Dr Martin Barrere Cambrun

Mobile computing devices and the services offered by them are utilized by millions of users on a daily basis. However, they operate in hostile environments getting exposed to a wide variety of threats. Accordingly, vulnerability management mechanisms are highly required. We present in this demo a novel approach for increasing the security of mobile devices by efficiently detecting vulnerable configurations. In that context, we propose Ovaldroid, an OVAL-based distributed framework for ensuring safe configurations within the Android platform and we present an implementation prototype developed to this end.

The autonomic paradigm has been introduced in order to cope with the growing complexity of management. In that context, autonomic networks and systems are in charge of their own configuration. However, the changes that are operated by these environments may generate vulnerable configurations. In the meantime, a strong standardization effort has been done for specifying the description of configuration vulnerabilities. We propose in this paper an approach for integrating these descriptions into the management plane of autonomic systems in order to ensure safe configurations. We describe the underlying architecture and a set of preliminary results based on the Cfengine configuration tool.

In this paper we identify some of the particular challenges that are encountered when trying to secure cyber-physical systems. We describe three of our current activities: the architecture of a system for monitoring cyber-physical systems; a new approach to modelling dependencies in such systems which leads to a measurement of the security of the system – interpreted as the least effort that an attacker has to expend to compromise the operation; and an approach to optimising the diversity of products used in a system with a view to slowing the propagation of malware. We conclude by discussing how these different threads of work contribute to meeting the challenges and identify possible avenues for future development, as well as providing some pointers to other work.

Cyber-Physical Systems (CPS) often involve complex networks of interconnected software and hardware components that are logically combined to achieve a common goal or mission; for example, keeping a plane in the air or providing energy to a city. Failures in these components may jeopardise the mission of the system. Therefore, identifying the minimal set of critical CPS components that is most likely to fail, and prevent the global system from accomplishing its mission, becomes essential to ensure reliability. In this article, we present a novel approach to identifying the Most Likely Mission-critical Component Set (MLMCS) using AND/OR dependency graphs enriched with independent failure probabilities. We address the MLMCS problem as a Maximum Satisfiability (MaxSAT) problem. We translate probabilities into a negative logarithmic space to linearise the problem within MaxSAT. The experimental results conducted with our open source tool LDA4CPS indicate that the approach is both effective and efficient. We also present a case study on complex aircraft systems that shows the feasibility of our approach and its applicability to mission-critical cyber-physical systems. Finally, we present two MLMCS-based security applications focused on system hardening and forensic investigations.

In this paper, we present a novel MaxSAT-based technique to compute Maximum Probability Minimal Cut Sets (MPMCSs) in fault trees. We model the MPMCS problem as a Weighted Partial MaxSAT problem and solve it using a parallel SAT-solving architecture. The results obtained with our open source tool indicate that the approach is effective and efficient.

Over the last years, computer networks have evolved into highly dynamic and interconnected environments, involving multiple heterogeneous devices and providing a myriad of services on top of them. This complex landscape has made it extremely difficult for security administrators to keep accurate and be effective in protecting their systems against cyber threats. In this paper, we describe our vision and scientific posture on how artificial intelligence techniques and a smart use of security knowledge may assist system administrators in better defending their networks. To that end, we put forward a research roadmap involving three complimentary axes, namely, (I) the use of FCA-based mechanisms for managing configuration vulnerabilities, (II) the exploitation of knowledge representation techniques for automated security reasoning, and (III) the design of a cyber threat intelligence mechanism as a CKDD process. Then, we describe a machine-assisted process for cyber threat analysis which provides a holistic perspective of how these three research axes are integrated together.

Vulnerability assessment activities usually analyze new security advisories over current running systems. However, a system compromised in the past by a vulnerability unknown at that moment may still constitute a potential security threat in the present. Accordingly, past unknown system exposures are required to be taken into account. We present in this paper a novel approach for increasing the overall security of computing systems by identifying past hidden vulnerable states. In that context, we propose a modeling for detecting unknown past system exposures as well as an OVAL-based distributed framework for autonomously gathering network devices information and automatically analyzing their past security exposure. We also describe an implementation prototype and evaluate its performance through an extensive set of experiments.

