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Illicit cryptocurrency mining has become one of the prevalent methods for monetization of computer security incidents. In this attack, victims’ computing resources are abused to mine cryptocurrency for the benefit of attackers. The most popular illicitly mined digital coin is Monero as it provides strong anonymity and is efficiently mined on CPUs.Illicit mining crucially relies on communication between compromised systems and remote mining pools using the de facto standard protocol Stratum. While prior research primarily focused on endpoint-based detection of in-browser mining, in this paper, we address network-based detection of cryptomining malware in general. We propose XMR-Ray, a machine learning detector using novel features based on reconstructing the Stratum protocol from raw NetFlow records. Our detector is trained offline using only mining traffic and does not require privacy-sensitive normal network traffic, which facilitates its adoption and integration.In our experiments, XMR-Ray attained 98.94% detection rate at 0.05% false alarm rate, outperforming the closest competitor. Our evaluation furthermore demonstrates that it reliably detects previously unseen mining pools, is robust against common obfuscation techniques such as encryption and proxies, and is applicable to mining in the browser or by compiled binaries. Finally, by deploying our detector in a large university network, we show its effectiveness in protecting real-world systems.

Given the ongoing "arms race" in cybersecurity, the shortage of skilled professionals in this field is one of the strongest in computer science. The currently unmet staffing demand in cybersecurity is estimated at over 3 million jobs worldwide. Furthermore, the qualifications of the existing workforce are largely believed to be insufficient. We attempt to gain deeper insights into the nature of the current skill gap in cybersecurity. To this end, we correlate data from job ads and academic curricula using two kinds of skill characterizations: manual definitions from established skill frameworks as well as "skill topics" automatically derived by text mining tools. Our analysis shows a strong agreement between these two analysis techniques and reveals a substantial undersupply in several crucial skill categories, e.g., software and application security, security management, requirements engineering, compliance, and certification. Based on the results of our analysis, we provide recommendations for future curricula development in cybersecurity so as to decrease the identified skill gaps.

Fifth Generation (5G) networks must support billions of heterogeneous devices while guaranteeing optimal Quality of Service (QoS). Such requirements are impossible to meet with human effort alone, and Machine Learning (ML) represents a core asset in 5G. ML, however, is known to be vulnerable to adversarial examples; moreover, as our paper will show, the 5G context is exposed to a yet another type of adversarial ML attacks that cannot be formalized with existing threat models. Proactive assessment of such risks is also challenging due to the lack of ML-powered 5G equipment available for adversarial ML research. To tackle these problems, we propose a novel adversarial ML threat model that is particularly suited to 5G scenarios, and is agnostic to the precise function solved by ML. In contrast to existing ML threat models, our attacks do not require any compromise of the target 5G system while still being viable due to the QoS guarantees and the open nature of 5G networks. Furthermore, we propose an original framework for realistic ML security assessments based on public data. We proactively evaluate our threat model on 6 applications of ML envisioned in 5G. Our attacks affect both the training and the inference stages, can degrade the performance of state-of-the-art ML systems, and have a lower entry barrier than previous attacks.

Machine learning (ML) has become an important paradigm for cyberthreat detection (CTD) in the recent years. A substantial research effort has been invested in the development of specialized algorithms for CTD tasks. From the operational perspective, however, the progress of ML-based CTD is hindered by the difficulty in obtaining the large sets of labelled data to train ML detectors. A potential solution to this problem are semisupervised learning (SsL) methods, which combine small labelled datasets with large amounts of unlabelled data. This paper is aimed at systematization of existing work on SsL for CTD and, in particular, on understanding the utility of unlabelled data in such systems. To this end, we analyze the cost of labelling in various CTD tasks and develop a formal cost model for SsL in this context. Building on this foundation, we formalize a set of requirements for evaluation of SsL methods, which elucidates the contribution of unlabelled data. We review the state-of-the-art and observe that no previous work meets such requirements. To address this problem, we propose a framework for assessing the benefits of unlabelled data in SsL. We showcase an application of this framework by performing the first benchmark evaluation that highlights the tradeoffs of 9 existing SsL methods on 9 public datasets. Our findings verify that, in some cases, unlabelled data provides a small, but statistically significant, performance gain. This paper highlights that SsL in CTD has a lot of room for improvement, which should stimulate future research in this field.

