18,122 research outputs found
An Evolutionary Approach for Learning Attack Specifications in Network Graphs
This paper presents an evolutionary algorithm that learns attack scenarios, called attack specifications, from a network graph. This learning process aims to find attack specifications that minimise cost and maximise the value that an attacker gets from a successful attack. The attack specifications that the algorithm learns are represented using an approach based on Hoare's CSP (Communicating Sequential Processes). This new approach is able to represent several elements found in attacks, for example synchronisation. These attack specifications can be used by network administrators to find vulnerable scenarios, composed from the basic constructs Sequence, Parallel and Choice, that lead to valuable assets in the network
Discovering, quantifying, and displaying attacks
In the design of software and cyber-physical systems, security is often
perceived as a qualitative need, but can only be attained quantitatively.
Especially when distributed components are involved, it is hard to predict and
confront all possible attacks. A main challenge in the development of complex
systems is therefore to discover attacks, quantify them to comprehend their
likelihood, and communicate them to non-experts for facilitating the decision
process. To address this three-sided challenge we propose a protection analysis
over the Quality Calculus that (i) computes all the sets of data required by an
attacker to reach a given location in a system, (ii) determines the cheapest
set of such attacks for a given notion of cost, and (iii) derives an attack
tree that displays the attacks graphically. The protection analysis is first
developed in a qualitative setting, and then extended to quantitative settings
following an approach applicable to a great many contexts. The quantitative
formulation is implemented as an optimisation problem encoded into
Satisfiability Modulo Theories, allowing us to deal with complex cost
structures. The usefulness of the framework is demonstrated on a national-scale
authentication system, studied through a Java implementation of the framework.Comment: LMCS SPECIAL ISSUE FORTE 201
Formal Template-Based Generation of Attack–Defence Trees for Automated Security Analysis
Systems that integrate cyber and physical aspects to create cyber-physical systems (CPS) are becoming increasingly complex, but demonstrating the security of CPS is hard and security is frequently compromised. These compromises can lead to safety failures, putting lives at risk. Attack Defense Trees with sequential conjunction (ADS) are an approach to identifying attacks on a system and identifying the interaction between attacks and the defenses that are present within the CPS. We present a semantic model for ADS and propose a methodology for generating ADS automatically. The methodology takes as input a CPS system model and a library of templates of attacks and defenses. We demonstrate and validate the effectiveness of the ADS generation methodology using an example from the automotive domain
Hackers vs. Security: Attack-Defence Trees as Asynchronous Multi-Agent Systems
Attack-Defence Trees (ADTs) are well-suited to assess possible attacks to
systems and the efficiency of counter-measures. In this paper, we first enrich
the available constructs with reactive patterns that cover further security
scenarios, and equip all constructs with attributes such as time and cost to
allow quantitative analyses. Then, ADTs are modelled as (an extension of)
Asynchronous Multi-Agents Systems--EAMAS. The ADT-EAMAS transformation is
performed in a systematic manner that ensures correctness. The transformation
allows us to quantify the impact of different agents configurations on metrics
such as attack time. Using EAMAS also permits parametric verification: we
derive constraints for property satisfaction. Our approach is exercised on
several case studies using the Uppaal and IMITATOR tools.Comment: This work was partially funded by the NWO project SEQUOIA (grant
15474), EU project SUCCESS (102112) and the PHC van Gogh PAMPAS. The work of
Arias and Petrucci has been supported by the BQR project AMoJA
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