3,754 research outputs found
Artificial intelligence in the cyber domain: Offense and defense
Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41
Lattice dynamical wavelet neural networks implemented using particle swarm optimisation for spatio-temporal system identification
Starting from the basic concept of coupled map lattices, a new family of adaptive wavelet neural networks, called lattice dynamical wavelet neural networks (LDWNN), is introduced for spatiotemporal system identification, by combining an efficient wavelet representation with a coupled map lattice model. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimisation (PSO) algorithm, is proposed for augmenting the proposed network. A novel two-stage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the orthogonal projection pursuit algorithm, significant wavelet-neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated waveletneurons are optimised using a particle swarm optimiser. The resultant network model, obtained in the first stage, may however be redundant. In the second stage, an orthogonal least squares (OLS) algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet-neurons from the network. The proposed two-stage hybrid training procedure can generally produce a parsimonious network model, where a ranked list of wavelet-neurons, according to the capability of each neuron to represent the total variance in the system output signal is produced. Two spatio-temporal system identification examples are presented to demonstrate the performance of the proposed new modelling framework
Orthogonal learning particle swarm optimization
Particle swarm optimization (PSO) relies on its
learning strategy to guide its search direction. Traditionally,
each particle utilizes its historical best experience and its neighborhood’s
best experience through linear summation. Such a
learning strategy is easy to use, but is inefficient when searching
in complex problem spaces. Hence, designing learning strategies
that can utilize previous search information (experience) more
efficiently has become one of the most salient and active PSO
research topics. In this paper, we proposes an orthogonal learning
(OL) strategy for PSO to discover more useful information that
lies in the above two experiences via orthogonal experimental
design. We name this PSO as orthogonal learning particle swarm
optimization (OLPSO). The OL strategy can guide particles to
fly in better directions by constructing a much promising and
efficient exemplar. The OL strategy can be applied to PSO with
any topological structure. In this paper, it is applied to both global
and local versions of PSO, yielding the OLPSO-G and OLPSOL
algorithms, respectively. This new learning strategy and the
new algorithms are tested on a set of 16 benchmark functions, and
are compared with other PSO algorithms and some state of the
art evolutionary algorithms. The experimental results illustrate
the effectiveness and efficiency of the proposed learning strategy
and algorithms. The comparisons show that OLPSO significantly
improves the performance of PSO, offering faster global convergence,
higher solution quality, and stronger robustness
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