scholarly journals Contextualized Filtering for Shared Cyber Threat Information

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4890
Author(s):  
Athanasios Dimitriadis ◽  
Christos Prassas ◽  
Jose Luis Flores ◽  
Boonserm Kulvatunyou ◽  
Nenad Ivezic ◽  
...  
Keyword(s):  
Information Sharing ◽  
Business Processes ◽  
State Of The Art ◽  
Contextual Information ◽  
Coarse Grained ◽  
Business Information ◽  
Cyber Threat ◽  
Domain Expertise ◽  
Multi Level ◽  
Filtering Approach

Cyber threat information sharing is an imperative process towards achieving collaborative security, but it poses several challenges. One crucial challenge is the plethora of shared threat information. Therefore, there is a need to advance filtering of such information. While the state-of-the-art in filtering relies primarily on keyword- and domain-based searching, these approaches require sizable human involvement and rarely available domain expertise. Recent research revealed the need for harvesting of business information to fill the gap in filtering, albeit it resulted in providing coarse-grained filtering based on the utilization of such information. This paper presents a novel contextualized filtering approach that exploits standardized and multi-level contextual information of business processes. The contextual information describes the conditions under which a given threat information is actionable from an organization perspective. Therefore, it can automate filtering by measuring the equivalence between the context of the shared threat information and the context of the consuming organization. The paper directly contributes to filtering challenge and indirectly to automated customized threat information sharing. Moreover, the paper proposes the architecture of a cyber threat information sharing ecosystem that operates according to the proposed filtering approach and defines the characteristics that are advantageous to filtering approaches. Implementation of the proposed approach can support compliance with the Special Publication 800-150 of the National Institute of Standards and Technology.

Risk Management using Cyber-Threat Information Sharing and Cyber-Insurance

2017 ◽  
Author(s):  
Deepak K. Tosh ◽  
Sachin Shetty ◽  
Shamik Sengupta ◽  
Jay P. Kesan ◽  
Charles Kamhoua
Keyword(s):  
Risk Management ◽  
Information Sharing ◽  
Cyber Insurance ◽  
Cyber Threat

Multi-level 3D Densenets for False-positive Reduction in Lung Nodule Detection Based on Chest Computed Tomography

2020 ◽  
Vol 16 (8) ◽  
pp. 1004-1021
Author(s):  
Xiaoqi Lu ◽  
Yu Gu ◽  
Lidong Yang ◽  
Baohua Zhang ◽  
Ying Zhao ◽  
...  
Keyword(s):  
False Positive ◽  
Contextual Information ◽  
Lung Nodule ◽  
Positive Sample ◽  
Lung Nodules ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Level Information ◽  
Multi Level ◽  
Cade System

Objective: False-positive nodule reduction is a crucial part of a computer-aided detection (CADe) system, which assists radiologists in accurate lung nodule detection. In this research, a novel scheme using multi-level 3D DenseNet framework is proposed to implement false-positive nodule reduction task. Methods: Multi-level 3D DenseNet models were extended to differentiate lung nodules from falsepositive nodules. First, different models were fed with 3D cubes with different sizes for encoding multi-level contextual information to meet the challenges of the large variations of lung nodules. In addition, image rotation and flipping were utilized to upsample positive samples which consisted of a positive sample set. Furthermore, the 3D DenseNets were designed to keep low-level information of nodules, as densely connected structures in DenseNet can reuse features of lung nodules and then boost feature propagation. Finally, the optimal weighted linear combination of all model scores obtained the best classification result in this research. Results: The proposed method was evaluated with LUNA16 dataset which contained 888 thin-slice CT scans. The performance was validated via 10-fold cross-validation. Both the Free-response Receiver Operating Characteristic (FROC) curve and the Competition Performance Metric (CPM) score show that the proposed scheme can achieve a satisfactory detection performance in the falsepositive reduction track of the LUNA16 challenge. Conclusion: The result shows that the proposed scheme can be significant for false-positive nodule reduction task.

