This communication focuses on the integration of organic nonlinear optical and gain materials into plasmonic and
metamaterial device architectures and most specifically focuses on the integration of organic electro-optic (OEO)
materials into such structures. The central focus is on structures that lead to sub-optical wavelength concentration of
light (mode confinement) and the interaction of photonic and plasmonic modes. Optical loss and bandwidth limitations
are serious issues with such structures and optical loss is evaluated for prototype device architectures associated with the
use of silver and gold nanoparticles and membranes supporting plasmonic resonances. Electro-optic activity in organic
materials requires that chromophores exhibit finite noncentrosymmetric organization. Because of material conductivity
and integration issues, plasmonic and metamaterial device architectures are more challenging than conventional triple
stack all-organic device architectures and electro-optic of a given OEO material may be an order of magnitude less in
such structures. Because of this, we have turned to a variety of materials processing options for such integration
including crystal growth, sequential synthesis/self assembly, and electric field poling of materials deposited from
solution or by vapor deposition. Recent demonstration of integration of silicon photonic modulator and lithium niobate
modulator structures with metallic plasmonic structures represent a severe challenge for organic electro-optic material
plasmonic devices as these devices afford high bandwidth operation and attractive VμL performance. Optical loss
remains a challenge for all structures.
A scheme that uses the hidden Markov model (HMM) is proposed in this work to detect unauthorized nuisance packets in IP networks, which waste network resources and may result in the denial of service (DoS) attack. The proposed HMM is designed to differentiate the attack traffic from the normal traffic systematically. The design of the basic HMM model is first introduced, and the operations of the detector are then described in detail. Finally, we show that the detector using HMM is not sensitive to various attack types and able to detect the attack at an earlier stage.
The third-generation (3G) wireless network is a convergence of
several types of telecommunication networks to support various
wireless data services. Wireless LAN also supports mobility via
mobile IP. As a result, the convergence and mobility have
potential vulnerability in security. In this paper, a
Denial-of-Service (DoS) attack which can waste wireless resource
by sending a large number of nuisance packets to the spoofed
destination address of IP packets is introduced. To effectively
prevent the attack, fast detection, reliability, and efficiency
with small overhead are suggested as requirements in a detection
system. We propose a detector using Hidden Markov Model (HMM) to
achieve these requirements and reduce the influences of the attack
as fast as possible. The generation of the HMM for the detector
are discussed and the operation of the detector are described.
Weighting factors and second order Markov models are employed to
improve the reliability of the detector. The proposed system is
compared with the existing sequential detection approach in terms
of the false alarm rate and optimum detection time interval to
evaluate the performance of the detectors. Our simulation results
using ns-2 simulator shows that the proposed HMM detector is
reliable and fast to detect the attack due to its dynamic
property.
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