Autonomic computing has become an important paradigm for dealing with large scale network management. However, changes operated by administrators and self-governed entities may generate vulnerable configurations increasing the exposure to security attacks. In this paper, we propose a novel approach for supporting collaborative treatments in order to remediate known security vulnerabilities in autonomic networks and systems. We put forward a mathematical formulation of vulnerability treatments as well as an XCCDF-based language for specifying them in a machine-readable manner. We describe a collaborative framework for performing these treatments taking advantage of optimized algorithms, and evaluate its performance in order to show the feasibility of our solution.

Changes that are operated by autonomic networks and systems may generate vulnerabilities and increase the exposure to security attacks. We present in this paper a new approach for increasing vulnerability awareness in such self-managed environments. Our objective is to enable autonomic networks to take advantage of the knowledge provided by vulnerability descriptions in order to maintain safe configurations. In that context, we propose a modeling and an architecture for automatically translating these descriptions into policy rules that are interpretable by an autonomic configuration system. We also describe an implementation prototype and evaluate its performance through an extensive set of experiments.

Over the last years, Industrial Control Systems (ICS) have become increasingly exposed to a wide range of cyber-physical threats. Efficient models and techniques able to capture their complex structure and identify critical cyber-physical components are therefore essential. AND/OR graphs have proven very useful in this context as they are able to semantically grasp intricate logical interdependencies among ICS components. However, identifying critical nodes in AND/OR graphs is an NP-complete problem. In addition, ICS settings normally involve various cyber and physical security measures that simultaneously protect multiple ICS components in overlapping manners, which makes this problem even harder. In this paper, we present an extended security metric based on AND/OR hypergraphs which efficiently identifies the set of critical ICS components and security measures that should be compromised, with minimum cost (effort) for an attacker, in order to disrupt the operation of vital ICS assets. Our approach relies on MAX-SAT techniques, which we have incorporated in META4ICS, a Java-based security metric analyser for ICS. We also provide a thorough performance evaluation that shows the feasibility of our method. Finally, we illustrate our methodology through a case study in which we analyse the security posture of a realistic Water Transport Network (WTN).

In this paper, we describe an efficient methodology to guide investigators during network forensic analysis. To this end, we introduce the concept of core attack graph, a compact representation of the main routes an attacker can take towards specific network targets. Such compactness allows forensic investigators to focus their efforts on critical nodes that are more likely to be part of attack paths, thus reducing the overall number of nodes (devices, network privileges) that need to be examined. Nevertheless, core graphs also allow investigators to hierarchically explore the graph in order to retrieve different levels of summarised information. We have evaluated our approach over different network topologies varying parameters such as network size, density, and forensic evaluation threshold. Our results demonstrate that we can achieve the same level of accuracy provided by standard logical attack graphs while significantly reducing the exploration rate of the network.

•Flexible model to represent complex dependencies in multi-protected ICS environments.•Novel security metric and algorithms to identify critical cyber-physical components.•META4ICS, an open source tool to analyse real ICS models.•Extensive experimental evaluation on performance and scalability aspects.•A thorough case study conducted on a realistic Water Transport Network (WTN). [Display omitted] In recent years, Industrial Control Systems (ICS) have become increasingly exposed to a wide range of cyber-physical attacks, having massive destructive consequences. Security metrics are therefore essential to assess and improve their security posture. In this paper, we present a novel ICS security metric based on AND/OR graphs and hypergraphs which is able to efficiently identify the set of critical ICS components and security measures that should be compromised, with minimum cost (effort) for an attacker, in order to disrupt the operation of vital ICS assets. Our tool, META4ICS (pronounced as metaphorics), leverages state-of-the-art methods from the field of logical satisfiability optimisation and MAX-SAT techniques in order to achieve efficient computation times. In addition, we present a case study where we have used our system to analyse the security posture of a realistic Water Transport Network (WTN).