THE APPLICATION OF PETRI NETS TO WORKFLOW MANAGEMENT

1998 ◽  
Vol 08 (01) ◽  
pp. 21-66 ◽  
Author(s):  
W. M. P. VAN DER AALST
Keyword(s):  
Petri Nets ◽  
Petri Net ◽  
Business Processes ◽  
State Of The Art ◽  
Workflow Management ◽  
Explicit Representation ◽  
The Other ◽  
Application Domain ◽  
Analysis Techniques ◽  
The One

Workflow management promises a new solution to an age-old problem: controlling, monitoring, optimizing and supporting business processes. What is new about workflow management is the explicit representation of the business process logic which allows for computerized support. This paper discusses the use of Petri nets in the context of workflow management. Petri nets are an established tool for modeling and analyzing processes. On the one hand, Petri nets can be used as a design language for the specification of complex workflows. On the other hand, Petri net theory provides for powerful analysis techniques which can be used to verify the correctness of workflow procedures. This paper introduces workflow management as an application domain for Petri nets, presents state-of-the-art results with respect to the verification of workflows, and highlights some Petri-net-based workflow tools.

The Impact of Stationarity, Regularity, and Context on the Predictability of Individual Human Mobility

2021 ◽  
Vol 7 (4) ◽  
pp. 1-24
Author(s):  
Douglas Do Couto Teixeira ◽  
Aline Carneiro Viana ◽  
Jussara M. Almeida ◽  
Mrio S. Alvim
Keyword(s):  
State Of The Art ◽  
Human Mobility ◽  
Contextual Information ◽  
Prediction Method ◽  
Mobility Prediction ◽  
Mobility Patterns ◽  
Distinct Cell ◽  
Inherent Nature ◽  
The One ◽  
The Impact

Predicting mobility-related behavior is an important yet challenging task. On the one hand, factors such as one’s routine or preferences for a few favorite locations may help in predicting their mobility. On the other hand, several contextual factors, such as variations in individual preferences, weather, traffic, or even a person’s social contacts, can affect mobility patterns and make its modeling significantly more challenging. A fundamental approach to study mobility-related behavior is to assess how predictable such behavior is, deriving theoretical limits on the accuracy that a prediction model can achieve given a specific dataset. This approach focuses on the inherent nature and fundamental patterns of human behavior captured in that dataset, filtering out factors that depend on the specificities of the prediction method adopted. However, the current state-of-the-art method to estimate predictability in human mobility suffers from two major limitations: low interpretability and hardness to incorporate external factors that are known to help mobility prediction (i.e., contextual information). In this article, we revisit this state-of-the-art method, aiming at tackling these limitations. Specifically, we conduct a thorough analysis of how this widely used method works by looking into two different metrics that are easier to understand and, at the same time, capture reasonably well the effects of the original technique. We evaluate these metrics in the context of two different mobility prediction tasks, notably, next cell and next distinct cell prediction, which have different degrees of difficulty. Additionally, we propose alternative strategies to incorporate different types of contextual information into the existing technique. Our evaluation of these strategies offer quantitative measures of the impact of adding context to the predictability estimate, revealing the challenges associated with doing so in practical scenarios.

Knowledge Transfer for Entity Resolution with Siamese Neural Networks

2021 ◽  
Vol 13 (1) ◽  
pp. 1-25
Author(s):  
Michael Loster ◽  
Ioannis Koumarelas ◽  
Felix Naumann
Keyword(s):  
Knowledge Transfer ◽  
Similarity Measure ◽  
State Of The Art ◽  
Similarity Measures ◽  
Engineering Process ◽  
Domain Experts ◽  
Multiple Datasets ◽  
Multiple Data ◽  
Domain Expertise ◽  
F Measure

The integration of multiple data sources is a common problem in a large variety of applications. Traditionally, handcrafted similarity measures are used to discover, merge, and integrate multiple representations of the same entity—duplicates—into a large homogeneous collection of data. Often, these similarity measures do not cope well with the heterogeneity of the underlying dataset. In addition, domain experts are needed to manually design and configure such measures, which is both time-consuming and requires extensive domain expertise. We propose a deep Siamese neural network, capable of learning a similarity measure that is tailored to the characteristics of a particular dataset. With the properties of deep learning methods, we are able to eliminate the manual feature engineering process and thus considerably reduce the effort required for model construction. In addition, we show that it is possible to transfer knowledge acquired during the deduplication of one dataset to another, and thus significantly reduce the amount of data required to train a similarity measure. We evaluated our method on multiple datasets and compare our approach to state-of-the-art deduplication methods. Our approach outperforms competitors by up to +26 percent F-measure, depending on task and dataset. In addition, we show that knowledge transfer is not only feasible, but in our experiments led to an improvement in F-measure of up to +4.7 percent.

