Software-defined networking (SDN) is an emerging network architecture where a programmable network control is decoupled from forwarding. Greater control of a network through programming, abstraction of the complexity of the underlying physical infrastructure, and emergence of new applications are some benefits of SDN, to name a few. Unfortunately, the idea of centralized control raises new security concerns that have become a research topic among both academia and industry. An attacker can exploit the required extensive communication between the control and data plane to launch a network-wide, type of denial-of-service attack, known as the data-to-control plane saturation attack. Such an attack can have devastating effect on a large part of the network. This paper introduces a new method for data-to-control plane saturation attack detection that is based on dynamically estimating and monitoring the rate of the Packet-In messages arriving to the controller. The proposed detection method is based on adaptive threshold that varies based on the rate of the received Packet-In messages. The detection technique by design allows discovering the protocol exploited to launch the attack. We utilize this feature, to present a simple attack mitigation method that is protocol independent and targets attacking traffic that belong to the identified attacking protocol. Moreover, being protocol independent, the proposed method can protect against flooding attacks based on self-defined protocols recently made possible with the emerging SDN technology. Attack mitigation is based on utilizing only the available OpenFlow commands without any change to the OpenFlow protocol. The results of the conducted experiments under different scenarios show that the presented method is capable of effectively protecting against the control plane saturation attack with an average detection time of (\(\approx 0.1\) s) which is comparable to state of the art with similar experimental setup. In addition, the method imposes almost (0%) overhead on legitimate traffic once the attack is mitigated.

Software systems usually evolve constantly, which requires constant development and maintenance. Subsequently, the architecture of these systems tends to degrade with time. Therefore, stability is a key measure for evaluating an architecture. Open-source software systems are becoming progressively vital these days. Since open-source software systems are usually developed in a different management style, the quality of their architectures needs to be studied. ISO/IEC SQuaRe quality standard characterized stability as one of the sub-characteristics of maintainability. Unstable software architecture could cause the software to require high maintenance cost and effort. In this work, the authors propose a simple, yet efficient, technique that is based on carefully aggregating the package level stability in order to measure the change in the architecture level stability as the architecture evolution happens. The proposed method can be used to further study the cause behind the positive or negative architecture stability changes.

This paper presents a new technique to texture image denoising using windowed nonlocal means with dominant neighborhood structure. The proposed method mainly addresses the incurred high blurring when the windowed nonlocal means is applied to texture images corrupted by high noise levels. The dominant neighborhood structure has been recently developed and successfully applied to texture classification (Khellah in IEEE Trans Image Process 20(11), 2011). Dominant neighborhood structure is an estimated global map representing the measured intensity similarity between any given image pixel and its surrounding neighbors within a certain search window. The map is used in this work to exclude all insignificant patches within the search window when computing the nonlocal means weights. The method is found to be quantitatively and qualitatively effective in denoising texture images when corrupted by high noise levels. In addition, the method highly reduces the computational requirements of the windowed nonlocal means filter.

This paper presents a new technique to image denoising that mainly addresses the incurred high blurring when the windowed nonlocal means is applied to images corrupted by high noise levels. The proposed method is based on an enhanced weighting function that computes patches similarity based on both their intensities and structural features. The structural features are encoded using Local Binary Pattern (LBP) a well known texture descriptors. A new LBP based weighting function is proposed that has properties complementing the intensity based weighting function. The LBP based weighting function is used to modulate the intensity based weighting function. The modulated weights are noise independent and reflect the actual patch similarity. The method is found to be quantitatively and qualitatively effective in denoising images when corrupted by high noise levels. It suppresses image noise while preserving significant image characteristics.

This paper proposes a new approach to extract global image features for the purpose of texture classification. The proposed texture features are obtained by generating an estimated global map representing the measured intensity similarity between any given image pixel and its surrounding neighbors within a certain window. The intensity similarity map is an average representation of the texture-image dominant neighborhood similarity. The estimated dominant neighborhood similarity is robust to noise and referred to as image dominant neighborhood structure. The global rotation-invariant features are then extracted from the generated image dominant neighborhood structure. Features obtained from the local binary patterns (LBPs) are then extracted in order to supply additional local texture features to the generated features from the dominant neighborhood structure. Both features complement each other. The experimental results on representative texture databases show that the proposed method is robust to noise and can achieve significant improvement in terms of the obtained classification accuracy in comparison to the LBP method. In addition, the method classification accuracy is comparable to the two recent LBP extensions: dominant LBP and completed LBP.

