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

SCMAS: A distributed hierarchical multi-agent architecture for blocking attacks to databases

One of the main attacks on databases is the SQL injection attack which causes severe damage both in the commercial aspect and the confidence of users. This paper presents a novel strategy for detecting and preventing SQL injection attacks consisting of a multi-agent based architecture called SCMAS. The SCMAS architecture is structured in hierarchical layers and incorporates SQLCBR agents with improved learning and adaptation capabilities. The SQLCBR agents presented within this paper have been specifically designed to classify SQL injection attacks and to predict the behaviour of malicious users. These agents incorporate a new technique based on a mixture of neural networks and a technique based on a temporal series. This paper begins with a detailed explanation of the SCMAS architecture and the SQLCBR agents. The results of their application to a case study are then presented and discussed.One of the main attacks on databases is the SQL injection attack which causes severe damage both in the commercial aspect and the confidence of users. This paper presents a novel strategy for detecting and preventing SQL injection attacks consisting of a multi-agent based architecture called SCMAS. The SCMAS architecture is structured in hierarchical layers and incorporates SQLCBR agents with improved learning and adaptation capabilities. The SQLCBR agents presented within this paper have been specifically designed to classify SQL injection attacks and to predict the behaviour of malicious users. These agents incorporate a new technique based on a mixture of neural networks and a technique based on a temporal series. This paper begins with a detailed explanation of the SCMAS architecture and the SQLCBR agents. The results of their application to a case study are then presented and discussed

A CBR System: The Core of an Ambient Intelligence Health Care Application

This paper presents a case-based reasoning system developed to generate an efficient and proactive ambient intelligent application. Ambient Intelligence (AmI) proposes a new way to interact between people and technology, where this last one is adapted to individuals and their context (Friedewald and Da Costa 2003). The objective of Ambient Intelligence is to develop intelligent and intuitive systems and interfaces capable to recognize and respond to the user’s necessities in a ubiquitous way, providing capabilities for ubiquitous computation and communication, considering people in the centre of the development, and creating technologically complex environments in medical, domestic, academic, etc. fields (Susperregui et al. 2004). Ambient Intelligence requires new ways for developing intelligent and intuitive systems and interfaces, capable to recognize and respond to the user’s necessities in a ubiquitous way, providing capabilities for ubiquitous computation and communication. The multi-agent systems (Wooldridge and Jennings 1995) have become increasingly relevant for developing distributed and dynamic intelligent environments. A case-based reasoning system (Aamodt and Plaza 1994) has been embedded within a deliberative agent and allows it to respond to events, to take the initiative according to its goals, to communicate with other agents, to interact with users, and to make use of past experiences to find the best plans to achieve goals. The deliberative agent works with the concepts of Belief, Desire, Intention (BDI) (Bratman 1987), and has learning and adaptation capabilities, which facilitates its work in dynamic environment

Integrating case-based planning and RPTW neural networks to construct an intelligent environment for health care

This paper presents an intelligent environment developed for monitoring patients’ health care in execution time in hospital environments. The CBPMP (case-based planner for monitoring patients) is an autonomous deliberative case-based planner designed to plan the nurses’ working time dynamically, to maintain the standard working reports about the nurses’ activities, and to guarantee that the patients assigned to the nurses are given the right care. The planner operates in wireless devices and is integrated with complementary software into an intelligent environment, named AmI-P (Ambient Intelligence for patients). CBPMP description, its relationship with the complementary technology, and preliminary results of system prototype in a real environment are presented

TaskCBP: an intelligent agent for task planning in elderly care

This paper presents an autonomous intelligent agent developed for healthcare in geriatric residences. The paper focuses on the role of ambient intelligence in the automation of healthcare services. The work here presented shows the development of an autonomous agent, TaskCBP, which incorporates a model of human thinking, such as reasoning based on past experiences. The planning mechanism integrated within the agent has been implemented by means of a novel QSOR neural network. The system has been tested and this paper presents the results obtaine

Ambient Intelligence Based Architecture for Automated Dynamic Environments.

This paper presents an Ambient Intelligence based architecture that uses intelligent agents with reasoning and planning mechanisms. The agents have the ability to obtain automatic and real time information about the context using a set of technologies, such as radio frequency identification, wireless networks and wireless control devices. The architecture can be implemented on a wide diversity of dynamic environments. A case study is presented, describing the main characteristics of the planning mechanism supported by the architecture, implemented in a geriatric residence, giving the agents a high level of autonomy and flexibility for solving problems

Replanning Mechanism for Deliberate Agents in Dynamic Changing Environments

This paper proposes a replanning mechanism for deliberative agents as a new approach to tackling the frame problem. We propose a beliefs desires and intentions (BDI) agent architecture using a case-based planning (CBP) mechanism for reasoning. We discuss the characteristics of the problems faced with planning where constraint satisfaction problems (CSP) resources are limited and formulate, through variation techniques, a reasoning model agent to resolve them. The design of the agent proposed, named MRP-Ag (most-replanable agent), will be evaluated in different environments using a series of simulation experiments, comparing it with others such as E-Ag (Efficient Agent) and O-Ag (Optimum Agent). Last, the most important results will be summarized, and the notion of an adaptable agent will be introduced

Autonomous FYDPS Neural Network-Based Planner Agent for Health Care in Geriatric Residences

This paper presents an autonomous intelligent agent developed for health care in geriatric residences. The paper focuses on the construction of an autonomous agent which incorporates a model of human thinking, such as reasoning based on past experiences. The work here presented focuses in the development of the CBP internal structure. The planning mechanism has been implemented by means of a novel FYDPS neural network. The system has been tested and this paper presents the results obtained

SMas: A Shopping Mall Multiagent Systems

This paper presents a multiagent model that facilitates aspects of shopping mall management, as well as increasing the quality of leisure facilities and shopping on offer. The work presented focuses on the use of a multi agent architecture, based on the use of deliberative agents that incorporates case-based planning. The architecture considers a dynamic framework, and the need to use autonomous models that are able to evolve over time. The architecture incorporates agents whose aim is to acquire knowledge and adapt themselves to the environmental changes. The system has been tested successfully, and the results obtained are presented in this paper