Culture and generalized inattentional blindness

A recent mathematical treatment of Baars' Global Workspace consciousness model, much in the spirit of Dretske's communication theory analysis of high level mental function, is used to study the effects of embedding cultural heritage on a generalized form of inattentional blindness. Culture should express itself quite distinctly in this basic psychophysical phenomenon, acting across a variety of sensory and other modalities, because the limited syntactic and grammatical 'bandpass' of the topological rate distortion manifold characterizing conscious attention is itself strongly sculpted by the constraints of cultural context

New Methods for Network Traffic Anomaly Detection

In this thesis we examine the efficacy of applying outlier detection techniques to understand the behaviour of anomalies in communication network traffic. We have identified several shortcomings. Our most finding is that known techniques either focus on characterizing the spatial or temporal behaviour of traffic but rarely both. For example DoS attacks are anomalies which violate temporal patterns while port scans violate the spatial equilibrium of network traffic. To address this observed weakness we have designed a new method for outlier detection based spectral decomposition of the Hankel matrix. The Hankel matrix is spatio-temporal correlation matrix and has been used in many other domains including climate data analysis and econometrics. Using our approach we can seamlessly integrate the discovery of both spatial and temporal anomalies. Comparison with other state of the art methods in the networks community confirms that our approach can discover both DoS and port scan attacks. The spectral decomposition of the Hankel matrix is closely tied to the problem of inference in Linear Dynamical Systems (LDS). We introduce a new problem, the Online Selective Anomaly Detection (OSAD) problem, to model the situation where the objective is to report new anomalies in the system and suppress know faults. For example, in the network setting an operator may be interested in triggering an alarm for malicious attacks but not on faults caused by equipment failure. In order to solve OSAD we combine techniques from machine learning and control theory in a unique fashion. Machine Learning ideas are used to learn the parameters of an underlying data generating system. Control theory techniques are used to model the feedback and modify the residual generated by the data generating state model. Experiments on synthetic and real data sets confirm that the OSAD problem captures a general scenario and tightly integrates machine learning and control theory to solve a practical problem

Anomaly-Based Intrusion Detection by Modeling Probability Distributions of Flow Characteristics

In recent years, with the increased use of network communication, the risk of compromising the information has grown immensely. Intrusions have evolved and become more sophisticated. Hence, classical detection systems show poor performance in detecting novel attacks. Although much research has been devoted to improving the performance of intrusion detection systems, few methods can achieve consistently efficient results with the constant changes in network communications. This thesis proposes an intrusion detection system based on modeling distributions of network flow statistics in order to achieve a high detection rate for known and stealthy attacks. The proposed model aggregates the traffic at the IP subnetwork level using a hierarchical heavy hitters algorithm. This aggregated traffic is used to build the distribution of network statistics for the most frequent IPv4 addresses encountered as destination. The obtained probability density functions are learned by the Extreme Learning Machine method which is a single-hidden layer feedforward neural network. In this thesis, different sequential and batch learning strategies are proposed in order to analyze the efficiency of this proposed approach. The performance of the model is evaluated on the ISCX-IDS 2012 dataset consisting of injection attacks, HTTP flooding, DDoS and brute force intrusions. The experimental results of the thesis indicate that the presented method achieves an average detection rate of 91% while having a low misclassification rate of 9%, which is on par with the state-of-the-art approaches using this dataset. In addition, the proposed method can be utilized as a network behavior analysis tool specifically for DDoS mitigation, since it can isolate aggregated IPv4 addresses from the rest of the network traffic, thus supporting filtering out DDoS attacks

