14562584
Integrating functional and security requirements with use case decomposition
Misuse case modeling is a viable option to depict the security requirements together with functional requirements. To investigate the interplay between functional and security requirements, this paper presents an approach to decomposing use cases, misuse cases, and mitigation use cases. We identify relationships among decomposed cases for each case type (use, misuse, mitigation use) and ensure consistency among the cases as decomposition occurs by properly modeling shared and optional cases. We also assign applicable actors to the decomposed cases. Decomposition is conducted for each case type independently and then integrated with the "threatens" and "mitigates" relationships. We provide processes for the proper use of the "threatens" relationship between misuse cases and use cases and the "mitigates" relationship between mitigation use cases and misuse cases at different levels of abstraction. Thus, a complete set of security-centric requirements can be specified from the project outset to guide subsequent software development phases
9746543
Design an Interoperable Mobile Agent System Based on Predicate Transition Net Models
Mobile agents provide an effective and flexible style to develop advanced distributed systems. In order to promote interoperability and ensure the quality of mobile agent systems, it is necessary to formalize software architecture of mobile agent systems. In this paper, we not only define the software architecture of interoperable mobile agent systems using predicate transition nets, but also analyze the interoperability between agents. In addition, a formal model-driven design method to develop mobile agent systems is described with examples. Our method naturally integrates formal methods and practical approaches in the agent system design phase. The method can be used to develop other complex software systems as well.
11130686
An Aspect-Oriented Approach to Security Requirements Analysis
This paper presents an aspect-oriented approach to integrated elicitation of functional and security requirements based on use case-driven development. We identify security threats with respect to use cases and adopt threat mitigations for preventing or reducing security threats. To capture crosscutting nature of threats and mitigations, we specify them as aspects that encapsulate pointcuts and advice. A threat (mitigation) pointcut is a collection of join points in use cases at which the use cases are threatened (secured); whereas threat/mitigation advice describes how a threat can become an attack (can be mitigated). Eliciting threats and mitigations as aspects provides a structured way for separating functional and security concerns
14135016
A Study on Illumination Invariant Face Recognition Methods Based on Multiple Eigenspaces
This paper presents two multiple illumination eigenspaces-based methods, RDEB and BPNNB, for solving the variable illumination problem of face recognition. The experiment shows that the methods have a high recognition ratio. In particular, BPNNB has outperformed the assumptive method which knows the illumination directions of faces and completes recognition in the specific eigenspace using eigenface method[2] for each face subset with a specific illumination direction.
11698669
Modeling and verifying multi-agent behaviors using predicate/transition nets
In a multi-agent system, how agents accomplish a goal task is usually specified by multi-agent plans built from basic actions (e.g. operators) of which the agents are capable. A critical problem with such an approach is how can the designer make sure the plans are reliable. To tackle this problem, this paper presents a formal approach for modeling and analyzing multi-agent behaviors using Predicate/Transition (PrT) nets, a high-level formalism of Petri nets. We construct a multi-agent model by representing agent capabilities as transitions. To verify a multi-agent PrT model, we adapt the planning graphs as a compact structure for the reachability analysis. We also demonstrate that, based on the PrT model, whether parallel actions specified in multi-agent plans can be executed in parallel and whether the plans guarantee the achievement of the goal can be verified by analyzing the dependency relations among the transitions.
41800471
Ensuring Consistent Use/Misuse Case Decomposition for Secure Systems
1250480
Understanding protein dispensability through machine-learning analysis of high-throughput data
MOTIVATION
Protein dispensability is fundamental to the understanding of gene function and evolution. Recent advances in generating high-throughput data such as genomic sequence data, protein-protein interaction data, gene-expression data and growth-rate data of mutants allow us to investigate protein dispensability systematically at the genome scale.


RESULTS
In our studies, protein dispensability is represented as a fitness score that is measured by the growth rate of gene-deletion mutants. By the analyses of high-throughput data in yeast Saccharomyces cerevisiae, we found that a protein's dispensability had significant correlations with its evolutionary rate and duplication rate, as well as its connectivity in protein-protein interaction network and gene-expression correlation network. Neural network and support vector machine were applied to predict protein dispensability through high-throughput data. Our studies shed some lights on global characteristics of protein dispensability and evolution.


