The growing system complexity of microservice architectures and the bilateral enhancement of artificial intelligence (AI) for both attackers and defenders present increasing security challenges for cloud-native operations. In particular, cloud-native operators require a holistic view of the dynamic security posture for the microservice-based cloud-native environment from a defense aspect. Additionally, both attackers and defenders can adopt advanced AI technologies. This makes the dynamic interaction and benchmark among different intelligent offense and defense strategies more crucial. Hence, following the multi-agent deep reinforcement learning (RL) paradigm, this research develops an agent-based intelligent security service framework (ISSF) for cloud-native operations. It includes a dynamic attack graph model to represent the cloud-native environment and an action model to represent offense and defense actions. Then we develop an approach to enable the training, publishing, and evaluating of intelligent security services using diverse deep RL algorithms and training strategies, facilitating their systematic development and benchmarking. The experiments demonstrate that our framework can sufficiently model the security posture of a cloud-native system for defenders, effectively develop and quantitatively benchmark different intelligent security services for both attackers and defenders, and guide further optimization.

We developed a simulation game to study the effectiveness of decision-makers in overcoming two complexities in building cybersecurity capabilities: potential delays in capability development; and uncertainties in predicting cyber incidents. Analyzing 1479 simulation runs, we compared the performances of a group of experienced professionals with those of an inexperienced control group. Experienced subjects did not understand the mechanisms of delays any better than inexperienced subjects; however, experienced subjects were better able to learn the need for proactive decision-making through an iterative process. Both groups exhibited similar errors when dealing with the uncertainty of cyber incidents. Our findings highlight the importance of training for decision-makers with a focus on systems thinking skills, and lay the groundwork for future research on uncovering mental biases about the complexities of cybersecurity.

The Peer-To-Peer Human-Robot Interaction (P2P-HRI) project is developing techniques to improve task coordination and collaboration between human and robot partners. Our work is motivated by the need to develop effective human-robot teams for space mission operations. A central element of our approach is creating dialogue and interaction tools that enable humans and robots to flexibly support one another. In order to understand how this approach can influence task performance, we recently conducted a series of tests simulating a lunar construction task with a human-robot team. In this paper, we describe the tests performed, discuss our initial results, and analyze the effect of intervention on task performance.

The change in meaning of data over time poses significant challenges for the use of that data. These challenges exist in the use of an individual data source and are further compounded with the integration of multiple sources. In this paper, we identify three types of temporal semantic heterogeneities. We propose a solution based on extensions to the context interchange framework, which has mechanisms for capturing semantics using ontology and temporal context. It also provides a mediation service that automatically resolves semantic conflicts. We show the feasibility of this approach with a prototype that implements a subset of the proposed extensions.

The proliferation of electronically available data within large organizations as well as publicly available data (e.g. over the World Wide Web) poses challenges for users who wish to efficiently interact with and integrate multiple heterogeneous sources. This paper presents CI3, a corporate information integrator, which applies XML as a tool to facilitate data mediation and integration amongst heterogeneous sources in the context of financial analysts creating corporate profiles. Sources include Lotus Notes, relational databases, and the World Wide Web. CI3 applies a unified XML data model to automate integration. By preserving metadata about the source of each datum in the integrated result set, CI3 supports source attribution. Users may trace the attribution metadata from the result back to the underlying sources and leverage their expertise in interpreting the data and, if necessary, use their judgment in assessing the authenticity and veracity of results. We present a functional overview of CI3, its system architecture including the XML data model, and the integration procedures. We conclude by reflecting on lessons learned.

TheContext Interchange strategypresents a novel perspective for mediated data access in which semantic conflicts among heterogeneous systems are not identified a priori, but are detected and reconciled by acontext mediatorthrough comparison ofcontexts axiomscorresponding to the systems engaged in data exchange. In this article, we show that queries formulated on shared views, export schema, and shared “ontologies” can be mediated in the same way using theContext Interchange framework. The proposed framework provides a logic-based object-oriented formalsim for representing and reasoning about data semantics in disparate systems, and has been validated in a prototype implementation providing mediated data access to both traditional and web-based information sources.

The paper reports on a practical implementation of a context mediator for the fixed income securities industry. The authors describe industry circumstances and the data and calculation services (DCS) mediator developed and deployed in the early 1990s. The mediator was designed as an interpretive engine controlled by a static declarative knowledge structure and client preference data. In addition to heterogeneous, autonomous data sources, the mediator integrated autonomously developed local and remote procedural components. Client access to both data and computational resources were provided through an active conceptual model. Structural and semantic context conversions were used to integrate disparate components and to support varying client needs. Lessons learned from the implementation and usage of this mediator provide insight into the requirements for a successful context mediator.

