Markos Avlonitis

A spatial stochastic model for worm propagation: scale effects

Realistic models for worm propagation in the Internet have become one of the major topics in the academic literature concerning network security. In this paper, we propose an evolution equation for worm propagation in a very small number of Internet hosts, hereinafter called a subnet and introduce a generalization of the classical epidemic model by including a second order spatial term which models subnet interactions. The corresponding gradient coefficient is a measure of the characteristic scale of interactions and as a result a novel scale approach for understanding the evolution of worm population in different scales, is considered. Results concerning random scan strategies and local preference scan worms are presented. A comparison of the proposed model with simulation results is also presented. Based on our model, more efficient monitoring strategies could be deployed.

Foreshocks and b Value: Bridging Macroscopic Observations to Source Mechanical Considerations

Spring-block models, such as the Olami-Feder-Christensen (OFC) model, were introduced several years ago to describe earthquake dynamics in the context of self-organized criticality. With the aim to address the dependency of the seismicity style on source’s material properties, we present an analytical enrichment of a 2D OFC model. We conclude with an analytical expression which introduces, through an appropriate constitutive equation, an effective dissipation parameter aeff related analytically not only with the elastic properties of the fault plane, but also with stochastic structural heterogeneities and structural processes of the source through a gradient coefficient. Moreover, within the proposed formulation, the low b values experimentally observed in foreshock sequences can be modeled by a process of material softening in the seismogenic volume. To check our analytical findings, a cellular automaton was built-up whereas simulation results have verified the model’s predictions for the evolution of b in macroscopic records.

Collective Intelligence in Video User’s Activity

In this work, we study collective intelligence behavior of Web users that share and watch video content. We propose that the aggregated users’ video activity exhibits characteristic patterns that may be used in order to infer important video scenes thus leading to collective intelligence concerning the video content. In particular, we have utilised a controlled user experiment with information-rich videos for which users’ interactions (e.g., pause, seek/scrub) have been gathered. Modeling the collective information seeking behavior by means of the corresponding probability distribution function we argue that bell-shaped reference patterns are shown to significantly correlate with the predefined scenes of interest for each video, as annotated by the users. In this way, the observed collective intelligence may be used to provide a video-segment ranking tool that detects the importance of video scene. In practice, the proposed techniques might improve navigation within videos on the web and have also the potential to improve video search results with personalised video thumbnails.

Specific Selection of FFT Amplitudes from Audio Sports and News Broadcasting for Classification Purposes

In this paper we investigate the problem of classification between sports and news broadcasting. We detect and classify files that consist of speech and music or background noise (news broadcasting), and speech and a noisy background (sports broadcasting). More specifically, this study investigates feature extraction and training and classification procedures. We compare the Average Magnitude Difference Function (AMDF) method, which we consider more robust to background noise, with a novel proposed method. This method uses several spectral audio features which may be considered as specific semantic information. We base the extraction of these features on the theory of computational geometry using an Onion Algorithm (OA). We tested the classification procedure as well as the learning ability of the two methods using a Learning Vector Quantizer One (LVQ1) neural network. The results of the experiment showed that the OA method has a faster learning procedure, which we characterise as an accurate feature extraction method for several audio cases.

Treating scalability and modelling human countermeasures against local preference worms via gradient models

A network worm is a specific type of malicious software that self propagates by exploiting application vulnerabilities in network-connected systems. Worm propagation models are mathematical models that attempt to capture the propagation dynamics of scanning worms as a means to understand their behaviour. It turns out that the emerged scalability in worm propagation plays an important role in order to describe the propagation in a realistic way. On the other hand human-based countermeasures also drastically affect the propagation in time and space. This work elaborates on a recent propagation model (Avlonitis et al. in J Comput Virol 3, 87–92, 2007) that makes use of Partial Differential Equations in order to treat correctly scalability and non-uniform behaviour (e.g., local preference worms). The aforementioned gradient model is extended in order to take into account human-based countermeasures that influence the propagation of local-preference worms in the Internet. Certain aspects of scalability emerged in random and local preference strategies are also discussed by means of random field considerations. As a result the size of a critical network that needs to be studied in order to describe the global propagation of a scanning worm is estimated. Finally, we present simulation results that validate the proposed analytical results and demonstrate the higher propagation rate of local preference worms compared with random scanning worms.

Statistical Aspects of Gradient Theory

The paper contains new results on the statistical origin of gradient terms in the macroscopic representation of stress and strain fields supported by engineering materials with appreciable heterogeneity at small scales. Physical arguments are employed to identify the correlation lengths defining the gradient coefficient in the corresponding Taylor series expansion of the random stress and strain fields. This allows to determine the number and type of stress and strain gradient terms which is important to include in the respective constitutive equations. The formulation also allows to incorporate microscopic fields with different statistical properties, including log-normal distributions. Depending on the type of correlation function employed, corresponding expressions for the gradient coefficients may be obtained.

Toward early warning against Internet worms based on critical-sized networks

In this paper, we build on a recent worm propagation stochastic model, in which random effects during worm spreading were modeled by means of a stochastic differential equation. On the basis of this model, we introduce the notion of the critical size of a network, which is the least size of a network that needs to be monitored, in order to correctly project the behavior of a worm in substantially larger networks. We provide a method for the theoretical estimation of the critical size of a network in respect to a worm with specific characteristics. Our motivation is the requirement in real systems to balance the needs for accuracy (i.e., monitoring a network of a sufficient size in order to reduce false alarms) and performance (i.e., monitoring a small-scale network to reduce complexity). In addition, we run simulation experiments in order to experimentally validate our arguments. Finally, based on notion of critical-sized networks, we propose a logical framework for a distributed early warning system against unknown and fast-spreading worms. In the proposed framework, propagation parameters of an early detected worm are estimated in real time by studying a critical-sized network. In this way, security is enhanced as estimations generated by a critical-sized network may help large-scale networks to respond faster to new worm threats. Copyright

Frequency-domain stochastic error concealment for wireless audio applications

Real time digital audio delivery over Wireless Local Area Networks (WLANs) represents an attractive, flexible and cost effective framework for realizing high-quality, multichannel home audio applications. However, the unreliable nature of WLANs IP link frequently imposes significant playback quality degradation, due to delay or permanent loss of a number of transmitted digital audio packets. In this paper, a novel packet error concealment technique is presented, based on the spectral reconstruction of the statistical equivalent of a previously successfully received audio data packet. It is shown that the proposed data reconstruction scheme outperforms previously published error concealment strategies, in both terms of objective and perceptual criteria.

Identifying Asperity Patterns Via Machine Learning Algorithms

An asperity’s location is very crucial in the spatiotemporal analysis of an area’s seismicity. In literature, b-value and seismic density have been proven as useful indicators for asperity location. In this paper, machine learning techniques are used to locate areas with high probability of asperity existence using as feature vector information extracted solely by earthquake catalogs. Many machine learning algorithms are tested to identify those with the best results. This method is tested for data from the wider region of Hokkaido, Japan where in an earlier study asperities have been detected.

Complex networks and simulation strategies: An application to olive fruit fly dispersion

In this work a study for the role of different environmental factors to the evolution of olive fruit fly, via an appropriate network of population traps is given. More explicitly, the olive fruit fly is a parasitic insect that infests olive groves in many countries. Through the use of a network of traps a simulation model was developed and used to simulate the dispersion of olive fruit fly inside a real olive grove for different environmental factors, such as different starting areas of olive fruit fly presence, different temperature sets as well as different drifting distances. Results showed that the level of infestation of the grove was not dependent on the limited areas the olive fruit fly emerged but on the drifting distance a fly could travel per day.