In security-sensitive applications, the success of machine learning depends on a thorough vetting of their resistance to adversarial data. In one pertinent, well-motivated attack scenario, an adversary may attempt to evade a deployed system at test time by carefully manipulating attack samples. In this work, we present a simple but effective gradient-based approach that can be exploited to systematically assess the security of several, widely-used classification algorithms against evasion attacks. Following a recently proposed framework for security evaluation, we simulate attack scenarios that exhibit different risk levels for the classifier by increasing the attacker's knowledge of the system and her ability to manipulate attack samples. This gives the classifier designer a better picture of the classifier performance under evasion attacks, and allows him to perform a more informed model selection (or parameter setting). We evaluate our approach on the relevant security task of malware detection in PDF files, and show that such systems can be easily evaded. We also sketch some countermeasures suggested by our analysis., Comment: In this paper, in 2013, we were the first to introduce the notion of evasion attacks (adversarial examples) created with high confidence (instead of minimum-distance misclassifications), and the notion of surrogate learners (substitute models). These two concepts are now widely re-used in developing attacks against deep networks (even if not always referring to the ideas reported in this work). arXiv admin note: text overlap with arXiv:1401.7727

Anomaly-based methods of intrusion detection are gaining increasing interest among IT-security practitioners. Unlike the traditional intrusion detection systems (IDS) based on pattern matching they are capable of detecting previously unknown attacks without any knowledge about attack signatures. For practical deployment, not only accuracy but also the computational performance is crucial. A deployed IDS must be able to process traffic volumes of several Gbps, which is typical for network infrastructure nodes. Most of the previously proposed anomaly-based IDS have not specifically addressed performance issues. Moreover, it has been widely believed that no anomaly-based system with full analysis of packet payload can reach a “sound barrier” of 1 Gbps. In this contribution, we show that using a careful selection of algorithms and common parallelization techniques, the performance of well over 1 Gbps is possible for a wide range of methods based on a metric embedding of packet/connection payloads. After a brief introduction to the embedding techniques, we describe the specific algorithms and data structures for a high-performance implementation of such methods. We present experiments on large-scale traces of real network traffic that demonstrate that processing rates of over 4 Gbps can be attained by our methods on commodity multi-core processors.

Whenever machine learning is used to prevent illegal or unsanctioned activity and there is an economic incentive, adversaries will attempt to circumvent the protection provided. Constraints on how adversaries can manipulate training and test data for classifiers used to detect suspicious behavior make problems in this area tractable and interesting. This special issue highlights papers that span many disciplines including email spam detection, computer intrusion detection, and detection of web pages deliberately designed to manipulate the priorities of pages returned by modern search engines. The four papers in this special issue provide a standard taxonomy of the types of attacks that can be expected in an adversarial framework, demonstrate how to design classifiers that are robust to deleted or corrupted features, demonstrate the ability of modern polymorphic engines to rewrite malware so it evades detection by current intrusion detection and antivirus systems, and provide approaches to detect web pages designed to manipulate web page scores returned by search engines. We hope that these papers and this special issue encourages the multidisciplinary cooperation required to address many interesting problems in this relatively new area including predicting the future of the arms races created by adversarial learning, developing effective long-term defensive strategies, and creating algorithms that can process the massive amounts of training and test data available for internet-scale problems.

Fixed mobile convergence (FMC) based on the 3GPP IP Multimedia Subsystem (IMS) is considered one of the most important communication technologies of this decade. Yet this all-IP-based network technology brings about the growing danger of security vulnerabilities in communication and data services. Protecting IMS infrastructure servers against malicious exploits poses a major challenge due to the huge number of systems that may be affected. We approach this problem by proposing an architecture for an autonomous and self-sufficient monitoring and protection system for devices and infrastructure inspired by network intrusion detection techniques. The crucial feature of our system is a signature-less detection of abnormal events and zero-day attacks. These attacks may be hidden in a single message or spread across a sequence of messages. Anomalies identified at any of the network domain's ingresses can be further analyzed for discriminative patterns that can be immediately distributed to all edge nodes in the network domain.

