134 research outputs found
A spatio-temporal entropy-based approach for the analysis of cyber attacks (demo paper)
Computer networks are ubiquitous systems growing exponentially with a predicted 50 billion devices connected by 2050. This dramatically increases the potential attack surface of Internet networks. A key issue in cyber defense is to detect, categorize and identify these attacks, the way they are propagated and their potential impacts on the systems affected. The research presented in this paper models cyber attacks at large by considering the Internet as a complex system in which attacks are propagated over a network. We model an attack as a path from a source to a target, and where each attack is categorized according to its intention. We setup an experimental testbed with the concept of honeypot that evaluates the spatiotemporal distribution of these Internet attacks. The preliminary results show a series of patterns in space and time that illustrate the potential of the approach, and how cyber attacks can be categorized according to the concept and measure of entropy
Editorial: Paz, la tarea es fortalecer la participación social y popular
Asistimos a un momento histórico en Colombia, se marca posiblemente, el cierre de una cruenta confrontación armada de más de 50 años, entre las Farc-Ep y el gobierno Colombiano, este solo hecho ya de por sí, es bien importante, pero no suficiente
Transition from regular to complex behaviour in a discrete deterministic asymmetric neural network model
We study the long time behaviour of the transient before the collapse on the
periodic attractors of a discrete deterministic asymmetric neural networks
model. The system has a finite number of possible states so it is not possible
to use the term chaos in the usual sense of sensitive dependence on the initial
condition. Nevertheless, at varying the asymmetry parameter, , one observes
a transition from ordered motion (i.e. short transients and short periods on
the attractors) to a ``complex'' temporal behaviour. This transition takes
place for the same value at which one has a change for the mean
transient length from a power law in the size of the system () to an
exponential law in . The ``complex'' behaviour during the transient shows
strong analogies with the chaotic behaviour: decay of temporal correlations,
positive Shannon entropy, non-constant Renyi entropies of different orders.
Moreover the transition is very similar to that one for the intermittent
transition in chaotic systems: scaling law for the Shannon entropy and strong
fluctuations of the ``effective Shannon entropy'' along the transient, for .Comment: 18 pages + 6 figures, TeX dialect: Plain TeX + IOP macros (included
Relaxation, closing probabilities and transition from oscillatory to chaotic attractors in asymmetric neural networks
Attractors in asymmetric neural networks with deterministic parallel dynamics
were shown to present a "chaotic" regime at symmetry eta < 0.5, where the
average length of the cycles increases exponentially with system size, and an
oscillatory regime at high symmetry, where the typical length of the cycles is
2. We show, both with analytic arguments and numerically, that there is a sharp
transition, at a critical symmetry \e_c=0.33, between a phase where the
typical cycles have length 2 and basins of attraction of vanishing weight and a
phase where the typical cycles are exponentially long with system size, and the
weights of their attraction basins are distributed as in a Random Map with
reversal symmetry. The time-scale after which cycles are reached grows
exponentially with system size , and the exponent vanishes in the symmetric
limit, where . The transition can be related to the dynamics
of the infinite system (where cycles are never reached), using the closing
probabilities as a tool.
We also study the relaxation of the function ,
where is the local field experienced by the neuron . In the symmetric
system, it plays the role of a Ljapunov function which drives the system
towards its minima through steepest descent. This interpretation survives, even
if only on the average, also for small asymmetry. This acts like an effective
temperature: the larger is the asymmetry, the faster is the relaxation of ,
and the higher is the asymptotic value reached. reachs very deep minima in
the fixed points of the dynamics, which are reached with vanishing probability,
and attains a larger value on the typical attractors, which are cycles of
length 2.Comment: 24 pages, 9 figures, accepted on Journal of Physics A: Math. Ge
Micro-computed tomography and histology to explore internal morphology in decapod larvae
Traditionally, the internal morphology of crustacean larvae has been studied using destructive
techniques such as dissection and microscopy. The present study combines advances in microcomputed
tomography (micro-CT) and histology to study the internal morphology of decapod larvae,
using the common spider crab (Maja brachydactyla Balss, 1922) as a model and resolving the individual
limitations of these techniques. The synergy of micro-CT and histology allows the organs to be easily
identified, revealing simultaneously the gross morphology (shape, size, and location) and histological
organization (tissue arrangement and cell identification). Micro-CT shows mainly the exoskeleton,
musculature, digestive and nervous systems, and secondarily the circulatory and respiratory systems,
while histology distinguishes several cell types and confirms the organ identity. Micro-CT resolves a
discrepancy in the literature regarding the nervous system of crab larvae. The major changes occur in
the metamorphosis to the megalopa stage, specifically the formation of the gastric mill, the shortening
of the abdominal nerve cord, the curving of the abdomen beneath the cephalothorax, and the
development of functional pereiopods, pleopods, and lamellate gills. The combination of micro-CT and
histology provides better results than either one alone.Financial support was provided by the Spanish Ministry of Economy and Competitiveness through the INIA
project (grant number RTA2011-00004-00-00) to G.G. and a pre-doctoral fellowship to D.C. (FPI-INIA)
Adaptive Honeypot Engagement through Reinforcement Learning of Semi-Markov Decision Processes
A honeynet is a promising active cyber defense mechanism. It reveals the
fundamental Indicators of Compromise (IoCs) by luring attackers to conduct
adversarial behaviors in a controlled and monitored environment. The active
interaction at the honeynet brings a high reward but also introduces high
implementation costs and risks of adversarial honeynet exploitation. In this
work, we apply infinite-horizon Semi-Markov Decision Process (SMDP) to
characterize a stochastic transition and sojourn time of attackers in the
honeynet and quantify the reward-risk trade-off. In particular, we design
adaptive long-term engagement policies shown to be risk-averse, cost-effective,
and time-efficient. Numerical results have demonstrated that our adaptive
engagement policies can quickly attract attackers to the target honeypot and
engage them for a sufficiently long period to obtain worthy threat information.
Meanwhile, the penetration probability is kept at a low level. The results show
that the expected utility is robust against attackers of a large range of
persistence and intelligence. Finally, we apply reinforcement learning to the
SMDP to solve the curse of modeling. Under a prudent choice of the learning
rate and exploration policy, we achieve a quick and robust convergence of the
optimal policy and value.Comment: The presentation can be found at https://youtu.be/GPKT3uJtXqk. arXiv
admin note: text overlap with arXiv:1907.0139
Rapid Internalization of the Oncogenic K+ Channel KV10.1
KV10.1 is a mammalian brain voltage-gated potassium channel whose ectopic expression outside of the brain has been proven relevant for tumor biology. Promotion of cancer cell proliferation by KV10.1 depends largely on ion flow, but some oncogenic properties remain in the absence of ion permeation. Additionally, KV10.1 surface populations are small compared to large intracellular pools. Control of protein turnover within cells is key to both cellular plasticity and homeostasis, and therefore we set out to analyze how endocytic trafficking participates in controlling KV10.1 intracellular distribution and life cycle. To follow plasma membrane KV10.1 selectively, we generated a modified channel of displaying an extracellular affinity tag for surface labeling by α-bungarotoxin. This modification only minimally affected KV10.1 electrophysiological properties. Using a combination of microscopy and biochemistry techniques, we show that KV10.1 is constitutively internalized involving at least two distinct pathways of endocytosis and mainly sorted to lysosomes. This occurs at a relatively fast rate. Simultaneously, recycling seems to contribute to maintain basal KV10.1 surface levels. Brief KV10.1 surface half-life and rapid lysosomal targeting is a relevant factor to be taken into account for potential drug delivery and targeting strategies directed against KV10.1 on tumor cells
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