Biblio

To enable virtual machines (VMs) with a large amount of memory to be flexibly migrated, split migration has been proposed. It divides a large-memory VM into small pieces and transfers them to multiple hosts. After the migration, the VM runs across those hosts and exchanges memory data between hosts using remote paging. For such a split-memory VM, however, it becomes difficult to securely run intrusion detection systems (IDS) outside the VM using a technique called IDS offloading. This paper proposes VMemTrans to support transparent IDS offloading for split-memory VMs. In VMemTrans, offloaded IDS can monitor a split-memory VM as if that memory were not distributed. To achieve this, VMemTrans enables IDS running in one host to transparently access VM's remote memory. To consider a trade-off, it provides two methods for obtaining memory data from remote hosts: self paging and proxy paging. We have implemented VMemTrans in KVM and compared the execution performance between the two methods.

Insider threats result from legitimate users abusing their privileges, causing tremendous damage or losses. Malicious insiders can be the main threats to an organization. This paper presents an anomaly detection system for detecting insider threat activities in an organization using an ensemble that consists of negative selection algorithms (NSA). The proposed system classifies a selected user activity into either of two classes: "normal" or "malicious." The effectiveness of our proposed detection system is evaluated using case studies from the computer emergency response team (CERT) synthetic insider threat dataset. Our results show that the proposed method is very effective in detecting insider threats.