7,387 research outputs found
Enhancing Trust –A Unified Meta-Model for Software Security Vulnerability Analysis
Over the last decade, a globalization of the software industry has taken place which has facilitated the sharing and reuse of code across existing project boundaries. At the same time, such global reuse also introduces new challenges to the Software Engineering community, with not only code implementation being shared across systems but also any vulnerabilities it is exposed to as well. Hence, vulnerabilities found in APIs no longer affect only individual projects but instead might spread across projects and even global software ecosystem borders. Tracing such vulnerabilities on a global scale becomes an inherently difficult task, with many of the resources required for the analysis not only growing at unprecedented rates but also being spread across heterogeneous resources. Software developers are struggling to identify and locate the required data to take full advantage of these resources. The Semantic Web and its supporting technology stack have been widely promoted to model, integrate, and support interoperability among heterogeneous data sources.
This dissertation introduces four major contributions to address these challenges: (1) It provides a literature review of the use of software vulnerabilities databases (SVDBs) in the Software Engineering community. (2) Based on findings from this literature review, we present SEVONT, a Semantic Web based modeling approach to support a formal and semi-automated approach for unifying vulnerability information resources. SEVONT introduces a multi-layer knowledge model which not only provides a unified knowledge representation, but also captures software vulnerability information at different abstract levels to allow for seamless integration, analysis, and reuse of the modeled knowledge. The modeling approach takes advantage of Formal Concept Analysis (FCA) to guide knowledge engineers in identifying reusable knowledge concepts and modeling them. (3) A Security Vulnerability Analysis Framework (SV-AF) is introduced, which is an instantiation of the SEVONT knowledge model to support evidence-based vulnerability detection. The framework integrates vulnerability ontologies (and data) with existing Software Engineering ontologies allowing for the use of Semantic Web reasoning services to trace and assess the impact of security vulnerabilities across project boundaries.
Several case studies are presented to illustrate the applicability and flexibility of our modelling approach, demonstrating that the presented knowledge modeling approach cannot only unify heterogeneous vulnerability data sources but also enables new types of vulnerability analysis
Predicting Network Attacks Using Ontology-Driven Inference
Graph knowledge models and ontologies are very powerful modeling and re
asoning tools. We propose an effective approach to model network attacks and
attack prediction which plays important roles in security management. The goals
of this study are: First we model network attacks, their prerequisites and
consequences using knowledge representation methods in order to provide
description logic reasoning and inference over attack domain concepts. And
secondly, we propose an ontology-based system which predicts potential attacks
using inference and observing information which provided by sensory inputs. We
generate our ontology and evaluate corresponding methods using CAPEC, CWE, and
CVE hierarchical datasets. Results from experiments show significant capability
improvements comparing to traditional hierarchical and relational models.
Proposed method also reduces false alarms and improves intrusion detection
effectiveness.Comment: 9 page
Mining Threat Intelligence about Open-Source Projects and Libraries from Code Repository Issues and Bug Reports
Open-Source Projects and Libraries are being used in software development
while also bearing multiple security vulnerabilities. This use of third party
ecosystem creates a new kind of attack surface for a product in development. An
intelligent attacker can attack a product by exploiting one of the
vulnerabilities present in linked projects and libraries.
In this paper, we mine threat intelligence about open source projects and
libraries from bugs and issues reported on public code repositories. We also
track library and project dependencies for installed software on a client
machine. We represent and store this threat intelligence, along with the
software dependencies in a security knowledge graph. Security analysts and
developers can then query and receive alerts from the knowledge graph if any
threat intelligence is found about linked libraries and projects, utilized in
their products
Vulnerable Open Source Dependencies: Counting Those That Matter
BACKGROUND: Vulnerable dependencies are a known problem in today's
open-source software ecosystems because OSS libraries are highly interconnected
and developers do not always update their dependencies. AIMS: In this paper we
aim to present a precise methodology, that combines the code-based analysis of
patches with information on build, test, update dates, and group extracted from
the very code repository, and therefore, caters to the needs of industrial
practice for correct allocation of development and audit resources. METHOD: To
understand the industrial impact of the proposed methodology, we considered the
200 most popular OSS Java libraries used by SAP in its own software. Our
analysis included 10905 distinct GAVs (group, artifact, version) when
considering all the library versions. RESULTS: We found that about 20% of the
dependencies affected by a known vulnerability are not deployed, and therefore,
they do not represent a danger to the analyzed library because they cannot be
exploited in practice. Developers of the analyzed libraries are able to fix
(and actually responsible for) 82% of the deployed vulnerable dependencies. The
vast majority (81%) of vulnerable dependencies may be fixed by simply updating
to a new version, while 1% of the vulnerable dependencies in our sample are
halted, and therefore, potentially require a costly mitigation strategy.
CONCLUSIONS: Our case study shows that the correct counting allows software
development companies to receive actionable information about their library
dependencies, and therefore, correctly allocate costly development and audit
resources, which is spent inefficiently in case of distorted measurements.Comment: This is a pre-print of the paper that appears, with the same title,
in the proceedings of the 12th International Symposium on Empirical Software
Engineering and Measurement, 201
Towards a Cloud-Based Ontology for Service Model Security -- Technical Report
The adoption of cloud computing has brought significant advancements in the
operational models of businesses. However, this shift also brings new security
challenges by expanding the attack surface. The offered services in cloud
computing have various service models. Each cloud service model has a defined
responsibility divided based on the stack layers between the service user and
their cloud provider. Regardless of its service model, each service is
constructed from sub-components and services running on the underlying layers.
In this paper, we aim to enable more transparency and visibility by designing
an ontology that links the provider's services with the sub-components used to
deliver the service. Such breakdown for each cloud service sub-components
enables the end user to track the vulnerabilities on the service level or one
of its sub-components. Such information can result in a better understanding
and management of reported vulnerabilities on the sub-components level and
their impact on the offered services by the cloud provider. Our ontology and
source code are published as an open-source and accessible via GitHub:
\href{https://github.com/mohkharma/cc-ontology}{mohkharma/cc-ontology}Comment: 8 page
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