HyBIS: Windows Guest Protection through Advanced Memory Introspection

Effectively protecting the Windows OS is a challenging task, since most implementation details are not publicly known. Windows has always been the main target of malwares that have exploited numerous bugs and vulnerabilities. Recent trusted boot and additional integrity checks have rendered the Windows OS less vulnerable to kernel-level rootkits. Nevertheless, guest Windows Virtual Machines are becoming an increasingly interesting attack target. In this work we introduce and analyze a novel Hypervisor-Based Introspection System (HyBIS) we developed for protecting Windows OSes from malware and rootkits. The HyBIS architecture is motivated and detailed, while targeted experimental results show its effectiveness. Comparison with related work highlights main HyBIS advantages such as: effective semantic introspection, support for 64-bit architectures and for latest Windows (8.x and 10), advanced malware disabling capabilities. We believe the research effort reported here will pave the way to further advances in the security of Windows OSes

The THREAT-ARREST Cyber-Security Training Platform

Cyber security is always a main concern for critical infrastructures and nation-wide safety and sustainability. Thus, advanced cyber ranges and security training is becoming imperative for the involved organizations. This paper presets a cyber security training platform, called THREAT-ARREST. The various platform modules can analyze an organization’s system, identify the most critical threats, and tailor a training program to its personnel needs. Then, different training programmes are created based on the trainee types (i.e. administrator, simple operator, etc.), providing several teaching procedures and accomplishing diverse learning goals. One of the main novelties of THREAT-ARREST is the modelling of these programmes along with the runtime monitoring, management, and evaluation operations. The platform is generic. Nevertheless, its applicability in a smart energy case study is detailed

COST Action IC 1402 ArVI: Runtime Verification Beyond Monitoring -- Activity Report of Working Group 1

This report presents the activities of the first working group of the COST Action ArVI, Runtime Verification beyond Monitoring. The report aims to provide an overview of some of the major core aspects involved in Runtime Verification. Runtime Verification is the field of research dedicated to the analysis of system executions. It is often seen as a discipline that studies how a system run satisfies or violates correctness properties. The report exposes a taxonomy of Runtime Verification (RV) presenting the terminology involved with the main concepts of the field. The report also develops the concept of instrumentation, the various ways to instrument systems, and the fundamental role of instrumentation in designing an RV framework. We also discuss how RV interplays with other verification techniques such as model-checking, deductive verification, model learning, testing, and runtime assertion checking. Finally, we propose challenges in monitoring quantitative and statistical data beyond detecting property violation

Analysis and evaluation of SafeDroid v2.0, a framework for detecting malicious Android applications

Android smartphones have become a vital component of the daily routine of millions of people, running a plethora of applications available in the official and alternative marketplaces. Although there are many security mechanisms to scan and filter malicious applications, malware is still able to reach the devices of many end-users. In this paper, we introduce the SafeDroid v2.0 framework, that is a flexible, robust, and versatile open-source solution for statically analysing Android applications, based on machine learning techniques. The main goal of our work, besides the automated production of fully sufficient prediction and classification models in terms of maximum accuracy scores and minimum negative errors, is to offer an out-of-the-box framework that can be employed by the Android security researchers to efficiently experiment to find effective solutions: the SafeDroid v2.0 framework makes it possible to test many different combinations of machine learning classifiers, with a high degree of freedom and flexibility in the choice of features to consider, such as dataset balance and dataset selection. The framework also provides a server, for generating experiment reports, and an Android application, for the verification of the produced models in real-life scenarios. An extensive campaign of experiments is also presented to show how it is possible to efficiently find competitive solutions: the results of our experiments confirm that SafeDroid v2.0 can reach very good performances, even with highly unbalanced dataset inputs and always with a very limited overhead

Monitoring extensions for component-based distributed software

This paper defines a generic class of monitoring extensions to component-based distributed enterprise software. Introducing a monitoring extension to a legacy application system can be very costly. In this paper, we identify the minimum support for application monitoring within the generic components of a distributed system, necessary for rapid development of new monitoring extensions. Furthermore, this paper offers an approach for design and implementation of monitoring extensions at reduced cost. A framework of basic facilities supporting the monitoring extensions is presented. These facilities handle different aspects critical to the monitoring process, such as ordering of the generated monitoring events, decoupling of the application components from the components of the monitoring extensions, delivery of the monitoring events to multiple consumers, etc.\ud The work presented in this paper is being validated in the prototype of a large distributed system, where a specific monitoring extension is built as a tool for debugging and testing the application behaviour.\u

Enabling Adaptive Grid Scheduling and Resource Management

Wider adoption of the Grid concept has led to an increasing amount of federated computational, storage and visualisation resources being available to scientists and researchers. Distributed and heterogeneous nature of these resources renders most of the legacy cluster monitoring and management approaches inappropriate, and poses new challenges in workflow scheduling on such systems. Effective resource utilisation monitoring and highly granular yet adaptive measurements are prerequisites for a more efficient Grid scheduler. We present a suite of measurement applications able to monitor per-process resource utilisation, and a customisable tool for emulating observed utilisation models. We also outline our future work on a predictive and probabilistic Grid scheduler. The research is undertaken as part of UK e-Science EPSRC sponsored project SO-GRM (Self-Organising Grid Resource Management) in cooperation with BT