The article presents a general view of a class of decomposition algorithms for training Support Vector Machines (SVM) which are motivated by the method of feasible directions. The first such algorithm for the pattern recognition SVM has been proposed in Joachims, T. (1999, Scholkopf et al. (Eds.) Advances in kernel methods-Support vector learning (pp. 185–208). MIT Press). Its extension to the regression SVM—the maximal inconsistency algorithm—has been recently presented by the author (Laskov, 2000, Solla, Leen, & Muller (Eds.) Advances in neural information processing systems 12 (pp. 484–490). MIT Press). A detailed account of both algorithms is carried out, complemented by theoretical investigation of the relationship between the two algorithms. It is proved that the two algorithms are equivalent for the pattern recognition SVM, and the feasible direction interpretation of the maximal inconsistency algorithm is given for the regression SVM. The experimental results demonstrate an order of magnitude decrease of training time in comparison with training without decomposition, and, most importantly, provide experimental evidence of the linear convergence rate of the feasible direction decomposition algorithms.

Learning-based classifiers are increasingly used for detection of various forms of malicious data. However, if they are deployed online, an attacker may attempt to evade them by manipulating the data. Examples of such attacks have been previously studied under the assumption that an attacker has full knowledge about the deployed classifier. In practice, such assumptions rarely hold, especially for systems deployed online. A significant amount of information about a deployed classifier system can be obtained from various sources. In this paper, we experimentally investigate the effectiveness of classifier evasion using a real, deployed system, PDFrate, as a test case. We develop a taxonomy for practical evasion strategies and adapt known evasion algorithms to implement specific scenarios in our taxonomy. Our experimental results reveal a substantial drop of PDFrate's classification scores and detection accuracy after it is exposed even to simple attacks. We further study potential defense mechanisms against classifier evasion. Our experiments reveal that the original technique proposed for PDFrate is only effective if the executed attack exactly matches the anticipated one. In the discussion of the findings of our study, we analyze some potential techniques for increasing robustness of learning-based systems against adversarial manipulation of data.

In this chapter we introduce basic concepts and ideas of the Support Vector Machines (SVM). In the first section we formulate the learning problem in a statistical framework. A special focus is put on the concept of consistency, which leads to the principle of structural risk minimization (SRM). Application of these ideas to classification problems brings us to the basic, linear formulation of the SVM, described in Sect. 30.3. We then introduce the so called “kernel trick” as a tool for building a non-linear SVM as well as applying an SVM to non-vectorial data (Sect. 30.4). The practical issues of implementation of the SVM training algorithms and the related optimization problems are the topic of Sect. 30.5. Extensions of the SVM algorithms for the problems of non-linear regression and novelty detection are presented in Sect. 30.6. A brief description of the most successful applications of the SVM is given in Sect. 30.7. Finally, in the last Sect. 30.8 we summarize the main ideas of the chapter.

Despite the recent security improvements in Adobe's PDF viewer, its underlying code base remains vulnerable to novel exploits. A steady flow of rapidly evolving PDF malware observed in the wild substantiates the need for novel protection instruments beyond the classical signature-based scanners. In this contribution we present a technique for detection of JavaScript-bearing malicious PDF documents based on static analysis of extracted JavaScript code. Compared to previous work, mostly based on dynamic analysis, our method incurs an order of magnitude lower run-time overhead and does not require special instrumentation. Due to its efficiency we were able to evaluate it on an extremely large real-life dataset obtained from the VirusTotal malware upload portal. Our method has proved to be effective against both known and unknown malware and suitable for large-scale batch processing.

Learning for security applications is an emerging field where adaptive approaches are needed but are complicated by changing adversarial behavior. Traditional approaches to learning assume benign errors in data and thus may be vulnerable to adversarial errors. In this paper, we incorporate the notion of adversarial corruption directly into the learning framework and derive a new criteria for classifier robustness to adversarial contamination.

