Factor analysis based anomaly detection
We propose a novel anomaly detection algorithm based on factor analysis and Mahalanobis distance. Factor analysis is used to uncover the latent structure (dimensions) of a set of variables. It reduces attribute space from a larger number of variables to a smaller number of factors. The Mahalanobis distance is used to determine the "similarity" of a set of values from an "unknown" sample to a set of values measured from a collection of "known" samples. Combined with factor analysis, Mahalanobis distance is extended to examine whether a given vector is an outlier from a model identified by "factors" based on factor analysis. We present a factor analysis-based network anomaly detection algorithm and apply it to DARPA intrusion detection evaluation data. The experimental results show that the proposed algorithm is able to detect network intrusions with relatively low false alarms.

title: Model replication: transformations to address model scalability
abstract: 2 SUMMARY In Model-Driven Engineering, it is often desirable to evaluate different design alternatives as they relate to scalability issues of the modeled system. A typical approach to address scalability is model replication, which starts by creating base models that capture the key entities as model elements and their relationships as model connections. A collection of base models can be adorned with necessary information to characterize a specific scalability concern as it relates to how the base modeling elements are replicated and connected together. In current modeling practice, such model replication is usually accomplished by scaling the base model manually. This is a time-consuming process that represents a source of error, especially when there are deep interactions between model components. As an alternative to the manual process, this paper presents the idea of automated model replication through a model transformation process that expands the number of elements from the base model and makes the correct connections among the generated modeling elements. The paper motivates the need for model replication through case studies taken from models supporting different domains.

title: Business Process-Oriented Software Architecture for Supporting Business Process Change
abstract: As a business process is essential in running an enterprise, this paper aims toward improving software architecture by extracting business-relevant semantics from business logic and transforming them as important elements that need to be molded and represented in software architecture. This allows for the development of a business process-oriented software architecture (BPOSA) that can support smoother business process change. The paper introduces the structure of BPOSA and its work mode. Then it describes the steps involved in designing a process-oriented software system, and provides analysis and design methods of extending process-relevant attributes for application in business operations.

title: Aspect mining from a modelling perspective
abstract: Aspect mining aims at identifying, analysing, and refactoring crosscutting concerns throughout a legacy system for the purpose of improving software modularisation. This paper presents our investigation into raising the benefits of aspect mining to high level domain-specific models. A key contribution is the capability to identify crosscutting concerns early in development, which will assist in the modularisation of a design through aspects, before proceeding to the implementation level. Furthermore, our experience has led us to believe that aspects are easier to identify at the modelling level, because much of the accidental complexities of implementation concerns are not present in the corresponding modelling abstractions.

title: A Composition-Oriented Approach for Business Process Formal Design
abstract: Traditional approaches to designing business processes are often inadequate for complicated, distributed, and collaborated settings. However since network technologies have become widespread, an increasing number of business processes exhibit these properties. Therefore a new approach is needed. To this end, we describe the motivation, conceptualization, and analysis of a composition-oriented business process design method. The key advance of our method is that it uses process algebra pi-calculus as its formal basis and builds a complex process by composing simple structured process control modules to reduce the complexity of business process design as well as let several designers collaborate the design of a complex process. To make business process more clear, we also provide way of separating exception processing logic from normal process control logic and provide scooping mechanisms to minimize the detrimental effects of unplanned contingencies by ways of composition

title: Automating change evolution in model-driven engineering
abstract: The escalating complexity of software and system models is making it difficult to rapidly explore the effects of a design decision. Automating such exploration with model transformation and aspect-oriented techniques can improve both productivity and model quality. The combination of model transformation and aspect weaving provides a powerful technology for rapidly transforming legacy systems from the high-level properties that models describe. Further, by applying aspect-oriented techniques and program transformation, small changes at the modeling level can trigger very large transformations at the source code level. Thus, model engineers can explore alternative configurations using an aspect weaver targeted for modeling tools and then use the models to generate program transformation rules for adapting legacy source code on a wide scale.

