Biblio

This is very true for the Windows operating system (OS) used by government and private organizations. With Windows, the closed source nature of the operating system has unfortunately meant that hidden security issues are discovered very late and the fixes are not found in real time. There needs to be a reexamination of current static methods of malware detection. This paper presents an integrated system for automated and real-time monitoring and prediction of rootkit and malware threats for the Windows OS. We propose to host the target Windows machines on the widely used Xen hypervisor, and collect process behavior using virtual memory introspection (VMI). The collected data will be analyzed using state of the art machine learning techniques to quickly isolate malicious process behavior and alert system administrators about potential cyber breaches. This research has two focus areas: identifying memory data structures and developing prediction tools to detect malware. The first part of research focuses on identifying memory data structures affected by malware. This includes extracting the kernel data structures with VMI that are frequently targeted by rootkits/malware. The second part of the research will involve development of a prediction tool using machine learning techniques.

Kernel rootkits often hide associated malicious processes by altering reported task struct information to upper layers and applications such as ps and top. Virtualized settings offer a unique opportunity to mitigate this behavior using dynamic virtual machine introspection (VMI). For known kernels, VMI can be deployed to search for kernel objects and identify them by using unique data structure "signatures". In existing work, VMI-detected data structure signatures are based on values and structural features which must be (often exactly) present in memory snapshots taken, for accurate detection. This features a certain brittleness and rootkits can escape detection by simply temporarily "un-tangling" the corresponding structures when not running. Here we introduce a new paradigm, that defeats such behavior by training for and observing signatures of timing access patterns to any and all kernel-mapped data regions, including objects that are not directly linked in the "official" list of tasks. The use of timing information in training detection signatures renders the defenses resistant to attacks that try to evade detection by removing their corresponding malicious processes before scans. KXRay successfully detected processes hidden by four traditional rootkits.

Cloud computing technologies are receiving a great deal of attention. Furthermore most of the hardware devices such as the PCs and mobile phones are increasingly having a trusted component called Trusted Platform Module embedded in them, which helps to measure the state of the platform and hence reason about its trust. Recently attestation techniques such as binary attestation and property based attestation techniques have been proposed based on the TPM. In this paper, we propose a novel trust enhanced security model for cloud services that helps to detect and prevent security attacks in cloud infrastructures using trusted attestation techniques. We consider a cloud architecture where different services are hosted on virtualized systems on the cloud by multiple cloud customers (multi-tenants). We consider attacker model and various attack scenarios for such hosted services in the cloud. Our trust enhanced security model enables the cloud service provider to certify certain security properties of the tenant virtual machines and services running on them. These properties are then used to detect and minimise attacks between the cloud tenants running virtual machines on the infrastructure and its customers as well as increase the assurance of the tenant virtual machine transactions. If there is a variation in the behaviour of the tenant virtual machine from the certified properties, the model allows us to dynamically isolate the tenant virtual machine or even terminate the malicious services on a fine granular basis. The paper describes the design and implementation of the proposed model and discusses how it deals with the different attack scenarios. We also show that our model is beneficial for the cloud service providers, cloud customers running tenant virtual machines as well as the customers using the services provided by these tenant virtual machines.