Hosts infected with malicious software, so called malware, are ubiquitous in today's computer networks. The means whereby malware can infiltrate a network are manifold and range from exploiting of software vulnerabilities to tricking a user into executing malicious code. Monitoring and detection of all possible infection vectors is intractable in practice. Hence, we approach the problem of detecting malicious software at a later point when it initiates contact with its maintainer; a process referred to as "phoning home". In particular, we introduce Botzilla, a method for detection of malware communication, which proceeds by repetitively recording network traffic of malware in a controlled environment and generating network signatures from invariant content patterns. Experiments conducted at a large university network demonstrate the ability of Botzilla to accurately identify malware communication in network traffic with very low false-positive rates.

The growing amount of web-based attacks poses a severe threat to the security of web applications. Signature-based detection techniques increasingly fail to cope with the variety and complexity of novel attack instances. As a remedy, we introduce a protocol-aware reverse HTTP proxy TokDoc (the token doctor), which intercepts requests and decides on a per-token basis whether a token requires automatic "healing". In particular, we propose an intelligent mangling technique, which, based on the decision of previously trained anomaly detectors, replaces suspicious parts in requests by benign data the system has seen in the past. Evaluation of our system in terms of accuracy is performed on two real-world data sets and a large variety of recent attacks. In comparison to state-of-the-art anomaly detectors, TokDoc is not only capable of detecting most attacks, but also significantly outperforms the other methods in terms of false positives. Runtime measurements show that our implementation can be deployed as an inline intrusion prevention system.

With an increasing demand of inter-connectivity and protocol standardization modern cyber-critical infrastructures are exposed to a multitude of serious threats that may give rise to severe damage for life and assets without the implementation of proper safeguards. Thus, we propose a method that is capable to reliably detect unknown, exploit-based attacks on cyber-critical infrastructures carried out over the network. We illustrate the effectiveness of the proposed method by conducting experiments on network traffic that can be found in modern industrial control systems. Moreover, we provide results of a throughput measuring which demonstrate the real-time capabilities of our system.

Currently there are several interesting alternatives for low-cost high-performance computing. We report here our experiences with an N-gram extraction and sorting problem, originated in the design of a real-time network intrusion detection system. We have considered FPGAs, multi-core CPUs in symmetric multi-CPU machines and GPUs and have created implementations for each of these platforms. After carefully comparing the advantages and disadvantages of each we have decided to go forward with the implementation written for multicore CPUs. Arguments for and against each platform are presented - corresponding to our hands-on experience - that we intend to be useful in helping with the selection of the hardware acceleration solutions for new projects.

We propose a framework for quantitative security analysis of machine learning methods. The key parts of this framework are the formal specification of a deployed learning model and attacker's constraints, the computation of an optimal attack, and the derivation of an upper bound on adversarial impact. We exemplarily apply the framework for the analysis of one specific learning scenario, online centroid anomaly detection, and experimentally verify the tightness of obtained theoretical bounds.

The threat posed by modern network attacks requires novel means for detection of intrusions, as regular signature-based systems fail to cope with the amount and diversity of attacks. Recently, several methods for detection of anomalies in network payloads have been proposed to counteract this threat and identify novel attacks during their initial propagation. However, intrusion detection systems must not only flag malicious events but also provide information needed for assessment of security incidents. Previous work on payload-based anomaly detection has largely ignored this need for explainable decisions. In this paper, we present instruments for visualization and explanation of anomaly detection which can guide the decisions of a security operator. In particular, we propose two techniques: feature differences, for identifying relevant string features of detected anomalies, and feature shading, for highlighting of anomalous contents in network payloads. Both techniques are empirically evaluated using real attacks and network traces, whereby their ability to emphasize typical patterns of attacks is demonstrated.

New overlay-based services aim to provide properties like resilience, availability or QoS. To achieve this, automatic organization and optimization is required, which again demands accurate information on the network. Collecting and exchanging this data has a significant impact on the network, especially if several overlays are used on the same host. In this paper, we provide a survey of the current state of the art and identify challenges which must be addressed in order for new overlay-based services to be successful. We present our own solution CLIO, a cross-layer information service for overlays. CLIO provides information for the automatic creation and optimization of overlays. The service supports multiple overlays on the same node, the provided information is overlay-independent, and collected information is reused.

