61,979 research outputs found
Detecting Key-Dependencies
The confidentiality of encrypted data depends on how well the key under which it was encrypted is maintained. If a session key was exchanged encrypted under a long-term key, exposure of the long-term key may reveal the session key and hence the data encrypted with it. The problem of key-dependencies between keys can be mapped onto connectivity of a graph, and the resulting graph can be inspected. This article presents a structured method (an algorithm) with which key-dependencies can be detected and analysed. Several well-known protocols are examined, and it is shown that they are vulnerable to certain attacks exploiting key-dependencies. Protocols which are free from this defect do exist. That is, when a session is terminated it is properly closed
Can Network Analysis Techniques help to Predict Design Dependencies? An Initial Study
The degree of dependencies among the modules of a software system is a key
attribute to characterize its design structure and its ability to evolve over
time. Several design problems are often correlated with undesired dependencies
among modules. Being able to anticipate those problems is important for
developers, so they can plan early for maintenance and refactoring efforts.
However, existing tools are limited to detecting undesired dependencies once
they appeared in the system. In this work, we investigate whether module
dependencies can be predicted (before they actually appear). Since the module
structure can be regarded as a network, i.e, a dependency graph, we leverage on
network features to analyze the dynamics of such a structure. In particular, we
apply link prediction techniques for this task. We conducted an evaluation on
two Java projects across several versions, using link prediction and machine
learning techniques, and assessed their performance for identifying new
dependencies from a project version to the next one. The results, although
preliminary, show that the link prediction approach is feasible for package
dependencies. Also, this work opens opportunities for further development of
software-specific strategies for dependency prediction.Comment: Accepted at ICSA 201
Specious rules: an efficient and effective unifying method for removing misleading and uninformative patterns in association rule mining
We present theoretical analysis and a suite of tests and procedures for
addressing a broad class of redundant and misleading association rules we call
\emph{specious rules}. Specious dependencies, also known as \emph{spurious},
\emph{apparent}, or \emph{illusory associations}, refer to a well-known
phenomenon where marginal dependencies are merely products of interactions with
other variables and disappear when conditioned on those variables.
The most extreme example is Yule-Simpson's paradox where two variables
present positive dependence in the marginal contingency table but negative in
all partial tables defined by different levels of a confounding factor. It is
accepted wisdom that in data of any nontrivial dimensionality it is infeasible
to control for all of the exponentially many possible confounds of this nature.
In this paper, we consider the problem of specious dependencies in the context
of statistical association rule mining. We define specious rules and show they
offer a unifying framework which covers many types of previously proposed
redundant or misleading association rules. After theoretical analysis, we
introduce practical algorithms for detecting and pruning out specious
association rules efficiently under many key goodness measures, including
mutual information and exact hypergeometric probabilities. We demonstrate that
the procedure greatly reduces the number of associations discovered, providing
an elegant and effective solution to the problem of association mining
discovering large numbers of misleading and redundant rules.Comment: Note: This is a corrected version of the paper published in SDM'17.
