As organizations drastically expand their usage of collaborative systems and multi-user applications during this period of mass remote work, it is crucial to understand and manage the risks that such platforms may introduce. Improperly or carelessly deployed and configured systems hide security threats that can impact not only a single organization, but the whole economy. Cloud-based architecture is used in many collaborative systems, such as audio/video conferencing, collaborative document sharing/editing, distance learning and others. Therefore, it is important to understand that safety risk can be triggered by attacks on remote servers and confidential information might be compromised. In this paper, we present an AI powered application that aims to constantly introspect multiple virtual servers in order to detect malicious activities based on their anomalous behavior. Once the suspicious process(es) detected, the application in real-time notifies system administrator about the potential threat. Developed software is able to detect user-space based keyloggers, rootkits, process hiding and other intrusion artifacts via agent-less operation, by operating directly from the host machine. Remote memory introspection means no software to install, no notice to malware to evacuate or destroy data. Conducted experiments on more than twenty different types of malicious applications provide evidence of high detection accuracy., 13th International Conference on Intelligent Networking and Collaborative Systems (INCoS-2021), September 1-3, 2021, Asia University, Virtual Conference (Online Presentation)},
 pages = {32--41},
 title = {Bio-inspired VM Introspection for Securing Collaboration Platforms},
 volume = {312},
 year = {2021},
 yomi = {コウライ, ケンイチ}
}