In this paper we propose an intrusion prevention system (IPS) which operates inline and is capable to detect unknown attacks using anomaly detection methods. Incorporated in the framework of a packet filter each incoming packet is analyzed and -- according to an internal connection state and a computed anomaly score -- either delivered to the production system, redirected to a special hardened system or logged to a network sink for later analysis. Runtime measurements of an actual implementation prove that the performance overhead of the system is sufficient for inline processing. Accuracy measurements on real network data yield improvements especially in the number of false positives, which are reduced by a factor of five compared to a plain anomaly detector.

Malicious software in form of Internet worms, computer viruses, and Trojan horses poses a major threat to the security of networked systems. The diversity and amount of its variants severely undermine the effectiveness of classical signature-based detection. Yet variants of malware families share typical behavioral patternsreflecting its origin and purpose. We aim to exploit these shared patterns for classification of malware and propose a method for learning and discrimination of malware behavior. Our method proceeds in three stages: (a) behavior of collected malware is monitored in a sandbox environment, (b) based on a corpus of malware labeled by an anti-virus scanner a malware behavior classifieris trained using learning techniques and (c) discriminative features of the behavior models are ranked for explanation of classification decisions. Experiments with different heterogeneous test data collected over several months using honeypots demonstrate the effectiveness of our method, especially in detecting novelinstances of malware families previously not recognized by commercial anti-virus software.

A frequent problem in anomaly detection is to decide among different feature sets to be used. For example, various features are known in network intrusion detection based on packet headers, content byte streams or application level protocol parsing. A method for automatic feature selection in anomaly detection is proposed which determines optimal mixture coeffcients for various sets of features. The method generalizes the support vector data description (SVDD) and can be expressed as a semi-innite linear program that can be solved with standard techniques. The case of a single feature set can be handled as a particular case of the proposed method. The experimental evaluation of the new method on unsanitized HTTP data demonstrates that detectors using automatically selected features attain competitive performance, while sparing practitioners from a priori decisions on feature sets to be used. A frequent problem in anomaly detection is to decide among different feature sets to be used. For example, various features are known in network intrusion detection based on packet headers, content byte streams or application level protocol parsing. A method for automatic feature selection in anomaly detection is proposed which determines optimal mixture coeffcients for various sets of features. The method generalizes the support vector data description (SVDD) and can be expressed as a semi-innite linear program that can be solved with standard techniques. The case of a single feature set can be handled as a particular case of the proposed method. The experimental evaluation of the new method on unsanitized HTTP data demonstrates that detectors using automatically selected features attain competitive performance, while sparing practitioners from a priori decisions on feature sets to be used.

Current Voice-over-IP infrastructures lack defenses against unexpected network threats, such as zero-day exploits and computer worms. The possibility of such threats originates from the ongoing convergence of telecommunication and IP network infrastructures. As a countermeasure, we propose a self-learning system for detection of unknown and novel attacks in the Session Initiation Protocol (SIP). The system identifies anomalous content by embedding SIP messages to a feature space and determining deviation from a model of normality. The system adapts to network changes by automatically retraining itself while being hardened against targeted manipulations. Experiments conducted with realistic SIP traffic demonstrate the high detection performance of the proposed system at low false-positive rates.

We propose a new stopping condition for a Support Vector Machine (SVM) solver which precisely reflects the objective of the Leave-One-Out error computation. The stopping condition guarantees that the output on an intermediate SVM solution is identical to the output of the optimal SVM solution with one data point excluded from the training set. A simple augmentation of a general SVM training algorithm allows one to use a stopping criterion equivalent to the proposed sufficient condition. A comprehensive experimental evaluation of our method shows consistent speedup of the exact LOO computation by our method, up to the factor of 13 for the linear kernel. The new algorithm can be seen as an example of constructive guidance of an optimization algorithm towards achieving the best attainable expected risk at optimal computational cost.