Dynamicestimation,theassimilationof dataover time, is an importantscientificissue in remotesensing,imageprocessing, andcomputervision, to nameafew. Themainmotivationfor this thesisis large-scale2-D dynamicestimationproblems relatedto remotesensing.For suchproblems,numberof variablesto beestimatedcan reachto theorderof millions. As a result,directapplicationof conventionalestimation algorithm,i.e.,theKalmanfilter, becomestotally impracticalfrom two technicalaspects: computational andstoragedemands.In this thesis,we proposea new methodfor largescale2-D estimationproblemsthatemulatesthe Kalmanfilter, but with moreefficient computational andstoragedemands. Usingparameterizederrormodelsto modelthehugeerrorcovariancematricesis the maincontributionof this thesis.Underthis scope,we developa new approximateerror predictionstepandanew approximatelarge-scaleupdatestep. Westudiedtheperformanceof theproposedmethodin thecontext of smallsynthetic 2-D diffusionprocesses. In addition,weappliedourmethodto alarge-scaleremotesensing problem: the estimationof the oceansurfacetemperaturebasedon sparsesatellite measurements.

The dynamic estimation of large-scale stochastic image sequences, as frequently encountered in remote sensing, is important in a variety of scientific applications. However, the size of such images makes conventional dynamic estimation methods, for example, the Kalman and related filters, impractical. In this paper, we present an approach that emulates the Kalman filter, but with considerably reduced computational and storage requirements. Our approach is illustrated in the context of a 512 x 512 image sequence of ocean surface temperature. The static estimation step, the primary contribution here, uses a mixture of stationary models to accurately mimic the effect of a nonstationary prior, simplifying both computational complexity and modeling. Our approach provides an efficient, stable, positive-definite model which is consistent with the given correlation structure. Thus, the methods of this paper may find application in modeling and single-frame estimation.

This manuscript presents studies examining the use of C-band polarimetric Synthetic Aperture Radar (SAR) systems for the detection of crashed aircraft. The ultimate aim is to assist Search and Rescue in Canada in the location of such targets. Detection methodologies based on the Polarimetric Whitening Filter, Cameron Decomposition, and measures of even bounce contributions to the backscatter have been examined. Tests were performed using imagery of serviceable and crashed aircraft and crashed aircraft parts. Although individual methods make it possible to detect the crashed aircraft, best results for target detection with decreased numbers of false alarms occur when these methods are used in combination.

Dynamic estimation of large-scale remote-sensing image sequences is important in a variety of scientific applications. However, the growing size of such sensed images makes conventional dynamic estimation methods, for example the Kalman and related filters, impractical. In this paper we present an approach that emulates the Kalman filter, but with considerably reduced computational and storage requirements. Our approach is illustrated in the context of a large (512/spl times/512) image sequence of ocean surface temperature.

Researchers at the Canada Centre for Remote Sensing of Natural Resources Canada are exploring the use of remotely sensed imagery to assist Search and Rescue in Canada. Studies have been examining the use of Synthetic Aperture Radar for the detection of crashed aircraft. Promising results have been obtained with techniques for detection of dihedrals in interferometric and polarimetric data. With further development in technologies and techniques, and improved coverage of the Canadian landmass by future spaceborne systems such as RADARSAT-2, it is expected that it will be possible to assist in Search and Rescue for land targets.

Sea surface temperature (SST) can be estimated from remotely-sensed images. Because of the sparsity of the available observation it is ideal to do estimation using dynamic methods (such as Kalman filtering). To model dynamics of SST accurately we need to know the motion of sea current. The traditional video motion estimation problem is straightforward, in some ways, because there are so few constraints. That is, the motion vectors are pretty much arbitrary, and successive image frames are densely pixellated, have the same number of pixels, with similar noise statistics. However there are many motion estimation problems, particularly in the area of remote sensing, which do not share these properties. In this paper we investigate the problem of determining the motion field of the sea surface, based on infrared measurements of surface temperature. This problem is challenging in that only a subset of the whole domain is measured at each point in time; specifically, only a few stripes are imaged. In addition, because of clouds, the measured subset varies from time to time. The quality (level of noise) can also vary from pixel to pixel. Our research is based on the following assumptions and observations: the motion field should be smooth and ideally divergence-free, i.e. the motion field is close to time-stationary. Based on these assumptions we choose to use optical flow method for this motion problem. We handle difficulty of data sparcity by pre-estimation to get a dense field. Pre-estimation can be refined by integrating this motion estimation result