Monitoring systems are essential to understand and control the behaviour of systems and networks. Cyber-physical systems (CPS) are particularly delicate under that perspective since they involve real-time constraints and physical phenomena that are not usually considered in common IT solutions. Therefore, there is a need for publicly available monitoring tools able to contemplate these aspects. In this poster/demo, we present our initiative, called CPS-MT, towards a versatile, real-time CPS monitoring tool, with a particular focus on security research. We first present its architecture and main components, followed by a MiniCPS-based case study. We also describe a performance analysis and preliminary results. During the demo, we will discuss CPS-MT's capabilities and limitations for security applications.

Vulnerability management constitutes a crucial activity within autonomic networks and systems. Distributed vulnerabilities must be assessed over a consolidated view of the network in order to detect vulnerable states that may simultaneously involve two or more devices. In this work, we present a novel approach for describing and assessing distributed vulnerabilities in such self-governed environments. We put forward a mathematical construction for defining distributed vulnerabilities as well as an extension of the OVAL language called DOVAL for describing them. We then define a framework for assessing distributed vulnerabilities in autonomic environments that exploits the knowledge provided by such descriptions. We finally show the feasibility of our solution by analyzing the behavior of the proposed algorithms and strategies through a comprehensive set of experiments.

Attack graphs are a powerful tool for security risk assessment by analysing network vulnerabilities and the paths attackers can use to compromise network resources. The uncertainty about the attacker's behaviour makes Bayesian networks suitable to model attack graphs to perform static and dynamic analysis. Previous approaches have focused on the formalization of attack graphs into a Bayesian model rather than proposing mechanisms for their analysis. In this paper we propose to use efficient algorithms to make exact inference in Bayesian attack graphs, enabling the static and dynamic network risk assessments. To support the validity of our approach we have performed an extensive experimental evaluation on synthetic Bayesian attack graphs with different topologies, showing the computational advantages in terms of time and memory use of the proposed techniques when compared to existing approaches.

Attack graphs constitute a powerful security tool aimed at modelling the many ways in which an attacker may compromise different assets in a network. Despite their usefulness in several security-related activities (e.g. hardening, monitoring, forensics), the complexity of these graphs can massively grow as the network becomes denser and larger, thus defying their practical usability. In this presentation, we first describe some of the problems that currently challenge the practical use of attack graphs. We then explain our approach based on core attack graphs, a novel perspective to address attack graph complexity. Finally, we present Naggen, a tool for generating, visualising and exploring core attack graphs. We use Naggen to show the advantages of our approach on different security applications.

We propose a model to represent the health of WSNs that allows us to evaluate a network's ability to execute its functions. Central to this model is how we quantify the importance of each network node. As we focus on the availability of the network data, we investigate how well different centrality measures identify the significance of each node for the network connectivity. In this process, we propose a new metric named current-flow sink betweenness. Through a number of experiments, we demonstrate that while no metric is invariably better in identifying sensors' connectivity relevance, the proposed current-flow sink betweenness outperforms existing metrics in the vast majority of cases.

Autonomic networks and services are exposed to a large variety of security risks. The vulnerability management process plays a crucial role for ensuring their safe configurations and preventing security attacks. We focus in this survey on the assessment of vulnerabilities in autonomic environments. In particular, we analyze current methods and techniques contributing to the discovery, the description and the detection of these vulnerabilities. We also point out important challenges that should be faced in order to fully integrate this process into the autonomic management plane.

Computer and network systems are consistently exposed to security threats, making their management even more complex. The management of known vulnerabilities plays a crucial role for ensuring their safe configurations and preventing security attacks. However, this activity should not generate new vulnerable states. In this paper we present a novel approach for autonomously assessing and remediating vulnerabilities. We describe a detailed mathematical model that supports this activity and we formalize the remediation decision process as a SAT problem. We present a framework that is able to assess OVAL vulnerability descriptions and perform corrective actions by using XCCDF-based descriptions of future machine states and the NETCONF protocol. We also provide details of our implementation and evaluate its feasibility through a comprehensive set of experiments.

