The concept of trust and/or trust management has received considerable attention in engineering research communities as trust is perceived as the basis for decision making in many contexts and the motivation for maintaining long-term relationships based on cooperation and collaboration Even if substantial research effort has been dedicated to addressing trust-based mechanisms or trust metrics (or computation) in diverse contexts, prior work has not clearly solved the issue of how to model and quantify trust with sufficient detail and context-based adequateness The issue of trust quantification has become more complicated as we have the need to derive trust from complex, composite networks that may involve four distinct layers of communication protocols, information exchange, social interactions, and cognitive motivations In addition, the diverse application domains require different aspects of trust for decision making such as emotional, logical, and relational trust This survey aims to outline the foundations of trust models for applications in these contexts in terms of the concept of trust, trust assessment, trust constructs, trust scales, trust properties, trust formulation, and applications of trust We discuss how different components of trust can be mapped to different layers of a complex, composite network; applicability of trust metrics and models; research challenges; and future work directions

https://dl.acm.org/doi/pdf/10.1145/2815595

A Survey on Trust Modeling

Data and information fusion (DIF) involves a process of combining data from multiple inputs (from one or more sources such as sensors and textual reports).1 The aim is to obtain information that is better (more useful and meaningful) than that would be derived from each of the sources individually (that is, without fusing). DIF is emerging as an important field of multidisciplinary study [113, 316]. This is due to increase in data and information flow, as well as improvement in communication, computing and sensor technology. The first applications of DIF techniques were in the military arena [177, 179, 455]. The use of DIF techniques for problem-solving has extended to many non-military applications in the commercial and industrial sectors [177, 179, 199, 228]. In general, data and information fusion can provide enhancement to the outcomes of processes for solving various application problems. Some advantages of carrying out DIF include [316]: 2 improvement in the accuracy of data, as well as reduction in uncertainty and ambiguity within data, and 2 improvement in situation awareness (SAW) and inference that lead to better decision making.

High-level Information Fusion: An Overview.

The publication of Linked Open Data on the Web has gained tremendous momentum over the last five years. This development makes possible (and interesting) the execution of queries using up-to-date data from multiple, automatically discovered data sources. As a result, we currently witness the emergence of a new research area that focuses on an online execution of Linked Data queries; i.e. queries that range over data that is made available using the Linked Data publishing principles. This article provides a general overview on this new area. In particular, we introduce the specific challenges that need to be addressed and then focus on possible strategies for executing Linked Data queries. Furthermore, we classify approaches proposed in the literature w.r.t. these strategies.

/pdf/an-overview-on-execution-strategies-for-linked-data-queries-5e7qj23yii.pdf

An Overview on Execution Strategies for Linked Data Queries

APT datasets and attack modeling for automated detection methods: A review

Pain monitoring: A dynamic and context-sensitive system

In this paper, we argue that query relaxation over RDF data is an important but largely overlooked research topic: the Semantic Web standards allow for answering crisp queries over crisp RDF data, but what of use-cases that require approximate answers for fuzzy queries over crisp data? We introduce a use-case from an EADS project that aims to aggregate intelligence information for police post-incident analysis. Query relaxation is needed to match incomplete descriptions of entities involved in crimes to structured descriptions thereof. We first discuss the use-case, formalise the problem, and survey current literature for possible approaches. We then present a proof-of-concept framework for enabling relaxation of structured entity-lookup queries, evaluating different distance measures for performing relaxation. We argue that beyond our specific scenario, query relaxation is important to many potential use-cases for Semantic Web technologies, and worthy of more attention.

/pdf/towards-fuzzy-query-relaxation-for-rdf-3hss09y2il.pdf

Towards fuzzy query-relaxation for RDF

This paper describes the integration of a particle filter and a continuous version o f the transferable belief model. The output from the particle filter is used as input to the transferable belief model. The transferable belief model's continuous nature allows for the prior knowledge over the classification space to be incorporated within the system. Classification of objects is demonstrated within the paper and compared to the more classical Bayesian classification routine. This is the first time that such an approach has been taken to jointly classify and track targets. We show that there is a great deal o f flexibility built into the continuous transferable belief model and in our comparison with a Bayesian classifier, we show that our novel approach offers a more robust classification output that is less influenced by noise.

Joint Tracking and Classification of Airbourne Objects using Particle Filters and the Continuous Transferable Belief Model

Presents an implementation of forward-looking infrared (FLIR) and laser radar (LADAR) data simulation for use in developing a multi-sensor data-fusion automated target recognition (ATR) system. Through the use of commercial models and software, we can create a highly detailed scene model, which provides a rich data set for later processing. We embed our own modules within this software to extract data from the scene and then present it to the FLIR and LADAR sensor models. These models produce simulated range and temperature readings for objects within the scene. These data are used to create a variety of images to aid in the visualisation of the data and to test our ATR system. Frames from approach sequences show the LADAR and FLIR data in different formats. The simulated data and subsequent images are accurate and rapid to produce, and provide invaluable data resources for testing an ATR system.

Simulation of FLIR and LADAR data using graphics animation software

Intelligence operations are dependent on humans for data gathering and processing. Measurements made by human observation are subjective and incorporate biases. Systematic processing of such data is not as straightforward as with electronic sensor data. This paper presents work in progress towards developing a system to fuse inconsistent information and minimising observation errors. A case-study is presented, relating the techniques to the application area of civilian intelligence systems. Within our system, the Transferable Belief Model (TBM) was used to fuse soft information from observers, and combine in both the discrete and continuous spaces.

Fusion of soft information using TBM

eXfiltration Advanced Persistent Threats (XAPTs) increasingly account for incidents concerned with intelligence information gathering by malicious adversaries. This research exploits the multi-phase nature of an XAPT, mapping its phases into a cyber attack kill chain. A novel Markov Multi-Phase Transferable Belief Model (MM-TBM) is proposed and demonstrated for fusing incoming evidence from a variety of sources which takes into account conflicting information. The MM-TBM algorithm predicts a cyber attacker's actions against a computer network and provides a visual representation of their footsteps.

Gavin Powell

Papers

Towards fuzzy query-relaxation for RDF

Joint Tracking and Classification of Airbourne Objects using Particle Filters and the Continuous Transferable Belief Model

Simulation of FLIR and LADAR data using graphics animation software

Fusion of soft information using TBM

A Markov multi-phase transferable belief model: An application for predicting data exfiltration APTs