8,917 research outputs found
AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments
This report considers the application of Articial Intelligence (AI) techniques to
the problem of misuse detection and misuse localisation within telecommunications
environments. A broad survey of techniques is provided, that covers inter alia
rule based systems, model-based systems, case based reasoning, pattern matching,
clustering and feature extraction, articial neural networks, genetic algorithms, arti
cial immune systems, agent based systems, data mining and a variety of hybrid
approaches. The report then considers the central issue of event correlation, that
is at the heart of many misuse detection and localisation systems. The notion of
being able to infer misuse by the correlation of individual temporally distributed
events within a multiple data stream environment is explored, and a range of techniques,
covering model based approaches, `programmed' AI and machine learning
paradigms. It is found that, in general, correlation is best achieved via rule based approaches,
but that these suffer from a number of drawbacks, such as the difculty of
developing and maintaining an appropriate knowledge base, and the lack of ability
to generalise from known misuses to new unseen misuses. Two distinct approaches
are evident. One attempts to encode knowledge of known misuses, typically within
rules, and use this to screen events. This approach cannot generally detect misuses
for which it has not been programmed, i.e. it is prone to issuing false negatives.
The other attempts to `learn' the features of event patterns that constitute normal
behaviour, and, by observing patterns that do not match expected behaviour, detect
when a misuse has occurred. This approach is prone to issuing false positives,
i.e. inferring misuse from innocent patterns of behaviour that the system was not
trained to recognise. Contemporary approaches are seen to favour hybridisation,
often combining detection or localisation mechanisms for both abnormal and normal
behaviour, the former to capture known cases of misuse, the latter to capture
unknown cases. In some systems, these mechanisms even work together to update
each other to increase detection rates and lower false positive rates. It is concluded
that hybridisation offers the most promising future direction, but that a rule or state
based component is likely to remain, being the most natural approach to the correlation
of complex events. The challenge, then, is to mitigate the weaknesses of
canonical programmed systems such that learning, generalisation and adaptation
are more readily facilitated
Software-Defined Networks Supporting Time-Sensitive In-Vehicular Communication
Future in-vehicular networks will be based on Ethernet. The IEEE
Time-Sensitive Networking (TSN) is a promising candidate to satisfy real-time
requirements in future car communication. Software-Defined Networking (SDN)
extends the Ethernet control plane with a programming option that can add much
value to the resilience, security, and adaptivity of the automotive
environment. In this work, we derive a first concept for combining
Software-Defined Networking with Time-Sensitive Networking along with an
initial evaluation. Our measurements are performed via a simulation that
investigates whether an SDN architecture is suitable for time-critical
applications in the car. Our findings indicate that the control overhead of SDN
can be added without a delay penalty for the TSN traffic when protocols are
mapped properly.Comment: To be published at IEEE VTC2019-Sprin
Software security requirements engineering: State of the art
Software Engineering has established techniques, methods and technology over two decades. However, due to the lack of understanding of software security vulnerabilities, we have not been so successful in applying software engineering principles that have been established for the past at least 25 years, when developing secure software systems. Therefore, software security can not be just added after a system has been built and delivered to customers as seen in today’s software applications. This keynote paper provides concise methods, techniques, and best practice requirements guidelines on software security and also discusses an Integrated-Secure SDLC model (IS-SDLC), which will benefit practitioners, researchers, learners, and educators
Questions related to Bitcoin and other Informational Money
A collection of questions about Bitcoin and its hypothetical relatives
Bitguilder and Bitpenny is formulated. These questions concern technical issues
about protocols, security issues, issues about the formalizations of
informational monies in various contexts, and issues about forms of use and
misuse. Some questions are formulated in the more general setting of
informational monies and near-monies.
