53,420 research outputs found
Estimating Impact and Frequency of Risks to Safety and Mission Critical Systems Using CVSS
Many safety and mission critical systems depend on the correct and secure operation of both supportive and core software systems. E.g., both the safety of personnel and the effective execution of core missions on an oil platform depend on the correct recording storing, transfer and interpretation of data, such as that for the Logging While Drilling (LWD) and Measurement While Drilling (MWD) subsystems. Here, data is recorded on site, packaged and then transferred to an on-shore operational centre. Today, the data is transferred on dedicated communication channels to ensure a secure and safe transfer, free from deliberately and accidental faults. However, as the cost control is ever more important some of the transfer will be over remotely accessible infrastructure in the future. Thus, communication will be prone to known security vulnerabilities exploitable by outsiders. This paper presents a model that estimates risk level of known vulnerabilities as a combination of frequency and impact estimates derived from the Common Vulnerability Scoring System (CVSS). The model is implemented as a Bayesian Belief Network (BBN)
Authentication and authorisation in entrusted unions
This paper reports on the status of a project whose aim is to implement and demonstrate in a real-life environment an integrated eAuthentication and eAuthorisation framework to enable trusted collaborations and delivery of services across different organisational/governmental jurisdictions. This aim will be achieved by designing a framework with assurance of claims, trust indicators, policy enforcement mechanisms and processing under encryption to address the security and confidentiality requirements of large distributed infrastructures. The framework supports collaborative secure distributed storage, secure data processing and management in both the cloud and offline scenarios and is intended to be deployed and tested in two pilot studies in two different domains, viz, Bio-security incident management and Ambient Assisted Living (eHealth). Interim results in terms of security requirements, privacy preserving authentication, and authorisation are reported
Quantifying Information Leaks Using Reliability Analysis
acmid: 2632367 keywords: Model Counting, Quantitative Information Flow, Reliability Analysis, Symbolic Execution location: San Jose, CA, USA numpages: 4acmid: 2632367 keywords: Model Counting, Quantitative Information Flow, Reliability Analysis, Symbolic Execution location: San Jose, CA, USA numpages: 4acmid: 2632367 keywords: Model Counting, Quantitative Information Flow, Reliability Analysis, Symbolic Execution location: San Jose, CA, USA numpages: 4We report on our work-in-progress into the use of reliability analysis to quantify information leaks. In recent work we have proposed a software reliability analysis technique that uses symbolic execution and model counting to quantify the probability of reaching designated program states, e.g. assert violations, under uncertainty conditions in the environment. The technique has many applications beyond reliability analysis, ranging from program understanding and debugging to analysis of cyber-physical systems. In this paper we report on a novel application of the technique, namely Quantitative Information Flow analysis (QIF). The goal of QIF is to measure information leakage of a program by using information-theoretic metrics such as Shannon entropy or Renyi entropy. We exploit the model counting engine of the reliability analyzer over symbolic program paths, to compute an upper bound of the maximum leakage over all possible distributions of the confidential data. We have implemented our approach into a prototype tool, called QILURA, and explore its effectiveness on a number of case studie
Estimating ToE Risk Level using CVSS
Security management is about calculated risk and requires continuous evaluation to ensure cost, time and resource effectiveness. Parts of which is to make future-oriented, cost-benefit investments in security. Security investments must adhere to healthy business principles where both security and financial aspects play an important role. Information on the current and potential risk level is essential to successfully trade-off security and financial aspects. Risk level is the combination of the frequency and impact of a potential unwanted event, often referred to as a security threat or misuse. The paper presents a risk level estimation model that derives risk level as a conditional probability over frequency and impact estimates. The frequency and impact estimates are derived from a set of attributes specified in the Common Vulnerability Scoring System (CVSS). The model works on the level of vulnerabilities (just as the CVSS) and is able to compose vulnerabilities into service levels. The service levels define the potential risk levels and are modelled as a Markov process, which are then used to predict the risk level at a particular time
Hiding Symbols and Functions: New Metrics and Constructions for Information-Theoretic Security
We present information-theoretic definitions and results for analyzing
symmetric-key encryption schemes beyond the perfect secrecy regime, i.e. when
perfect secrecy is not attained. We adopt two lines of analysis, one based on
lossless source coding, and another akin to rate-distortion theory. We start by
presenting a new information-theoretic metric for security, called symbol
secrecy, and derive associated fundamental bounds. We then introduce
list-source codes (LSCs), which are a general framework for mapping a key
length (entropy) to a list size that an eavesdropper has to resolve in order to
recover a secret message. We provide explicit constructions of LSCs, and
demonstrate that, when the source is uniformly distributed, the highest level
of symbol secrecy for a fixed key length can be achieved through a construction
based on minimum-distance separable (MDS) codes. Using an analysis related to
rate-distortion theory, we then show how symbol secrecy can be used to
determine the probability that an eavesdropper correctly reconstructs functions
of the original plaintext. We illustrate how these bounds can be applied to
characterize security properties of symmetric-key encryption schemes, and, in
particular, extend security claims based on symbol secrecy to a functional
setting.Comment: Submitted to IEEE Transactions on Information Theor
Towards Egocentric Person Re-identification and Social Pattern Analysis
Wearable cameras capture a first-person view of the daily activities of the
camera wearer, offering a visual diary of the user behaviour. Detection of the
appearance of people the camera user interacts with for social interactions
analysis is of high interest. Generally speaking, social events, lifestyle and
health are highly correlated, but there is a lack of tools to monitor and
analyse them. We consider that egocentric vision provides a tool to obtain
information and understand users social interactions. We propose a model that
enables us to evaluate and visualize social traits obtained by analysing social
interactions appearance within egocentric photostreams. Given sets of
egocentric images, we detect the appearance of faces within the days of the
camera wearer, and rely on clustering algorithms to group their feature
descriptors in order to re-identify persons. Recurrence of detected faces
within photostreams allows us to shape an idea of the social pattern of
behaviour of the user. We validated our model over several weeks recorded by
different camera wearers. Our findings indicate that social profiles are
potentially useful for social behaviour interpretation
Food web topology and nested keystone species complexes
Important species may be in critically central network positions in ecological interaction networks. Beyond quantifying
which one is the most central species in a food web, a multi-node approach can identify the key sets of the most central
n species as well. However, for sets of different size n, these structural keystone species complexes may differ in their
composition. If larger sets contain smaller sets, higher nestedness may be a proxy for predictive ecology and efficient
management of ecosystems. On the contrary, lower nestedness makes the identification of keystones more complicated.
Our question here is how the topology of a network can influence nestedness as an architectural constraint. Here, we
study the role of keystone species complexes in 27 real food webs and quantify their nestedness. After quantifying their
topology properties, we determine their keystones species complexes, calculate their nestedness and statistically analyze
the relationship between topological indices and nestedness. A better understanding of the cores of ecosystems is crucial
for efficient conservation efforts and to know which networks will have more nested keystone species complexes would
be a great help for prioritizing species that could preserve the ecosystem’s structural integrity
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