An Attack Detection Mechanism Based on a Distributed Hierarchical Multi-agent Architecture for Protecting Databases

This paper presents an innovative approach to detect and classify SQL injection attacks. The existing approaches are centralized while this proposal is based on a distributed hierarchical architecture to provide a robust and dynamic strategy. The strategy for the classification and detection of SQL injection attacks uses a combination based on detection by anomalies and misuses. The detection by anomaly uses a case-based reasoning mechanism incorporating a mixture of neural networks. The approach has been tested and the results are presented in this paper.This paper presents an innovative approach to detect and classify SQL injection attacks. The existing approaches are centralized while this proposal is based on a distributed hierarchical architecture to provide a robust and dynamic strategy. The strategy for the classification and detection of SQL injection attacks uses a combination based on detection by anomalies and misuses. The detection by anomaly uses a case-based reasoning mechanism incorporating a mixture of neural networks. The approach has been tested and the results are presented in this paper

Improving Knowledge Retrieval in Digital Libraries Applying Intelligent Techniques

Nowadays an enormous quantity of heterogeneous and distributed information is stored in the digital University. Exploring online collections to find knowledge relevant to a user’s interests is a challenging work. The artificial intelligence and Semantic Web provide a common framework that allows knowledge to be shared and reused in an efficient way. In this work we propose a comprehensive approach for discovering E-learning objects in large digital collections based on analysis of recorded semantic metadata in those objects and the application of expert system technologies. We have used Case Based-Reasoning methodology to develop a prototype for supporting efficient retrieval knowledge from online repositories. We suggest a conceptual architecture for a semantic search engine. OntoUS is a collaborative effort that proposes a new form of interaction between users and digital libraries, where the latter are adapted to users and their surroundings

Modeling and Reasoning over Distributed Systems using Aspect-Oriented Graph Grammars

Aspect-orientation is a relatively new paradigm that introduces abstractions to modularize the implementation of system-wide policies. It is based on a composition operation, called aspect weaving, that implicitly modifies a base system by performing related changes within the system modules. Aspect-oriented graph grammars (AOGG) extend the classic graph grammar formalism by defining aspects as sets of rule-based modifications over a base graph grammar. Despite the advantages of aspect-oriented concepts regarding modularity, the implicit nature of the aspect weaving operation may also introduce issues when reasoning about the system behavior. Since in AOGGs aspect weaving is characterized by means of rule-based rewriting, we can overcome these problems by using known analysis techniques from the graph transformation literature to study aspect composition. In this paper, we present a case study of a distributed client-server system with global policies, modeled as an aspect-oriented graph grammar, and discuss how to use the AGG tool to identify potential conflicts in aspect weaving

Developing a Decision Support System leveraging Distributed and Heterogeneous Sources:Case-Based Reasoning for Manufacturing Incident Handling

Case-Based Reasoning is a proven method to provide decision support in a manufacturing context. However, data and knowledge relevant for the case representation is often spread over distributed sources, leading to challenges in the case representation and retrieval. Those challenges require different techniques that this PhD project aims to develop. Techniques for data collection and integration during the case representation, as well as similarity measurement during case retrieval. This paper describes the motivating problem, the research methods, and the current state and future plans

A Multiagent Solution to Adaptively Classify SOAP Message and Protect against DoS Attack

SOAP messages use XML code, which makes them vulnerable to denial of service (DoS) attacks and puts the availability of web services at risk. This article presents an adaptive solution for dealing with DoS attacks in web service environments. The solution proposes a distributed hierarchical multiagent architecture that implements a robust mechanism of classification based on an advanced CBR-BDI agent. The agent incorporates a case-based reasoning engine that integrate a Perceptron Multilayer neural network during the re-use phase to classify incoming SOAP messages and reject those that are considered malicious. A prototype of the architecture was developed and the results obtained are presented in this study.SOAP messages use XML code, which makes them vulnerable to denial of service (DoS) attacks and puts the availability of web services at risk. This article presents an adaptive solution for dealing with DoS attacks in web service environments. The solution proposes a distributed hierarchical multiagent architecture that implements a robust mechanism of classification based on an advanced CBR-BDI agent. The agent incorporates a case-based reasoning engine that integrate a Perceptron Multilayer neural network during the re-use phase to classify incoming SOAP messages and reject those that are considered malicious. A prototype of the architecture was developed and the results obtained are presented in this study

