11,394 research outputs found
Resilience of multi-robot systems to physical masquerade attacks
The advent of autonomous mobile multi-robot systems has driven innovation in both the industrial and defense sectors. The integration of such systems in safety-and security-critical applications has raised concern over their resilience to attack. In this work, we investigate the security problem of a stealthy adversary masquerading as a properly functioning agent. We show that conventional multi-agent pathfinding solutions are vulnerable to these physical masquerade attacks. Furthermore, we provide a constraint-based formulation of multi-agent pathfinding that yields multi-agent plans that are provably resilient to physical masquerade attacks. This formalization leverages inter-agent observations to facilitate introspective monitoring to guarantee resilience.Accepted manuscrip
Masquerade attack detection through observation planning for multi-robot systems
The increasing adoption of autonomous mobile robots comes with
a rising concern over the security of these systems. In this work, we
examine the dangers that an adversary could pose in a multi-agent
robot system. We show that conventional multi-agent plans are
vulnerable to strong attackers masquerading as a properly functioning
agent. We propose a novel technique to incorporate attack
detection into the multi-agent path-finding problem through the
simultaneous synthesis of observation plans. We show that by
specially crafting the multi-agent plan, the induced inter-agent
observations can provide introspective monitoring guarantees; we
achieve guarantees that any adversarial agent that plans to break
the system-wide security specification must necessarily violate the
induced observation plan.Accepted manuscrip
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
Security challenges of small cell as a service in virtualized mobile edge computing environments
Research on next-generation 5G wireless networks is currently attracting a lot of attention in both academia and industry. While 5G development and standardization activities are still at their early stage, it is widely acknowledged that 5G systems are going to extensively rely on dense small cell deployments, which would exploit infrastructure and network functions virtualization (NFV), and push the network intelligence towards network edges by embracing the concept of mobile edge computing (MEC). As security will be a fundamental enabling factor of small cell as a service (SCaaS) in 5G networks, we present the most prominent threats and vulnerabilities against a broad range of targets. As far as the related work is concerned, to the best of our knowledge, this paper is the first to investigate security challenges at the intersection of SCaaS, NFV, and MEC. It is also the first paper that proposes a set of criteria to facilitate a clear and effective taxonomy of security challenges of main elements of 5G networks. Our analysis can serve as a staring point towards the development of appropriate 5G security solutions. These will have crucial effect on legal and regulatory frameworks as well as on decisions of businesses, governments, and end-users
Resilient and Decentralized Control of Multi-level Cooperative Mobile Networks to Maintain Connectivity under Adversarial Environment
Network connectivity plays an important role in the information exchange
between different agents in the multi-level networks. In this paper, we
establish a game-theoretic framework to capture the uncoordinated nature of the
decision-making at different layers of the multi-level networks. Specifically,
we design a decentralized algorithm that aims to maximize the algebraic
connectivity of the global network iteratively. In addition, we show that the
designed algorithm converges to a Nash equilibrium asymptotically and yields an
equilibrium network. To study the network resiliency, we introduce three
adversarial attack models and characterize their worst-case impacts on the
network performance. Case studies based on a two-layer mobile robotic network
are used to corroborate the effectiveness and resiliency of the proposed
algorithm and show the interdependency between different layers of the network
during the recovery processes.Comment: 9 pages, 6 figure
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