Exact Inference Techniques for the Analysis of Bayesian Attack Graphs

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.Comment: 14 pages, 15 figure

MaxSAT Evaluation 2020 -- Benchmark: Identifying Maximum Probability Minimal Cut Sets in Fault Trees

This paper presents a MaxSAT benchmark focused on the identification of Maximum Probability Minimal Cut Sets (MPMCSs) in fault trees. We address the MPMCS problem by transforming the input fault tree into a weighted logical formula that is then used to build and solve a Weighted Partial MaxSAT problem. The benchmark includes 80 cases with fault trees of different size and composition as well as the optimal cost and solution for each case.Comment: 5 pages, 1 figure. To appear in Proceedings of the MaxSAT Evaluation 2020 (MSE'20). https://maxsat-evaluations.github.io/2020

Identifying Security-Critical Cyber-Physical Components in Industrial Control Systems

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.Comment: Keywords: Security metrics, industrial control systems, cyber-physical systems, AND-OR graphs, MAX-SAT resolutio

Ovalyzer: an OVAL to Cfengine Translator

International audienceIn this demo we show how we can increase the vulnerability awareness of self-governed environments by feeding the autonomic system Cfengine with security advisories taken from OVAL repositories and automatically translated by Ovalyzer. Ovalyzer is an extensible plugin-based OVAL to Cfengine translator capable of translating OVAL documents to Cfengine policy rules that represent them. These policy rules can be later consumed by Cfengine agents in order to assess their own security exposure in an autonomous manner

Collaborative Remediation of Configuration Vulnerabilities in Autonomic Networks and Systems

International audienceAutonomic 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

Improving Present Security through the Detection of Past Hidden Vulnerable States

International audienceVulnerability 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

Increasing Android Security using a Lightweight OVAL-based Vulnerability Assessment Framework

International audienceMobile 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 paper a novel approach for increasing the security of mobile devices by efficiently detecting vulnerable configurations. In that context, we propose a modeling for performing vulnerability assessment activities as well as an OVAL-based distributed framework for ensuring safe configurations within the Android platform. We also describe an implementation prototype and evaluate its performance through an extensive set of experiments

A Probabilistic Cost-efficient Approach for Mobile Security Assessment

International audienceThe 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

Machine-assisted Cyber Threat Analysis using Conceptual Knowledge Discovery: – Position Paper –

International audienceOver 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