12,433 research outputs found
Classification of logical vulnerability based on group attacking method
New advancement in the field of e-commerce software technology has also brought many benefits, at the same time developing process always face different sort of problems from design phase to implement phase. Software faults and defects increases the issues of reliability and security, that’s reason why a solution of this problem is required to fortify these issues. The paper addresses the problem associated with lack of clear component-based web application related classification of logical vulnerabilities through identifying Attack Group Method by categorizing two different types of vulnerabilities in component-based web applications. A new classification scheme of logical group attack method is proposed and developed by using a Posteriori Empirically methodology
Model the System from Adversary Viewpoint: Threats Identification and Modeling
Security attacks are hard to understand, often expressed with unfriendly and
limited details, making it difficult for security experts and for security
analysts to create intelligible security specifications. For instance, to
explain Why (attack objective), What (i.e., system assets, goals, etc.), and
How (attack method), adversary achieved his attack goals. We introduce in this
paper a security attack meta-model for our SysML-Sec framework, developed to
improve the threat identification and modeling through the explicit
representation of security concerns with knowledge representation techniques.
Our proposed meta-model enables the specification of these concerns through
ontological concepts which define the semantics of the security artifacts and
introduced using SysML-Sec diagrams. This meta-model also enables representing
the relationships that tie several such concepts together. This representation
is then used for reasoning about the knowledge introduced by system designers
as well as security experts through the graphical environment of the SysML-Sec
framework.Comment: In Proceedings AIDP 2014, arXiv:1410.322
Towards quantum enhanced adversarial robustness in machine learning
Machine learning algorithms are powerful tools for data driven tasks such as
image classification and feature detection, however their vulnerability to
adversarial examples - input samples manipulated to fool the algorithm -
remains a serious challenge. The integration of machine learning with quantum
computing has the potential to yield tools offering not only better accuracy
and computational efficiency, but also superior robustness against adversarial
attacks. Indeed, recent work has employed quantum mechanical phenomena to
defend against adversarial attacks, spurring the rapid development of the field
of quantum adversarial machine learning (QAML) and potentially yielding a new
source of quantum advantage. Despite promising early results, there remain
challenges towards building robust real-world QAML tools. In this review we
discuss recent progress in QAML and identify key challenges. We also suggest
future research directions which could determine the route to practicality for
QAML approaches as quantum computing hardware scales up and noise levels are
reduced.Comment: 10 Pages, 4 Figure
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