The nature of computer crimes has systematically evolved with the progress of computer technologies. Due to the complexity of forensic investigations, the design of new techniques and tools for speeding up and automating tasks required by digital forensic processes has become a challenging task. In particular, the collection of (live) digital evidence is a delicate work that requires special care and proved investigator skills. This work presents a framework for the specification of collection procedures based on an extension of the OVAL language and describes a tool that has been implemented to automate the execution of those procedures.

The development of mobile technologies and services has contributed to the large-scale deployment of smartphones and tablets. These environments are exposed to a wide range of security attacks and may contain critical information about users such as contact directories and phone calls. Assessing configuration vulnerabilities is a key challenge for maintaining their security, but this activity should be performed in a lightweight manner in order to minimize the impact on their scarce resources. In this paper we present a novel approach for assessing configuration vulnerabilities in mobile devices by using a probabilistic cost-efficient security framework. We put forward a probabilistic assessment strategy supported by a mathematical model and detail our assessment framework based on OVAL vulnerability descriptions. We also describe an implementation prototype and evaluate its feasibility through a comprehensive set of experiments.

Attack graphs are a fundamental security tool focused on depicting how multi-stage attacks can be carried out through a network to compromise specific assets and systems. While attack graphs have been widely utilised in the IT cyber domain, their use in Operational Technology (OT) environments requires new approaches able to properly model and analyse Cyber-Physical Systems (CPS). In this paper, we introduce Cyber-Physical Attack Graphs (CPAGs) as a class of attack graphs able to cover both cyber and physical aspects. CPAGs aim at extending the reach of standard attack graphs to cyber-physical networks typically observed in industrial environments and critical infrastructure systems, analyse how an attacker can move within the network, and understand the impact that these actions may have on the system. We propose a constructive methodology to design CPAGs backed up by a formal rule-based approach that specifies how integral parts of the model can be generated and later composed to build more complex CPAGs. We then explore the semantics of CPAGs associated to cyber and physical attack actions as well as their impact on CPS environments. We also discuss potential CPAG-based analysis techniques and focus on risk analysis using Bayesian CPAGs. Finally, we show the application of the proposed model over a realistic scenario on smart farming using our open source tool T-CITY.

This paper presents a MaxSAT benchmark focused on identifying critical nodes in AND/OR graphs. We use AND/OR graphs to model Industrial Control Systems (ICS) as they are able to semantically grasp intricate logical interdependencies among ICS components. However, identifying critical nodes in AND/OR graphs is an NP-complete problem. We address this problem by efficiently transforming the input AND/OR graph-based model into a weighted logical formula that is then used to build and solve a Weighted Partial MaxSAT problem. The benchmark includes 80 cases with AND/OR graphs of different size and composition as well as the optimal cost and solution for each case.

In recent years, Industrial Control Systems (ICS) have become an appealing target for cyber attacks, having massive destructive consequences. Security metrics are therefore essential to assess their security posture. In this paper, we present a novel ICS security metric based on AND/OR graphs that represent cyber-physical dependencies among network components. Our metric is able to efficiently identify sets of critical cyber-physical components, with minimal cost for an attacker, such that if compromised, the system would enter into a non-operational state. We address this problem by efficiently transforming the input AND/OR graph-based model into a weighted logical formula that is then used to build and solve a Weighted Partial MAX-SAT problem. Our tool, META4ICS, leverages state-of-the-art techniques from the field of logical satisfiability optimisation in order to achieve efficient computation times. Our experimental results indicate that the proposed security metric can efficiently scale to networks with thousands of nodes and be computed in seconds. In addition, we present a case study where we have used our system to analyse the security posture of a realistic water transport network. We discuss our findings on the plant as well as further security applications of our metric.