A methodology for testing virtualisation security

There is a growing interest in virtualisation due to its central role in cloud computing, virtual desktop environments and Green IT. Data centres and cloud computing utilise this technology to run multiple operating systems on one physical server, thus reducing hardware costs. However, vulnerabilities in the hypervisor layer have an impact on any virtual machines running on top, making security an important part of virtualisation. In this paper, we evaluate the security of virtualisation, including detection and escaping the environment. We present a methodology to investigate if a virtual machine can be detected and further compromised, based upon previous research. Finally, this methodology is used to evaluate the security of virtual machines. The methods used to evaluate the security include analysis of known vulnerabilities and fuzzing to test the virtual device drivers on three different platforms: VirtualBox, Hyper-V and VMware ESXI. Our results demonstrate that the attack surface of virtualisation is more prone to vulnerabilities than the hypervisor. Comparing our results with previous studies, each platform withstood IOCTL and random fuzzing, demonstrating that the platforms are more robust and secure than previously found. By building on existing research, the results show that security in the hypervisor has been improved. However, using the proposed methodology in this paper it has been shown that an attacker can easily determine that the machine is a virtual machine, which could be used for further exploitation. Finally, our proposed methodology can be utilised to effectively test the security of a virtualised environment

ATTACK2VEC: Leveraging Temporal Word Embeddings to Understand the Evolution of Cyberattacks

Despite the fact that cyberattacks are constantly growing in complexity, the research community still lacks effective tools to easily monitor and understand them. In particular, there is a need for techniques that are able to not only track how prominently certain malicious actions, such as the exploitation of specific vulnerabilities, are exploited in the wild, but also (and more importantly) how these malicious actions factor in as attack steps in more complex cyberattacks. In this paper we present ATTACK2VEC, a system that uses temporal word embeddings to model how attack steps are exploited in the wild, and track how they evolve. We test ATTACK2VEC on a dataset of billions of security events collected from the customers of a commercial Intrusion Prevention System over a period of two years, and show that our approach is effective in monitoring the emergence of new attack strategies in the wild and in flagging which attack steps are often used together by attackers (e.g., vulnerabilities that are frequently exploited together). ATTACK2VEC provides a useful tool for researchers and practitioners to better understand cyberattacks and their evolution, and use this knowledge to improve situational awareness and develop proactive defenses

Reactive point processes: A new approach to predicting power failures in underground electrical systems

Reactive point processes (RPPs) are a new statistical model designed for predicting discrete events in time based on past history. RPPs were developed to handle an important problem within the domain of electrical grid reliability: short-term prediction of electrical grid failures ("manhole events"), including outages, fires, explosions and smoking manholes, which can cause threats to public safety and reliability of electrical service in cities. RPPs incorporate self-exciting, self-regulating and saturating components. The self-excitement occurs as a result of a past event, which causes a temporary rise in vulner ability to future events. The self-regulation occurs as a result of an external inspection which temporarily lowers vulnerability to future events. RPPs can saturate when too many events or inspections occur close together, which ensures that the probability of an event stays within a realistic range. Two of the operational challenges for power companies are (i) making continuous-time failure predictions, and (ii) cost/benefit analysis for decision making and proactive maintenance. RPPs are naturally suited for handling both of these challenges. We use the model to predict power-grid failures in Manhattan over a short-term horizon, and to provide a cost/benefit analysis of different proactive maintenance programs.Comment: Published at http://dx.doi.org/10.1214/14-AOAS789 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Analysis of operating system diversity for intrusion tolerance

One of the key benefits of using intrusion-tolerant systems is the possibility of ensuring correct behavior in the presence of attacks and intrusions. These security gains are directly dependent on the components exhibiting failure diversity. To what extent failure diversity is observed in practical deployment depends on how diverse are the components that constitute the system. In this paper, we present a study with operating system's (OS's) vulnerability data from the NIST National Vulnerability Database (NVD). We have analyzed the vulnerabilities of 11 different OSs over a period of 18 years, to check how many of these vulnerabilities occur in more than one OS. We found this number to be low for several combinations of OSs. Hence, although there are a few caveats on the use of NVD data to support definitive conclusions, our analysis shows that by selecting appropriate OSs, one can preclude (or reduce substantially) common vulnerabilities from occurring in the replicas of the intrusion-tolerant system

Predicting Exploitation of Disclosed Software Vulnerabilities Using Open-source Data

Each year, thousands of software vulnerabilities are discovered and reported to the public. Unpatched known vulnerabilities are a significant security risk. It is imperative that software vendors quickly provide patches once vulnerabilities are known and users quickly install those patches as soon as they are available. However, most vulnerabilities are never actually exploited. Since writing, testing, and installing software patches can involve considerable resources, it would be desirable to prioritize the remediation of vulnerabilities that are likely to be exploited. Several published research studies have reported moderate success in applying machine learning techniques to the task of predicting whether a vulnerability will be exploited. These approaches typically use features derived from vulnerability databases (such as the summary text describing the vulnerability) or social media posts that mention the vulnerability by name. However, these prior studies share multiple methodological shortcomings that inflate predictive power of these approaches. We replicate key portions of the prior work, compare their approaches, and show how selection of training and test data critically affect the estimated performance of predictive models. The results of this study point to important methodological considerations that should be taken into account so that results reflect real-world utility

Model-Based Security Testing

Security testing aims at validating software system requirements related to security properties like confidentiality, integrity, authentication, authorization, availability, and non-repudiation. Although security testing techniques are available for many years, there has been little approaches that allow for specification of test cases at a higher level of abstraction, for enabling guidance on test identification and specification as well as for automated test generation. Model-based security testing (MBST) is a relatively new field and especially dedicated to the systematic and efficient specification and documentation of security test objectives, security test cases and test suites, as well as to their automated or semi-automated generation. In particular, the combination of security modelling and test generation approaches is still a challenge in research and of high interest for industrial applications. MBST includes e.g. security functional testing, model-based fuzzing, risk- and threat-oriented testing, and the usage of security test patterns. This paper provides a survey on MBST techniques and the related models as well as samples of new methods and tools that are under development in the European ITEA2-project DIAMONDS.Comment: In Proceedings MBT 2012, arXiv:1202.582

Network hierarchy evolution and system vulnerability in power grids

(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.The seldom addressed network hierarchy property and its relationship with vulnerability analysis for power transmission grids from a complex-systems point of view are given in this paper. We analyze and compare the evolution of network hierarchy for the dynamic vulnerability evaluation of four different power transmission grids of real cases. Several meaningful results suggest that the vulnerability of power grids can be assessed by means of a network hierarchy evolution analysis. First, the network hierarchy evolution may be used as a novel measurement to quantify the robustness of power grids. Second, an antipyramidal structure appears in the most robust network when quantifying cascading failures by the proposed hierarchy metric. Furthermore, the analysis results are also validated and proved by empirical reliability data. We show that our proposed hierarchy evolution analysis methodology could be used to assess the vulnerability of power grids or even other networks from a complex-systems point of view.Peer ReviewedPostprint (author's final draft