We also formulate questions about legal, psychological, and ethical aspects
of informational money. Finally we formulate a number of questions concerning
the economical merits of and outlooks for Bitcoin.Comment: 31 pages. In v2 the section on patterns for use and misuse has been
improved and expanded with so-called contaminations. Other small improvements
were made and 13 additional references have been include
CEPS Task Force on Artificial Intelligence and Cybersecurity Technology, Governance and Policy Challenges Task Force Evaluation of the HLEG Trustworthy AI Assessment List (Pilot Version). CEPS Task Force Report 22 January 2020
The Centre for European Policy Studies launched a Task Force on Artificial Intelligence (AI) and
Cybersecurity in September 2019. The goal of this Task Force is to bring attention to the market,
technical, ethical and governance challenges posed by the intersection of AI and cybersecurity,
focusing both on AI for cybersecurity but also cybersecurity for AI. The Task Force is multi-stakeholder
by design and composed of academics, industry players from various sectors, policymakers and civil
society.
The Task Force is currently discussing issues such as the state and evolution of the application of AI
in cybersecurity and cybersecurity for AI; the debate on the role that AI could play in the dynamics
between cyber attackers and defenders; the increasing need for sharing information on threats and
how to deal with the vulnerabilities of AI-enabled systems; options for policy experimentation; and
possible EU policy measures to ease the adoption of AI in cybersecurity in Europe.
As part of such activities, this report aims at assessing the High-Level Expert Group (HLEG) on AI Ethics
Guidelines for Trustworthy AI, presented on April 8, 2019. In particular, this report analyses and
makes suggestions on the Trustworthy AI Assessment List (Pilot version), a non-exhaustive list aimed
at helping the public and the private sector in operationalising Trustworthy AI. The list is composed
of 131 items that are supposed to guide AI designers and developers throughout the process of
design, development, and deployment of AI, although not intended as guidance to ensure
compliance with the applicable laws. The list is in its piloting phase and is currently undergoing a
revision that will be finalised in early 2020.
This report would like to contribute to this revision by addressing in particular the interplay between
AI and cybersecurity. This evaluation has been made according to specific criteria: whether and how
the items of the Assessment List refer to existing legislation (e.g. GDPR, EU Charter of Fundamental
Rights); whether they refer to moral principles (but not laws); whether they consider that AI attacks
are fundamentally different from traditional cyberattacks; whether they are compatible with
different risk levels; whether they are flexible enough in terms of clear/easy measurement,
implementation by AI developers and SMEs; and overall, whether they are likely to create obstacles
for the industry.
The HLEG is a diverse group, with more than 50 members representing different stakeholders, such
as think tanks, academia, EU Agencies, civil society, and industry, who were given the difficult task of
producing a simple checklist for a complex issue. The public engagement exercise looks successful
overall in that more than 450 stakeholders have signed in and are contributing to the process.
The next sections of this report present the items listed by the HLEG followed by the analysis and
suggestions raised by the Task Force (see list of the members of the Task Force in Annex 1)
Software security requirements management as an emerging cloud computing service
© 2016 Elsevier Ltd. All rights reserved.Emerging cloud applications are growing rapidly and the need for identifying and managing service requirements is also highly important and critical at present. Software Engineering and Information Systems has established techniques, methods and technology over two decades to help achieve cloud service requirements, design, development, and testing. However, due to the lack of understanding of software security vulnerabilities that should have been identified and managed during the requirements engineering phase, we have not been so successful in applying software engineering, information management, and requirements management principles that have been established for the past at least 25 years, when developing secure software systems. Therefore, software security cannot just be added after a system has been built and delivered to customers as seen in today's software applications. This paper provides concise methods, techniques, and best practice requirements engineering and management as an emerging cloud service (SSREMaaES) and also provides guidelines on software security as a service. This paper also discusses an Integrated-Secure SDLC model (IS-SDLC), which will benefit practitioners, researchers, learners, and educators. This paper illustrates our approach for a large cloud system Amazon EC2 service
Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS - a collection of Technical Notes Part 1
This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and issues, Resilience and Safety Requirements, Open Systems Perspective and Formal Verification and Static Analysis of ML Systems. Part 2: Simulation and Dynamic Testing, Defence in Depth and Diversity, Security-Informed Safety Analysis, Standards and Guidelines
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