Social business process management

2019 ◽  
Vol 26 (1) ◽  
pp. 191-211
Author(s):  
Patricia Bazan ◽  
Elsa Estevez
Keyword(s):  
Business Process ◽  
Business Process Management ◽  
Process Management ◽  
Business Processes ◽  
New Technologies ◽  
State Of The Art ◽  
Implicit Knowledge ◽  
Social Software ◽  
Social Business ◽  
Content Type

Purpose The purpose of this paper is to assess the state of the art of social business process management (Social BPM), explaining applied approaches, existing tools and challenges and to propose a research agenda for encouraging further development of the area. Design/methodology/approach The methodology comprises a qualitative analysis using secondary data. The approach relies on searches of scientific papers conducted in well-known databases, identifying research work related to Social BPM solutions and those contributing with social characteristics to BPM. Based on the identified papers, the authors selected the most relevant and the latest publications, and categorized their contributions and findings based on open and selective coding. In total, the analysis is based on 51 papers that were selected and analyzed in depth. Findings Main results show that there are several studies investigating modeling approaches for socializing process activities and for capturing implicit knowledge possessed and used by process actors, enabling to add some kind of flexibility to business processes. However, despite the proven interest in the area, there are not yet adequate tools providing effective solutions for Social BPM. Based on our findings, the authors propose a research agenda comprising three main lines: contributions of social software (SS) to Social BPM, Social BPM as a mechanism for adding flexibility to and for discovering new business processes and Social BPM for enhancing business processes with the use of new technologies. The authors also identify relevant problems for each line. Practical implications Some SS tools, like wikis, enable managing social aspects in executing business processes and can be used to coordinate simple business processes. Despite they are commonly used, they are not yet mature tools supporting Social BPM and more efficient tools are yet to appear. The lack of tools preclude organizations from benefitting from implicit knowledge owned by and shared among business process actors, which could contribute to better-informed decisions related to organizational processes. In addition, more research is needed for considering Social BPM as an approach for organizations to benefit from the adoption of new technologies in their business processes. Originality/value The paper assesses the state of the art in Social BPM, an incipient area in research and practice. The area can be defined as the intersection of two bigger areas highly relevant for organizations; on the one hand, the management and execution of business processes; and on the other hand, the use of social software, including social media tools, for leveraging on implicit knowledge shared by business process actors to improving efficiency of business processes.

scholarly journals One for All: Neural Joint Modeling of Entities and Events

2019 ◽  
Vol 33 ◽  
pp. 6851-6858 ◽  
Author(s):  
Trung Minh Nguyen ◽  
Thien Huu Nguyen
Keyword(s):  
Deep Learning ◽  
Recent Work ◽  
State Of The Art ◽  
Contextual Information ◽  
Joint Modeling ◽  
Event Extraction ◽  
Event Trigger ◽  
The Individual ◽  
Novel Model ◽  
Argument Roles

The previous work for event extraction has mainly focused on the predictions for event triggers and argument roles, treating entity mentions as being provided by human annotators. This is unrealistic as entity mentions are usually predicted by some existing toolkits whose errors might be propagated to the event trigger and argument role recognition. Few of the recent work has addressed this problem by jointly predicting entity mentions, event triggers and arguments. However, such work is limited to using discrete engineering features to represent contextual information for the individual tasks and their interactions. In this work, we propose a novel model to jointly perform predictions for entity mentions, event triggers and arguments based on the shared hidden representations from deep learning. The experiments demonstrate the benefits of the proposed method, leading to the state-of-the-art performance for event extraction.

scholarly journals Preface to the April 2018 Issue including selected works from CIbSE 2017 and LACLO 2016

2018 ◽  
Vol 21 (1) ◽  
Author(s):  
Héctor Cancela ◽  
Isabel Brito ◽  
Luca Cernuzzi ◽  
Marcela Genero ◽  
Jesús García Molina ◽  
...  
Keyword(s):  
Costa Rica ◽  
Software Engineering ◽  
Latin American ◽  
Buenos Aires ◽  
Review Paper ◽  
State Of The Art ◽  
Contextual Information ◽  
Learning Objects ◽  
The State ◽  
San Jose

This issue of the CLEIej consists of three main parts: i) a review paper on the state of the art of how contextual information extracted from a user task can help to improve searches for contents relevant to this task; ii) extended and revised versions of Selected Papers (which correspond to the second and third best paper from each track) presented at the XX Ibero-American Conference on Software Engineering (CIbSE 2017), which took place in Buenos Aires, Argentina, in May 2017; and, iii) extended and revised versions of selected papers from LACLO 2016, the XI Latin American Conference on Learning Objects and Technology, which took place in San José, Costa Rica, in October 2016.

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