title: Application of multisensor data fusion based on RBF neural networks for fault diagnosis of SAMS
abstract: The idea of multisensor integration is to use multiple sensors for measuring the same variables, where each sensor has its own accuracy, reliability and drawbacks. The sensor information is integrated by some data integration algorithms. In this paper, Radial Basis Function (RBF) neural networks and multisensor data fusion technology are combined and used in the fault detection and diagnosis of sensors hardware faults in the Satellite Attitude Measurement System (SAMS). The fusion method of the RBF neural networks is adopted. By using the combination method the outputs of the system are more accurate and reliable than each individual sensor. Research results show that this method for the detection and diagnosis of the sensors hardware faults in the SAMS is feasible and more effective, and for the sensors which measure the same attitude angle, using the method of firstly integration, then faults diagnosis, finally connection with the measurement system, the systematic measurement precision and performance-price-ratio can be improved.

title: SPOT: A System for Detecting Projected Outliers From High-dimensional Data Streams
abstract: In this paper, we present a new technique, called stream projected ouliter detector (SPOT), to deal with outlier detection problem in high-dimensional data streams. SPOT is unique in a number of aspects. First, SPOT employs a novel window-based time model and decaying cell summaries to capture statistics from the data stream. Second, sparse subspace template (SST), a set of top sparse subspaces obtained by unsupervised and/or supervised learning processes, is constructed in SPOT to detect projected outliers effectively. Multi-Objective genetic algorithm (MOGA) is employed as an effective search method in unsupervised learning for finding outlying subspaces from training data. Finally, SST is able to carry out online self- evolution to cope with dynamics of data streams. This paper provides details on the motivation and technical challenges of detecting outliers from high-dimensional data streams, present an overview of SPOT, and give the plans for system demonstration of SPOT.

title: Anomaly detection in high-dimensional network data streams: A case study
abstract: In this paper, we study the problem of anomaly detection in high-dimensional network streams. We have developed a new technique, called Stream Projected Outlier deTector (SPOT), to deal with the problem of anomaly detection from high-dimensional data streams. We conduct a case study of SPOT in this paper by deploying it on 1999 KDD Intrusion Detection application. Innovative approaches for training data generation, anomaly classification and false positive reduction are proposed in this paper as well. Experimental results demonstrate that SPOT is effective in detecting anomalies from network data streams and outperforms existing anomaly detection methods.

title: Applying PageRank Algorithm in Requirement Concern Impact Analysis
abstract: As an important part of requirement management, managing requirement change plays a key role in controlling project schedule and costs at early stage. Effective requirement impact analysis would give proper assessment on the effect of certain requirement changes on the whole system, and provide useful information for making trade-off decisions on future system design and implementation. In this paper a quantitative approach to concern impact analysis at requirement level has been proposed with the application of PageRank algorithm, which is a successful link based web page sorting algorithm. At first, separation of concerns is applied during deriving formal requirement specification from textual requirement statements. Next, concerns are specified and concern relationship graph is established. Finally, PageRank algorithm is utilized on concern relationship graph for assessing the impact of concern changes. Our approach has been applied to hallway section in Light Control System and validation of analysis result has been stated.

title: A Novel Approach Using PCA and SVM for Face Detection
abstract: Nowadays, face detection and recognition have gained importance in security and information access. In this paper, an efficient method of face detection based on principal components analysis (PCA) and support vector machine (SVM) is proposed. It firsly filter the face potential area using statistical feature which is generated by analyzing local histogram distribution. And then, SVM classifier is used to detect face feature in the test image, SVM has great performance in classification task. PCA is used to reduce dimension of sample data. After PCA transform, the feature vectors, which are used for training SVM classifier, are generated. Our tests in this paper are based on CMU face database. The experimental results demonstrate that the proposed method is encouraging with a successful detection rate.