In the equation on page 4, the range of the sum has been correcte
Detecting and Refactoring Operational Smells within the Domain Name System
The Domain Name System (DNS) is one of the most important components of the
Internet infrastructure. DNS relies on a delegation-based architecture, where
resolution of names to their IP addresses requires resolving the names of the
servers responsible for those names. The recursive structures of the inter
dependencies that exist between name servers associated with each zone are
called dependency graphs. System administrators' operational decisions have far
reaching effects on the DNSs qualities. They need to be soundly made to create
a balance between the availability, security and resilience of the system. We
utilize dependency graphs to identify, detect and catalogue operational bad
smells. Our method deals with smells on a high-level of abstraction using a
consistent taxonomy and reusable vocabulary, defined by a DNS Operational
Model. The method will be used to build a diagnostic advisory tool that will
detect configuration changes that might decrease the robustness or security
posture of domain names before they become into production.Comment: In Proceedings GaM 2015, arXiv:1504.0244
Managerial Views of Corporate Impacts and Dependencies on Ecosystem Services : A Case of International and Domestic Forestry Companies in China
A line of research is emerging investigating the private sector impacts and dependencies on critical biodiversity and ecosystem services, and related business risks and opportunities. While the ecosystem services narrative is being forwarded globally as a key paradigm for promoting business sustainability, there is scarce knowledge of how these issues are considered at managerial level. This study thus investigates managerial views of corporate sustainability after the ecosystem services concept. We analyse interviews conducted with 20 managers from domestic and international forestry companies operating with a plantation-based business model in China. Content analysis was employed to analyse the data, with a focus on four key areas: (1) interviewee familiarity with the ecosystem services concept; (2) their views of corporate dependencies and impacts on ecosystem services; (3) related business risks and opportunities; and (4) viability of existing instruments and practices that can be employed in detecting and addressing business impacts and dependencies on ecosystem services. Through an inductive approach to the empirical findings, we refined a framework that holds operational value for developing company response strategies to ecosystem services impact/dependence assessment, ensuring that all issues are addressed comprehensively, and that related risks and opportunities are properly acknowledged.Peer reviewe
Method For Detecting Shilling Attacks In E-commerce Systems Using Weighted Temporal Rules
The problem of shilling attacks detecting in e-commerce systems is considered. The purpose of such attacks is to artificially change the rating of individual goods or services by users in order to increase their sales. A method for detecting shilling attacks based on a comparison of weighted temporal rules for the processes of selecting objects with explicit and implicit feedback from users is proposed. Implicit dependencies are specified through the purchase of goods and services. Explicit feedback is formed through the ratings of these products. The temporal rules are used to describe hidden relationships between the choices of user groups at two consecutive time intervals. The method includes the construction of temporal rules for explicit and implicit feedback, their comparison, as well as the formation of an ordered subset of temporal rules that capture potential shilling attacks. The method imposes restrictions on the input data on sales and ratings, which must be ordered by time or have timestamps. This method can be used in combination with other approaches to detecting shilling attacks. Integration of approaches allows to refine and supplement the existing attack patterns, taking into account the latest changes in user priorities
Identifying statistical dependence in genomic sequences via mutual information estimates
Questions of understanding and quantifying the representation and amount of
information in organisms have become a central part of biological research, as
they potentially hold the key to fundamental advances. In this paper, we
demonstrate the use of information-theoretic tools for the task of identifying
segments of biomolecules (DNA or RNA) that are statistically correlated. We
develop a precise and reliable methodology, based on the notion of mutual
information, for finding and extracting statistical as well as structural
dependencies. A simple threshold function is defined, and its use in
quantifying the level of significance of dependencies between biological
segments is explored. These tools are used in two specific applications. First,
for the identification of correlations between different parts of the maize
zmSRp32 gene. There, we find significant dependencies between the 5'
untranslated region in zmSRp32 and its alternatively spliced exons. This
observation may indicate the presence of as-yet unknown alternative splicing
mechanisms or structural scaffolds. Second, using data from the FBI's Combined
DNA Index System (CODIS), we demonstrate that our approach is particularly well
suited for the problem of discovering short tandem repeats, an application of
importance in genetic profiling.Comment: Preliminary version. Final version in EURASIP Journal on
Bioinformatics and Systems Biology. See http://www.hindawi.com/journals/bsb
How to Juggle Columns: An Entropy-Based Approach for Table Compression
Many relational databases exhibit complex dependencies between data attributes, caused either by the nature of the underlying data or by explicitly denormalized schemas. In data warehouse scenarios, calculated key figures may be materialized or hierarchy levels may be held within a single dimension table. Such column correlations and the resulting data redundancy may result in additional storage requirements. They may also result in bad query performance if inappropriate independence assumptions are made during query compilation. In this paper, we tackle the specific problem of detecting functional dependencies between columns to improve the compression rate for column-based database systems, which both reduces main memory consumption and improves query performance. Although a huge variety of algorithms have been proposed for detecting column dependencies in databases, we maintain that increased data volumes and recent developments in hardware architectures demand novel algorithms with much lower runtime overhead and smaller memory footprint. Our novel approach is based on entropy estimations and exploits a combination of sampling and multiple heuristics to render it applicable for a wide range of use cases. We demonstrate the quality of our approach by means of an implementation within the SAP NetWeaver Business Warehouse Accelerator. Our experiments indicate that our approach scales well with the number of columns and produces reliable dependence structure information. This both reduces memory consumption and improves performance for nontrivial queries
- âŠ