The Context Interchange strategy presents a novel approach for mediated data access in which semantic conflicts among heterogeneous systems are not identified a priori, but are detected and reconciled by a context mediator through comparison of contexts. This paper reports on the implementation of a Context Interchange Prototype which provides a concrete demonstration of the features and benefits of this integration strategy.

Currently, there is a proliferation of database integration approaches in response to the need to achieve semantic interoperability in heterogeneous, distributed and autonomous environments. To date, however, we lack abstract, formal descriptions of the task of semantic interoperation, independent of idiosyncratic implementation details. Such abstract descriptions are needed to aid our understanding of these various complex systems that are being constructed. Therefore, we argue that a knowledge level perspective of interoperable systems is desirable. The knowledge level serves as an abstract specification of what a system should do and facilitates the design and analysis of complex systems. In this paper, we show how an interoperable system, based on the Context Interchange Architecture, may be specified in terms of first-order logic which is an ideal specification language at the knowledge level. This specification represents a theoretical ideal which serves to formalize and communicate the key ideas behind the Context Interchange Approach, unencumbered by the details, limitations and compromises of specific implementations. As such, it provides a rigorous basis for implementing such systems. Similar specifications may also be developed for other models of interoperable systems. As a result, we have a rigorous means of understanding, comparing and analyzing these complex systems.

Research in database interoperability has primarily focused on circumventing schematic and semantic incompatibility arising from autonomy of the underlying databases. We argue that, while existing integration strategies might provide satisfactory support for small or static systems, their inadequacies rapidly become evident in large-scale interoperable database systems operating in a dynamic environment. This paper highlights the problem of receiver heterogeneity, scalability, and evolution which have received little attention in the literature, provides an overview of the Context Interchange approach to interoperability, illustrates why this is able to better circumvent the problems identified, and forges the connections to other works by suggesting how the context interchange framework differs from other integration approaches in the literature.

Large organizations need to exchange information among many separately developed systems. In order for this exchange to be useful, the individual systems must agree on the meaning of their exchanged data. That is, the organization must ensure semantic interoperability . This paper provides a theory of semantic values as a unit of exchange that facilitates semantic interoperability betweeen heterogeneous information systems. We show how semantic values can either be stored explicitly or be defined by environments . A system architecture is presented that allows autonomous components to share semantic values. The key component in this architecture is called the context mediator , whose job is to identify and construct the semantic values being sent, to determine when the exchange is meaningful, and to convert the semantic values to the form required by the receiver. Our theory is then applied to the relational model. We provide an interpretation of standard SQL queries in which context conversions and manipulations are transparent to the user. We also introduce an extension of SQL, called Context-SQL (C-SQL), in which the context of a semantic value can be explicitly accessed and updated. Finally, we describe the implementation of a prototype context mediator for a relational C-SQL system.

The use of inference rules to support intelligent data processing is an increasingly important tool in many areas of computer science. In database systems, rules are used in semantic query optimization as a method for reducing query processing costs. The savings is dependent on the ability of experts to supply a set of useful rules and the ability of the optimizer to quickly find the appropriate transformations generated by these rules. Unfortunately, the most useful rules are not always those that would or could be specified by an expert. This paper describes the architecture of a system having two interrelated components: a combined conventional/semantic query optimizer, and an automatic rule deriver. Our automatic rule derivation method uses intermediate results from the optimization process to direct the search for learning new rules. Unlike a system employing only user-specified rules, a system with an automatic capability can derive rules that may be true only in the current state of the database and can modify the rule set to reflect changes in the database and its usage pattern. This system has been implemented as an extension of the EXODUS conventional query optimizer generator. We describe the implementation, and show how semantic query optimization is an extension of conventional optimization in this context.

C<,retoness conditicms arc given which describe some nf tile pt'tq~crlie~ c×hibiLed by Ilighly aw~itable distributed database systems sttch a:; the 511,'x.RI) (Systcnl I~n' l lighly Available Replicated Data) ,¢.ystem cuurcntlv be~hg developed at Comptller Corporation of /~l]lOi'iC;i.This svslcm allows a da~d~ase application to contilme OpClatlt:~l ill tile lace o1" communicatiun faih.llCS,incheding fietwork pa0"tidons.A penalty is paid tbr thir; c×tz'a availability: the usual corrc,:tncss conditi,n,~; serializability of tr:msactions and preservation of iritcgrily c,n.~traims, ,:re not guaranteed.I Iowevcr, it is still possihle to make intcrestin~g claims about the beh,vior e,f the system.The kinds of claims whicli can be 9roved include bom~ds on tile costs oF violatior, of integrity cofistraints, and Ihirness guma,tces.In comrast to serializ~bility:s all-or-m~thing character, this work has a "continuous" flavor:small changes in available inform~itiou lead a) small perturbations in corrcctuess conditk)ns."['l:.iswork is novel, because there has been very little previous success in'stating .inlcresti'ngproperties whk:h are guaranteed by nonscrializable systems.