Biblio

Defense-in-depth is an important security architecture principle that has significant application to industrial control systems (ICS), cloud services, storehouses of sensitive data, and many other areas. We claim that an ideal defense-in-depth posture is 'deep', containing many layers of security, and 'narrow', the number of node independent attack paths is minimized. Unfortunately, accurately calculating both depth and width is difficult using standard graph algorithms because of a lack of independence between multiple vulnerability instances (i.e., if an attacker can penetrate a particular vulnerability on one host then they can likely penetrate the same vulnerability on another host). To address this, we represent known weaknesses and vulnerabilities as a type of colored attack graph. We measure depth and width through solving the shortest color path and minimum color cut problems. We prove both of these to be NP-Hard and thus for our solution we provide a suite of greedy heuristics. We then empirically apply our approach to large randomly generated networks as well as to ICS networks generated from a published ICS attack template. Lastly, we discuss how to use these results to help guide improvements to defense-in-depth postures.

Attack graphs used in network security analysis are analyzed to determine sequences of exploits that lead to successful acquisition of privileges or data at critical assets. An attack graph edge corresponds to a vulnerability, tacitly assuming a connection exists and tacitly assuming the vulnerability is known to exist. In this paper we explore use of uncertain graphs to extend the paradigm to include lack of certainty in connection and/or existence of a vulnerability. We extend the standard notion of uncertain graph (where the existence of each edge is probabilistically independent) however, as signicant correlations on edge existence probabilities exist in practice, owing to common underlying causes for dis-connectivity and/or presence of vulnerabilities. Our extension describes each edge probability as a Boolean expression of independent indicator random variables. This paper (i) shows that this formalism is maximally descriptive in the sense that it can describe any joint probability distribution function of edge existence, (ii) shows that when these Boolean expressions are monotone then we can easily perform uncertainty analysis of edge probabilities, and (iii) uses these results to model a partial attack graph of the Stuxnet worm and a small enterprise network and to answer important security-related questions in a probabilistic manner.

Most cyber network attacks begin with an adversary gaining a foothold within the network and proceed with lateral movement until a desired goal is achieved. The mechanism by which lateral movement occurs varies but the basic signature of hopping between hosts by exploiting vulnerabilities is the same. Because of the nature of the vulnerabilities typically exploited, lateral movement is very difficult to detect and defend against. In this paper we define a dynamic reachability graph model of the network to discover possible paths that an adversary could take using different vulnerabilities, and how those paths evolve over time. We use this reachability graph to develop dynamic machine-level and network-level impact scores. Lateral movement mitigation strategies which make use of our impact scores are also discussed, and we detail an example using a freely available data set.

Information and communication technologies have augmented interoperability and rapidly advanced varying industries, with vast complex interconnected networks being formed in areas such as safety-critical systems, which can be further categorised as critical infrastructures. What also must be considered is the paradigm of the Internet of Things which is rapidly gaining prevalence within the field of wireless communications, being incorporated into areas such as e-health and automation for industrial manufacturing. As critical infrastructures and the Internet of Things begin to integrate into much wider networks, their reliance upon communication assets by third parties to ensure collaboration and control of their systems will significantly increase, along with system complexity and the requirement for improved security metrics. We present a critical analysis of the risk assessment methods developed for generating attack graphs. The failings of these existing schemas include the inability to accurately identify the relationships and interdependencies between the risks and the reduction of attack graph size and generation complexity. Many existing methods also fail due to the heavy reliance upon the input, identification of vulnerabilities, and analysis of results by human intervention. Conveying our work, we outline our approach to modelling interdependencies within large heterogeneous collaborative infrastructures, proposing a distributed schema which utilises network modelling and attack graph generation methods, to provide a means for vulnerabilities, exploits and conditions to be represented within a unified model.

One of the primary challenges when developing or implementing a security framework for any particular environment is determining the efficacy of the implementation. Does the implementation address all of the potential vulnerabilities in the environment, or are there still unaddressed issues? Further, if there is a choice between two frameworks, what objective measure can be used to compare the frameworks? To address these questions, we propose utilizing a technique of attack graph analysis to map the attack surface of the environment and identify the most likely avenues of attack. We show that with this technique we can quantify the baseline state of an application and compare that to the attack surface after implementation of a security framework, while simultaneously allowing for comparison between frameworks in the same environment or a single framework across multiple applications.

Moving Target Defense (MTD) changes the attack surface of a system that confuses intruders to thwart attacks. Various MTD techniques are developed to enhance the security of a networked system, but the effectiveness of these techniques is not well assessed. Security models (e.g., Attack Graphs (AGs)) provide formal methods of assessing security, but modeling the MTD techniques in security models has not been studied. In this paper, we incorporate the MTD techniques in security modeling and analysis using a scalable security model, namely Hierarchical Attack Representation Models (HARMs), to assess the effectiveness of the MTD techniques. In addition, we use importance measures (IMs) for scalable security analysis and deploying the MTD techniques in an effective manner. The performance comparison between the HARM and the AG is given. Also, we compare the performance of using the IMs and the exhaustive search method in simulations.

Cloud computing brings in a lot of advantages for enterprise IT infrastructure; virtualization technology, which is the backbone of cloud, provides easy consolidation of resources, reduction of cost, space and management efforts. However, security of critical and private data is a major concern which still keeps back a lot of customers from switching over from their traditional in-house IT infrastructure to a cloud service. Existence of techniques to physically locate a virtual machine in the cloud, proliferation of software vulnerability exploits and cross-channel attacks in-between virtual machines, all of these together increases the risk of business data leaks and privacy losses. This work proposes a framework to mitigate such risks and engineer customer trust towards enterprise cloud computing. Everyday new vulnerabilities are being discovered even in well-engineered software products and the hacking techniques are getting sophisticated over time. In this scenario, absolute guarantee of security in enterprise wide information processing system seems a remote possibility; software systems in the cloud are vulnerable to security attacks. Practical solution for the security problems lies in well-engineered attack mitigation plan. At the positive side, cloud computing has a collective infrastructure which can be effectively used to mitigate the attacks if an appropriate defense framework is in place. We propose such an attack mitigation framework for the cloud. Software vulnerabilities in the cloud have different severities and different impacts on the security parameters (confidentiality, integrity, and availability). By using Markov model, we continuously monitor and quantify the risk of compromise in different security parameters (e.g.: change in the potential to compromise the data confidentiality). Whenever, there is a significant change in risk, our framework would facilitate the tenants to calculate the Mean Time to Security Failure (MTTSF) cloud and allow them to adopt a dynamic mitigation plan. This framework is an add-on security layer in the cloud resource manager and it could improve the customer trust on enterprise cloud solutions.