5,082 research outputs found
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
Game Theory Meets Network Security: A Tutorial at ACM CCS
The increasingly pervasive connectivity of today's information systems brings
up new challenges to security. Traditional security has accomplished a long way
toward protecting well-defined goals such as confidentiality, integrity,
availability, and authenticity. However, with the growing sophistication of the
attacks and the complexity of the system, the protection using traditional
methods could be cost-prohibitive. A new perspective and a new theoretical
foundation are needed to understand security from a strategic and
decision-making perspective. Game theory provides a natural framework to
capture the adversarial and defensive interactions between an attacker and a
defender. It provides a quantitative assessment of security, prediction of
security outcomes, and a mechanism design tool that can enable
security-by-design and reverse the attacker's advantage. This tutorial provides
an overview of diverse methodologies from game theory that includes games of
incomplete information, dynamic games, mechanism design theory to offer a
modern theoretic underpinning of a science of cybersecurity. The tutorial will
also discuss open problems and research challenges that the CCS community can
address and contribute with an objective to build a multidisciplinary bridge
between cybersecurity, economics, game and decision theory
Privacy-Preserving Distributed Optimization via Subspace Perturbation: A General Framework
As the modern world becomes increasingly digitized and interconnected,
distributed signal processing has proven to be effective in processing its
large volume of data. However, a main challenge limiting the broad use of
distributed signal processing techniques is the issue of privacy in handling
sensitive data. To address this privacy issue, we propose a novel yet general
subspace perturbation method for privacy-preserving distributed optimization,
which allows each node to obtain the desired solution while protecting its
private data. In particular, we show that the dual variables introduced in each
distributed optimizer will not converge in a certain subspace determined by the
graph topology. Additionally, the optimization variable is ensured to converge
to the desired solution, because it is orthogonal to this non-convergent
subspace. We therefore propose to insert noise in the non-convergent subspace
through the dual variable such that the private data are protected, and the
accuracy of the desired solution is completely unaffected. Moreover, the
proposed method is shown to be secure under two widely-used adversary models:
passive and eavesdropping. Furthermore, we consider several distributed
optimizers such as ADMM and PDMM to demonstrate the general applicability of
the proposed method. Finally, we test the performance through a set of
applications. Numerical tests indicate that the proposed method is superior to
existing methods in terms of several parameters like estimated accuracy,
privacy level, communication cost and convergence rate
Keys in the Clouds: Auditable Multi-device Access to Cryptographic Credentials
Personal cryptographic keys are the foundation of many secure services, but
storing these keys securely is a challenge, especially if they are used from
multiple devices. Storing keys in a centralized location, like an
Internet-accessible server, raises serious security concerns (e.g. server
compromise). Hardware-based Trusted Execution Environments (TEEs) are a
well-known solution for protecting sensitive data in untrusted environments,
and are now becoming available on commodity server platforms.
Although the idea of protecting keys using a server-side TEE is
straight-forward, in this paper we validate this approach and show that it
enables new desirable functionality. We describe the design, implementation,
and evaluation of a TEE-based Cloud Key Store (CKS), an online service for
securely generating, storing, and using personal cryptographic keys. Using
remote attestation, users receive strong assurance about the behaviour of the
CKS, and can authenticate themselves using passwords while avoiding typical
risks of password-based authentication like password theft or phishing. In
addition, this design allows users to i) define policy-based access controls
for keys; ii) delegate keys to other CKS users for a specified time and/or a
limited number of uses; and iii) audit all key usages via a secure audit log.
We have implemented a proof of concept CKS using Intel SGX and integrated this
into GnuPG on Linux and OpenKeychain on Android. Our CKS implementation
performs approximately 6,000 signature operations per second on a single
desktop PC. The latency is in the same order of magnitude as using
locally-stored keys, and 20x faster than smart cards.Comment: Extended version of a paper to appear in the 3rd Workshop on
Security, Privacy, and Identity Management in the Cloud (SECPID) 201
Multi-Layer Cyber-Physical Security and Resilience for Smart Grid
The smart grid is a large-scale complex system that integrates communication
technologies with the physical layer operation of the energy systems. Security
and resilience mechanisms by design are important to provide guarantee
operations for the system. This chapter provides a layered perspective of the
smart grid security and discusses game and decision theory as a tool to model
the interactions among system components and the interaction between attackers
and the system. We discuss game-theoretic applications and challenges in the
design of cross-layer robust and resilient controller, secure network routing
protocol at the data communication and networking layers, and the challenges of
the information security at the management layer of the grid. The chapter will
discuss the future directions of using game-theoretic tools in addressing
multi-layer security issues in the smart grid.Comment: 16 page
Personal data management with the databox: What's inside the box?
We are all increasingly the subjects of data collection and processing systems that use data generated both about and by us to provide and optimise a wide range of services. Means for others to collect and process data that concerns each of us -- often referred to possessively as "your data" -- are only increasing with the long-heralded advent of the Internet of Things just the latest example. As a result, means to enable personal data management is generally recognised as a pressing societal issue.
We have previously proposed that one such means might be realised by the Databox, a collection of physical and cloud-hosted software components that provide for an individual data subject to manage, log and audit access to their data by other parties. In this paper we elaborate on this proposal, describing the software architecture we are developing, and the current status of a prototype implementation. We conclude with a brief discussion of Databox's limitations
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