The syntax of application layer protocols carries valuable information for network intrusion detection. Hence, the majority of modern IDS perform some form of protocol analysis to refine their signatures with application layer context. Protocol analysis, however, has been mainly used for misuse detection, which limits its application for the detection of unknown and novel attacks. In this contribution we address the issue of incorporating application layer context into anomaly-based intrusion detection. We extend a payload-based anomaly detection method by incorporating structural information obtained from a protocol analyzer. The basis for our extension is computation of similarity between attributed tokens derived from a protocol grammar. The enhanced anomaly detection method is evaluated in experiments on detection of web attacks, yielding an improvement of detection accuracy of 49%. While byte-level anomaly detection is sufficient for detection of buffer overflow attacks, identification of recent attacks such as SQL and PHP code injection strongly depends on the availability of application layer context.

In this paper, we propose a method for network intrusion detection based on language models. Our method proceeds by extracting language features such as n-grams and words from connection payloads and applying unsupervised anomaly detection—without prior learning phase or presence of labeled data. The essential part of this procedure is linear-time computation of similarity measures between language models of connection payloads. Particular patterns in these models decisive for differentiation of attacks and normal data can be traced back to attack semantics and utilized for automatic generation of attack signatures. Results of experiments conducted on two datasets of network traffic demonstrate the importance of high-order n-grams and variable-length language models for detection of unknown network attacks. An implementation of our system achieved detection accuracy of over 80% with no false positives on instances of recent remote-to-local attacks in HTTP, FTP and SMTP traffic.

We propose a method for network intrusion detection based on language models such as n-grams and words. Our method proceeds by extracting these models from TCP connection payloads and applying unsupervised anomaly detection. The essential part of our approach is linear-time computation of similarity measures between language models stored in trie data structures Results of our experiments conducted on two datasets of network traffic demonstrate the importance of higher-order n-grams for detection of unknown network attacks. Our method is also suitable for language models based on words, which are more amenable in practical security applications. An implementation of our system achieved detection accuracy of over 80% with no false positives on instances of recent attacks in HTTP, FTP and SMTP traffic

Kernel functions as similarity measures for sequential data have been extensively studied in previous research. This contribution addresses the efficient computation of distance functions and similarity coefficients for sequential data. Two proposed algorithms utilize different data structures for efficient computation and yield a runtime linear in the sequence length. Experiments on network data for intrusion detection suggest the importance of distances and even non-metric similarity measures for sequential data.

Application and development of specialized machine learning techniques is gaining increasing attention in the intrusion detection community. A variety of learning techniques proposed for different intrusion detection problems can be roughly classified into two broad categories: supervised (classification) and unsupervised (anomaly detection and clustering). In this contribution we develop an experimental framework for comparative analysis of both kinds of learning techniques. In our framework we cast unsupervised techniques into a special case of classification, for which training and model selection can be performed by means of ROC analysis. We then investigate both kinds of learning techniques with respect to their detection accuracy and ability to detect unknown attacks.

Fingerspelling recognition and handshape recognition are two examples of real-world, multi-class recognition problems consisting of 26 and 78 classes respectively. While it is theoretically possible to solve any multi-class problem with a single "smart" classifier the complexity of such a classifier is usually prohibitively high. This paper looks at several approaches to solving a numerous multi-class recognition problem and discusses in detail a method involving coded output. Experiments are conducted using biomechanical data from a human hand as input, but work is continuing concerning the extraction of this data from multi-view hand images alone. Code generation is discussed and results are presented for several different coded output cases including the Hamming, Golay, and several hybrid codes. Conclusions show that the recognition accuracy increases proportionally to code length.

