6,940 research outputs found
Seven properties of self-organization in the human brain
The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: 1) modular connectivity, 2) unsupervised learning, 3) adaptive ability, 4) functional resiliency, 5) functional plasticity, 6) from-local-to-global functional organization, and 7) dynamic system growth. These are defined here in the light of insight from neurobiology, cognitive neuroscience and Adaptive Resonance Theory (ART), and physics to show that self-organization achieves stability and functional plasticity while minimizing structural system complexity. A specific example informed by empirical research is discussed to illustrate how modularity, adaptive learning, and dynamic network growth enable stable yet plastic somatosensory representation for human grip force control. Implications for the design of “strong” artificial intelligence in robotics are brought forward
No Need to Know Physics: Resilience of Process-based Model-free Anomaly Detection for Industrial Control Systems
In recent years, a number of process-based anomaly detection schemes for
Industrial Control Systems were proposed. In this work, we provide the first
systematic analysis of such schemes, and introduce a taxonomy of properties
that are verified by those detection systems. We then present a novel general
framework to generate adversarial spoofing signals that violate physical
properties of the system, and use the framework to analyze four anomaly
detectors published at top security conferences. We find that three of those
detectors are susceptible to a number of adversarial manipulations (e.g.,
spoofing with precomputed patterns), which we call Synthetic Sensor Spoofing
and one is resilient against our attacks. We investigate the root of its
resilience and demonstrate that it comes from the properties that we
introduced. Our attacks reduce the Recall (True Positive Rate) of the attacked
schemes making them not able to correctly detect anomalies. Thus, the
vulnerabilities we discovered in the anomaly detectors show that (despite an
original good detection performance), those detectors are not able to reliably
learn physical properties of the system. Even attacks that prior work was
expected to be resilient against (based on verified properties) were found to
be successful. We argue that our findings demonstrate the need for both more
complete attacks in datasets, and more critical analysis of process-based
anomaly detectors. We plan to release our implementation as open-source,
together with an extension of two public datasets with a set of Synthetic
Sensor Spoofing attacks as generated by our framework
Traffic Management Applications for Stateful SDN Data Plane
The successful OpenFlow approach to Software Defined Networking (SDN) allows
network programmability through a central controller able to orchestrate a set
of dumb switches. However, the simple match/action abstraction of OpenFlow
switches constrains the evolution of the forwarding rules to be fully managed
by the controller. This can be particularly limiting for a number of
applications that are affected by the delay of the slow control path, like
traffic management applications. Some recent proposals are pushing toward an
evolution of the OpenFlow abstraction to enable the evolution of forwarding
policies directly in the data plane based on state machines and local events.
In this paper, we present two traffic management applications that exploit a
stateful data plane and their prototype implementation based on OpenState, an
OpenFlow evolution that we recently proposed.Comment: 6 pages, 9 figure
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