5,611 research outputs found
Vulnerability Assessment by Learning Attack Specifications in Graphs
This paper presents an evolutionary approach for learning attack specifications that describe attack scenarios. The objective is to find vulnerabilities in computer networks which minimise the cost of an attack with maximum impact. Although we focus on Insider Threat, the proposed approach applies to networks in general, including social networks and computer grid
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
A Mobile Ambients-based Approach for Network Attack Modelling and Simulation
Attack Graphs are an important support for assessment and subsequent improvement of network security. They reveal possible paths an attacker can take to break through security perimeters and traverse a network to reach valuable assets deep inside the network. Although scalability is no longer the main issue, Attack Graphs still have some problems that make them less useful in practice. First, Attack Graphs remain difficult to relate to the network topology. Second, Attack Graphs traditionally only consider the exploitation of vulnerable hosts. Third, Attack Graphs do not rely on automatic identification of potential attack targets. We address these gaps in our MsAMS (Multi-step Attack Modelling and Simulation) tool, based on Mobile Ambients. The tool not only allows the modelling of more static aspects of the network, such as the network topology, but also the dynamics of network attacks. In addition to Mobile Ambients, we use the PageRank algorithm to determine targets and hub scores produced by the HITS (Hypertext Induced Topic Search) algorithm to guide the simulation of an attacker searching for targets
Continuous Monitoring System Based on Systems\u27 Environment
We present a new framework (and its mechanisms) of a Continuous Monitoring System (CMS) having new improved capabilities, and discuss its requirements and implications. The CMS is based on the real-time actual configuration of the system and the environment rather than a theoretic or assumed configuration. Moreover, the CMS predicts organizational damages taking into account chains of impacts among systems\u27 components generated by messaging among software components. In addition, the CMS takes into account all organizational effects of an attack. Its risk measurement takes into account the consequences of a threat, as defines in risk analysis standards. Loss prediction is based on a neural network algorithm with learning and improving capabilities, rather than a fixed algorithm which typically lacks the necessary environmental dynamic updates. Framework presentation includes systems design, neural network architecture design, and an example of the detailed network architecture.
Keywords: Continuous Monitoring, Computer security, Attack graph, Software vulnerability, Risk management, Impact propagation, Cyber attack, Configuration managemen
A Survey on Trust Metrics for Autonomous Robotic Systems
This paper surveys the area of Trust Metrics related to security for
autonomous robotic systems. As the robotics industry undergoes a transformation
from programmed, task oriented, systems to Artificial Intelligence-enabled
learning, these autonomous systems become vulnerable to several security risks,
making a security assessment of these systems of critical importance.
Therefore, our focus is on a holistic approach for assessing system trust which
requires incorporating system, hardware, software, cognitive robustness, and
supplier level trust metrics into a unified model of trust. We set out to
determine if there were already trust metrics that defined such a holistic
system approach. While there are extensive writings related to various aspects
of robotic systems such as, risk management, safety, security assurance and so
on, each source only covered subsets of an overall system and did not
consistently incorporate the relevant costs in their metrics. This paper
attempts to put this prior work into perspective, and to show how it might be
extended to develop useful system-level trust metrics for evaluating complex
robotic (and other) systems
DCDIDP: A distributed, collaborative, and data-driven intrusion detection and prevention framework for cloud computing environments
With the growing popularity of cloud computing, the exploitation of possible vulnerabilities grows at the same pace; the distributed nature of the cloud makes it an attractive target for potential intruders. Despite security issues delaying its adoption, cloud computing has already become an unstoppable force; thus, security mechanisms to ensure its secure adoption are an immediate need. Here, we focus on intrusion detection and prevention systems (IDPSs) to defend against the intruders. In this paper, we propose a Distributed, Collaborative, and Data-driven Intrusion Detection and Prevention system (DCDIDP). Its goal is to make use of the resources in the cloud and provide a holistic IDPS for all cloud service providers which collaborate with other peers in a distributed manner at different architectural levels to respond to attacks. We present the DCDIDP framework, whose infrastructure level is composed of three logical layers: network, host, and global as well as platform and software levels. Then, we review its components and discuss some existing approaches to be used for the modules in our proposed framework. Furthermore, we discuss developing a comprehensive trust management framework to support the establishment and evolution of trust among different cloud service providers. © 2011 ICST
Analysis of the NIST database towards the composition of vulnerabilities in attack scenarios
The composition of vulnerabilities in attack scenarios has been traditionally performed based on detailed pre- and post-conditions. Although very precise, this approach is dependent on human analysis, is time consuming, and not at all scalable. We investigate the NIST National Vulnerability Database (NVD) with three goals: (i) understand the associations among vulnerability attributes related to impact, exploitability, privilege, type of vulnerability and clues derived from plaintext descriptions, (ii) validate our initial composition model which is based on required access and resulting effect, and (iii) investigate the maturity of XML database technology for performing statistical analyses like this directly on the XML data. In this report, we analyse 27,273 vulnerability entries (CVE 1) from the NVD. Using only nominal information, we are able to e.g. identify clusters in the class of vulnerabilities with no privilege which represent 52% of the entries
SHARKS: Smart Hacking Approaches for RisK Scanning in Internet-of-Things and Cyber-Physical Systems based on Machine Learning
Cyber-physical systems (CPS) and Internet-of-Things (IoT) devices are
increasingly being deployed across multiple functionalities, ranging from
healthcare devices and wearables to critical infrastructures, e.g., nuclear
power plants, autonomous vehicles, smart cities, and smart homes. These devices
are inherently not secure across their comprehensive software, hardware, and
network stacks, thus presenting a large attack surface that can be exploited by
hackers. In this article, we present an innovative technique for detecting
unknown system vulnerabilities, managing these vulnerabilities, and improving
incident response when such vulnerabilities are exploited. The novelty of this
approach lies in extracting intelligence from known real-world CPS/IoT attacks,
representing them in the form of regular expressions, and employing machine
learning (ML) techniques on this ensemble of regular expressions to generate
new attack vectors and security vulnerabilities. Our results show that 10 new
attack vectors and 122 new vulnerability exploits can be successfully generated
that have the potential to exploit a CPS or an IoT ecosystem. The ML
methodology achieves an accuracy of 97.4% and enables us to predict these
attacks efficiently with an 87.2% reduction in the search space. We demonstrate
the application of our method to the hacking of the in-vehicle network of a
connected car. To defend against the known attacks and possible novel exploits,
we discuss a defense-in-depth mechanism for various classes of attacks and the
classification of data targeted by such attacks. This defense mechanism
optimizes the cost of security measures based on the sensitivity of the
protected resource, thus incentivizing its adoption in real-world CPS/IoT by
cybersecurity practitioners.Comment: This article has been accepted in IEEE Transactions on Emerging
Topics in Computing. 17 pages, 12 figures, IEEE copyrigh
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