The problem of data fusion, the merging of data taken by different sensors, becomes ever more relevant with the launch of each new remote-sensing platform. One example of considerable current interest in the climate change community is the production of an improved sea surface temperature (SST) map. In particular, two state-of-the-art instruments - the Along Track Scanning Radiometer (ATSR) and AVHRR - share complementary features: ATSR allows excellent cloud discrimination and atmospheric correction based on its dual-view scanning geometry, but observes only narrow swaths of ocean; AVHRR suffers from low-wavenumber atmospheric distortions and cloud contamination, but has extensive global coverage. The authors propose a methodology for combining ocean skin temperatures from the ATSR and AVHRR instruments to produce a continuous analysis at one-sixth degree spatial and three-day temporal resolutions, together with reliable error estimates, from the fusion of multiple datasets with arbitrary sampling characteristics, resulting in estimated temperatures which unite the precision of ATSR with the superior coverage afforded by AVHRR

This paper addresses the dynamic estimation of the ocean surface temperature based on data from the Along-Track Scanning Radiometer (ATSR) for large (512/spl times/512) fields. For such huge problems, the conventional dynamic estimation tool (the Kalman filter) is not directly applicable, instead, we develop a recursive estimation algorithm that emulates the Kalman filter. Our approach uses a multiscale estimation algorithm for the update step, and makes simplifying assumptions about the surface dynamics leading to a computationally efficient prediction step.

P.W. FIEGUTH F.M. KHELLAH M.J. MURRAY M.R. ALLEN University of Waterloo Rutherford Appleton Laboratory Waterloo, Ont., N2L 3G1, Canada Chilton, Didcot, OX11 OQX, UK p eguth@uwaterloo.ca Tel: (519) 888-4567 x3599 Fax: (519) 746-4791 ABSTRACT This paper addresses the dynamic estimation of the ocean surface temperature based on data from the AlongTrack Scanning Radiometer (ATSR) for large (512 512) elds. For such huge problems, the conventional dynamic estimation tool (the Kalman lter) is not directly applicable, instead, we develop a recursive estimation algorithm that emulates the Kalman lter. Our approach uses a recently-developed multiscale estimation algorithm for the update step, and makes simplifying assumptions about the surface dynamics leading to a computationally e cient prediction step. INTRODUCTION Dynamic models, long the focus of global circulation and related climate models, have been slow to be adopted for very large statistical estimation problems. This is primarily due to the O(n3) computational complexity of the Kalman lter for n pixels, which is totally impractical for mega-pixel remotely-sensed elds. For problems which are very densely measured, it may be adequate to employ a static optimal interpolation procedure, incorporating purely spatial statistics, which assumes that the underlying eld is constant over time. However a dynamic approach has much to o er: the eld is not assumed to be constant, a prior-mean eld does not need to be de ned, and meaningful estimates can be computed in the presence of data gaps. In this paper, we discuss a method of dynamic estimation for sea surface temperature SST data from the alongtrack scanning radiometer (ATSR); for which a sample 3-day set of measurements is shown in Fig.1. The ATSR data is well-suited to a dynamic approach: the SST is time-varying but also highly-correlated over time, the SST does not really possess a well-de ned mean eld, and there are frequent (and sometimes widespread) data gaps due to clouds. Longitude (East) La tit ud e (N or th )

Considerable progress in the recognition techniques for Latin and Chinese characters has been achieved. By contrast, Arabic optical character recognition has started recently, with relatively slow advances. In this paper, an Arabic character recognition system for hexagonally sampled data is presented. Hexagonal sampling of images is found to be the optimal sampling scheme in terms of storage requirements, computational cost, connectivity, adjacency, and angular resolution. The presented system is mainly composed of three stages: viz. the preprocessing stage; the feature extractor; and the classification stage. The preprocessing stage includes data acquisition, hexagonal sampling, and noise elimination via a statistical smoothing algorithm. The Arabic character is then described by a set of features obtained from Fourier descriptors, boundary line encoding features, in addition to the presence of holes and upper or lower dots. Finally, in the classification stage, recognition is achieved by comparing the features of the unknown character to the prestored prototypes generated in a training phase. Experimental results indicate that the system is efficient in terms of correct recognition rates, speed, and space requirements.