Expressive Body Capture: 3D Hands, Face, and Body from a Single Image

To facilitate the analysis of human actions, interactions and emotions, we compute a 3D model of human body pose, hand pose, and facial expression from a single monocular image. To achieve this, we use thousands of 3D scans to train a new, unified, 3D model of the human body, SMPL-X, that extends SMPL with fully articulated hands and an expressive face. Learning to regress the parameters of SMPL-X directly from images is challenging without paired images and 3D ground truth. Consequently, we follow the approach of SMPLify, which estimates 2D features and then optimizes model parameters to fit the features. We improve on SMPLify in several significant ways: (1) we detect 2D features corresponding to the face, hands, and feet and fit the full SMPL-X model to these; (2) we train a new neural network pose prior using a large MoCap dataset; (3) we define a new interpenetration penalty that is both fast and accurate; (4) we automatically detect gender and the appropriate body models (male, female, or neutral); (5) our PyTorch implementation achieves a speedup of more than 8x over Chumpy. We use the new method, SMPLify-X, to fit SMPL-X to both controlled images and images in the wild. We evaluate 3D accuracy on a new curated dataset comprising 100 images with pseudo ground-truth. This is a step towards automatic expressive human capture from monocular RGB data. The models, code, and data are available for research purposes at https://smpl-x.is.tue.mpg.de.Comment: To appear in CVPR 201

Generalized inattentional blindness from a Global Workspace perspective

We apply Baars' Global Workspace model of consciousness to inattentional blindness, using the groupoid network method of Stewart et al. to explore modular structures defined by information measures associated with cognitive process. Internal cross-talk breaks the fundamental groupoid symmetry, and, if sufficiently strong, creates, in a highly punctuated manner, a linked, shifting, giant component which instantiates the global workspace of consciousness. Embedding, exterior, information sources act as an external field which breaks the groupoid symmetry in a somewhat different manner, definng the slowly-acting contexts of Baars' theory and providing topological constraints on the manifestations of consciousness. This analysis significantly extends recent mathematical treatments of the global workspace, and identifies a shifting, topologically-determined syntactical and grammatical 'bottleneck' as a tunable rate distortion manifold which constrains what sensory or other signals can be brought to conscious attention, typically in a punctuated manner. Sensations outside the limits of that filter's syntactic 'bandpass' have lower probability of detection, regardless of their structure, accounting for generalized forms of inattentional blindness

Features extraction using random matrix theory.

Representing the complex data in a concise and accurate way is a special stage in data mining methodology. Redundant and noisy data affects generalization power of any classification algorithm, undermines the results of any clustering algorithm and finally encumbers the monitoring of large dynamic systems. This work provides several efficient approaches to all aforementioned sides of the analysis. We established, that notable difference can be made, if the results from the theory of ensembles of random matrices are employed. Particularly important result of our study is a discovered family of methods based on projecting the data set on different subsets of the correlation spectrum. Generally, we start with traditional correlation matrix of a given data set. We perform singular value decomposition, and establish boundaries between essential and unimportant eigen-components of the spectrum. Then, depending on the nature of the problem at hand we either use former or later part for the projection purpose. Projecting the spectrum of interest is a common technique in linear and non-linear spectral methods such as Principal Component Analysis, Independent Component Analysis and Kernel Principal Component Analysis. Usually the part of the spectrum to project is defined by the amount of variance of overall data or feature space in non-linear case. The applicability of these spectral methods is limited by the assumption that larger variance has important dynamics, i.e. if the data has a high signal-to-noise ratio. If it is true, projection of principal components targets two problems in data mining, reduction in the number of features and selection of more important features. Our methodology does not make an assumption of high signal-to-noise ratio, instead, using the rigorous instruments of Random Matrix Theory (RNIT) it identifies the presence of noise and establishes its boundaries. The knowledge of the structure of the spectrum gives us possibility to make more insightful projections. For instance, in the application to router network traffic, the reconstruction error procedure for anomaly detection is based on the projection of noisy part of the spectrum. Whereas, in bioinformatics application of clustering the different types of leukemia, implicit denoising of the correlation matrix is achieved by decomposing the spectrum to random and non-random parts. For temporal high dimensional data, spectrum and eigenvectors of its correlation matrix is another representation of the data. Thus, eigenvalues, components of the eigenvectors, inverse participation ratio of eigenvector components and other operators of eigen analysis are spectral features of dynamic system. In our work we proposed to extract spectral features using the RMT. We demonstrated that with extracted spectral features we can monitor the changing dynamics of network traffic. Experimenting with the delayed correlation matrices of network traffic and extracting its spectral features, we visualized the delayed processes in the system. We demonstrated in our work that broad range of applications in feature extraction can benefit from the novel RMT based approach to the spectral representation of the data