AVAILABILITY
The original datasets for protein dispensability analysis and prediction, together with related scripts, are available at http://digbio.missouri.edu/~ychen/ProDispen/


CONTACT
xudong@missouri.edu.
7047265
Privacy-preserving alert correlation: a concept hierarchy based approach
With the increasing security threats from infrastructure attacks such as worms and distributed denial of service attacks, it is clear that the cooperation among different organizations is necessary to defend against these attacks. However, organizations' privacy concerns for the incident and security alert data require that sensitive data be sanitized before they are shared with other organizations. Such sanitization process usually has negative impacts on intrusion analysis (such as alert correlation). To balance the privacy requirements and the need for intrusion analysis, we propose a privacy-preserving alert correlation approach based on concept hierarchies. Our approach consists of two phases. The first phase is entropy guided alert sanitization, where sensitive alert attributes are generalized to high-level concepts to introduce uncertainty into the dataset with partial semantics. To balance the privacy and the usability of alert data, we propose to guide the alert sanitization process with the entropy or differential entropy of sanitized attributes. The second phase is sanitized alert correlation. We focus on defining similarity functions between sanitized attributes and building attack scenarios from sanitized alerts. Our preliminary experimental results demonstrate the effectiveness of the proposed techniques
9229028
A conceptual model of personalized virtual learning environments
The Virtual Learning Environment (VLE) is one of the fastest growing areas in educational technology research and development. In order to achieve learning effectiveness, ideal VLEs should be able to identify learning needs and customize solutions, with or without an instructor to supplement instruction. They are called Personalized VLEs (PVLEs). In order to achieve PVLEs success, comprehensive conceptual models corresponding to PVLEs are essential. Such conceptual modeling development is important because it facilitates early detection and correction of system development errors. Therefore, in order to capture the PVLEs knowledge explicitly, this paper focuses on the development of conceptual models for PVLEs, including models of knowledge primitives in terms of learner, curriculum, and situational models, models of VLEs in general pedagogical bases, and particularly, the definition of the ontology of PVLEs on the constructivist pedagogical principle. Based on those comprehensive conceptual models, a prototyped multiagent-based PVLE has been implemented. A field experiment was conducted to investigate the learning achievements by comparing personalized and non-personalized systems. The result indicates that the PVLE we developed under our comprehensive ontology successfully provides significant learning achievements. These comprehensive models also provide a solid knowledge representation framework for PVLEs development practice, guiding the analysis, design, and development of PVLEs.
40999406
Multi-agent collaboration for B2B workflow monitoring
Business-to-business (B2B) applications environments are exceedingly dynamic and competitive. This dynamism is manifested in the form of changing process requirements and time constraints. However, current workflow management technologies have difficulties to solve the challenges problems, such as: how to deal with the dynamic nature of B2B commerce processes, how to manage the distributed knowledge and recourses, and how to reduce the transaction risk. In this paper, a collaborative multi-agent system has been proposed. Multiple intelligent agents in our system can work together not only to identify the workflow problems, but also to solve such problems, by applying business rules, such as re-organizing the procurement and the transaction processes, and making necessary workflow process changes.
13620764
Multi-level differential surface representation based on local transformations
One crucial issue of multi-resolution surface representations is how to effectively record and reconstruct geometric details among surface levels. Standard multi-resolution techniques encode details directly as local displacements in the vertices, and may produce unplausible results when the base level endures large deformations. In this paper we propose an alternative detail representation and reconstruction scheme, based on local transformations on a per-triangle basis. While more storage is required, recording details as local transformations favors global coupling of geometric details and allows for large-scale surface manipulations. By modeling the scale components of the surface modifications as a set of deforming factors, we achieved detail-preserving reconstruction results naturally under very large deformations. Comprehensive experimental results verify the efficiency and robustness of our approach.