Efficient Aggregated Deliveries with Strong Guarantees in an Event-based Distributed System

A popular approach to designing large scale distributed systems is to follow an event-based approach. In an event-based approach, a set of software components interact by producing and consuming events. The event-based model allows for the decoupling of software components, allowing distributed systems to scale to a large number of components. Event correlation allows for higher order reasoning of events by constructing complex events from single, consumable events. In many cases, event correlation applications rely on centralized setups or broker overlay networks. In the case of centralized setups, the guarantees for complex event delivery are stronger, however, centralized setups create performance bottlenecks and single points of failure. With broker overlays, the performance and fault tolerance are improved but at the cost of weaker guarantees

Reflective learning conversations model for simulation debriefing: a co-design process and development innovation

© 2023 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Background: Health practitioners must be equipped with effective clinical reasoning skills to make appropriate, safe clinical decisions and avoid practice errors. Under-developed clinical reasoning skills have the potential to threaten patient safety and delay care or treatment, particularly in critical and acute care settings. Simulation-based education which incorporates post-simulation reflective learning conversations as a debriefing method is used to develop clinical reasoning skills while patient safety is maintained. However, due to the multidimensional nature of clinical reasoning, the potential risk of cognitive overload, and the varying use of analytic (hypothetical-deductive) and non-analytic (intuitive) clinical reasoning processes amongst senior and junior simulation participants, it is important to consider experience, competence, flow and amount of information, and case complexity related factors to optimize clinical reasoning while attending group- based post-simulation reflective learning conversations as a debriefing method. We aim to describe the development of a post-simulation reflective learning conversations model in which a number of contributing factors to achieve clinical reasoning optimization were addressed. Methods: A Co-design working group (N = 18) of doctors, nurses, researchers, educators, and patients’ representatives collaboratively worked through consecutive workshops to co-design a post-simulation reflective learning conversations model to be used for simulation debriefing. The co-design working group established the model through a theoretical and conceptual-driven process and multiphasic expert reviews. Concurrent integration of appreciative inquiry, plus/delta, and Bloom’s Taxonomy methods were considered to optimize simulation participants’ clinical reasoning while attending simulation activities. The face and content validity of the model were established using the Content Validity Index CVI and Content Validity Ratio CVR methods. Results: A Post-simulation reflective learning conversations model was developed and piloted. The model was supported with worked examples and scripted guidance. The face and content validity of the model were evaluated and confirmed. Conclusions: The newly co-designed model was established in consideration to different simulation participants’ seniority and competence, flow and amount of information, and simulation case complexity. These factors were considered to optimize clinical reasoning while attending group-based simulation activities.Peer reviewe

Semantical Markov Logic Network for Distributed Reasoning in Cyber-Physical Systems

The challenges associated with developing accurate models for cyber-physical systems are attributable to the intrinsic concurrent and heterogeneous computations of these systems. Even though reasoning based on interconnected domain specific ontologies shows promise in enhancing modularity and joint functionality modelling, it has become necessary to build interoperable cyber-physical systems due to the growing pervasiveness of these systems. In this paper, we propose a semantically oriented distributed reasoning architecture for cyber-physical systems. This model accomplishes reasoning through a combination of heterogeneous models of computation. Using the flexibility of semantic agents as a formal representation for heterogeneous computational platforms, we define autonomous and intelligent agent-based reasoning procedure for distributed cyber-physical systems. Sensor networks underpin the semantic capabilities of this architecture, and semantic reasoning based on Markov logic networks is adopted to address uncertainty in modelling. To illustrate feasibility of this approach, we present a Markov logic based semantic event model for cyber-physical systems and discuss a case study of event handling and processing in a smart home