Recovery of motion parameters and point correspondences is a fundamental problem in computer vision. Although a great deal of research has been done solving rigid motion, nonrigid motion analysis has only recently been addressed, and is gaining interest due to its wide range of applications. This thesis introduces a novel method for estimating motion parameters and point correspondences between 2D curves and 3D surfaces under small nonrigid deformations. We use curvature to track point correspondences and motion parameters between 2D curves under small deformations. Curvature is invariant to the type of parameterization used, and to rigid transformations. These properties help us to deal with purely nonrigid motion, as curvature is not affected by translation/rotation of a given point. The motion parameter in this analysis is a point function, called the displacement function. In this work, it was defined as a polynomial function of a point on the curve under motion. Simulations are performed for illustrating accuracy of the algorithm's estimation by generating deformations on different curves. Real data experiments are also performed, using MR grid data, breast contours from mammogram images and contours of the left ventricle of heart. Three approaches are presented in this thesis for tracking point correspondences, and estimating motion parameters in 3D nonrigid motion. Unlike the 2D case, the motion parameter used here is a vector point function (not scalar), also called displacement function. It it represents both direction and magnitude of deformation at a given point. The first approach involves the use of changes in the "discriminant" for nonrigid motion analysis. Discriminant is defined as the infinitesimal area of surface at a given point. The second approach involves the use of changes in the unit normal at any given point on a surface under deformation. The third approach uses the challenges in Gaussian curvature along with the changes in unit normal direction. In all these approaches, the displacement function can be any point function representing the deformation involved. In our work, we have chosen a polynomial vector function to approximate the deformation. Simulations are performed for each approach by generating nonrigid motion on an ellipsoidal data set to illustrate performance and accuracy of the derived algorithms. Then, the algorithm is tested on a sequence of facial range images. A total of 16 sets of images were used in this experiment, where eight persons have generated two expressions each. We demonstrate the correct correspondence recovery by tracking points on the face during each facial expression. The correspondences estimation is also evaluated by comparing the algorithm's output against manual tracking of feature points by different users. In addition, nonrigid motion segmentation is successfully performed on these images. The motion parameters generated by the algorithm have also been used to demonstrate the abnormality in cardiac motion from CT images.

The paper presents two useful extensions of the incremental SVM in the context of online learning. An online support vector data description algorithm enables application of the online paradigm to unsupervised learning. Furthermore, online learning can be used in the large-scale classification problems to limit the memory requirements for storage of the kernel matrix. The proposed algorithms are evaluated on the task of online monitoring of EEG data, and on the classification task of learning the USPS dataset with a-priori chosen working set size.

We explore recognition implications of understanding gesture communication, having chosen American sign language as an example of a gesture language. An instrumented glove and specially developed software have been used for data collection and labeling. We address the problem of recognizing dynamic signing, i.e. signing performed at natural speed. Two neural network architectures have been used for recognition of different types of finger-spelled sentences. Experimental results are presented suggesting that two features of signing affect recognition accuracy: signing frequency which to a large extent can be accounted for by training a network on the samples of the respective frequency; and coarticulation effect which a network fails to identify. As a possible solution to coarticulation problem two post-processing algorithms for temporal segmentation are proposed and experimentally evaluated.

This manifesto is the result of the Perspective Workshop Network Attack Detection and Defense held in Schloss Dagstuhl (Germany) from March 2nd–6th, 2008. The participants of the workshop represent researchers from Austria, France, Norway, the Switzerland, the United States, and Germany who work actively in the field of intrusion detection and network monitoring. The workshop attendee’s opinion was that intrusion detection and flow analysis, which have been developed as complementary approaches for the detection of network attacks, should more strongly combine event detection and correlation techniques to better meet future challenges in future reactive security. The workshop participants considered various perspectives to envision future network attack detection and defense. The following topics are seen as important in the future: the development of early warning systems, the introduction of situation awareness, the improvement of measurement technology, taxonomy of attacks, the application of intrusion and fraud detection for web services, and anomaly detection. In order to realize those visions the state of the art, the challenges, and research priorities were identified for each topic by working groups. The outcome of the discussion is summarized in working group papers which are published in the workshop proceedings. The papers were compiled by the editors to this manifesto.

Background Bioinformatics data analysis often deals with additive mixtures of signals for which only class labels are known. Then, the overall goal is to estimate class related signals for data mining purposes. A convenient application is metabolic monitoring of patients using infrared spectroscopy. Within an infrared spectrum each single compound contributes quantitatively to the measurement. Results In this work, we propose a novel factorization technique for additive signal factorization that allows learning from classified samples. We define a composed loss function for this task and analytically derive a closed form equation such that training a model reduces to searching for an optimal threshold vector. Our experiments, carried out on synthetic and clinical data, show a sensitivity of up to 0.958 and specificity of up to 0.841 for a 15-class problem of disease classification. Using class and regression information in parallel, our algorithm outperforms linear SVM for training cases having many classes and few data. Conclusions The presented factorization method provides a simple and generative model and, therefore, represents a first step towards predictive factorization methods.