Christopher W. Geib

Plan recognition in intrusion detection systems

To be effective, current intrusion detection systems (IDSs) must incorporate artificial intelligence methods for plan recognition. Plan recognition is critical both to predicting the future actions of attackers and planning appropriate responses to their actions. However network security places a new set of requirements on plan recognition. We present an argument for including plan recognition in IDSs and an algorithm for conducting plan recognition that meets the needs of the network security domain.

Information modeling for intrusion report aggregation

The paper describes the SCYLLARUS approach to fusing reports from multiple intrusion detection systems (ID-Ses) to provide an overall approach to intrusion situation awareness. The overall view provided by SCYLLARUS centers around the sites security goals, aggregating large numbers of individual IDS reports based on their impact. The overall view reduces information overload by aggregating multiple IDS reports in a rep-down view; and by reducing false positives by weighing evidence provided by multiple ID-Ses and other information sources. Unlike previous efforts in this area, SCYLLARUS is centered around its intrusion reference model (IRM). The SCYLLARUS IRM contains both dynamic and static (configuration) information. A network entity/relationship database (NERD), providing information about the sites hardware and software; a security goal database, describing the sites objectives and security policy; and an event dictionary, describing important events, both intrusions and benign; comprise the static portion of the IRM. The set of IDS reports; the events SCYLLARUS hypothesizes to explain them; and the resulting judgment of the state of site security goals comprise the dynamic part of the IRM.

An open agent architecture for assisting elder independence

We are building an agent-oriented system to aid elderly people to live longer in their homes, increasing the duration of their independence from round-the-clock care while maintaining important social connectedness and reducing caregiver burden. The Independent LifeStyle Assistant

Plan Recognition (Dagstuhl Seminar 11141)

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AAAI-07 Workshop Reports

(I.L.S.A.) is a multiagent system that incorporates a unified sensing model, probabilistically derived situation awareness, hierarchical task network response planning, real-time action selection control, complex coordination, and machine learning. This paper describes the problem, our reasoning for selecting an agent-based approach, and the architecture of the system.

Reports on the Twenty-First National Conference on Artificial Intelligence (AAAI-06) Workshop Program

This Dagstuhl seminar brought together researchers with a wide range of interests and backgrounds related to plan and activity recognition. It featured a substantial set of longer tutorials on aspects of plan and activity recognition, and related topics and useful methods, as a way of establishing a common vocabulary and shared basis of understanding. Building on this shared understanding, individual researchers presented talks about their work in the area. There were also panel discussions which addressed questions about how to best foster progress in the field --- specifically how to improve our ability to compare different plan and activity recognition algorithms --- and address the question of whether to assume rationality in the modeled agents (a question that is of great concern in many fields at this time). This report presents a summary of the talks and discussions at the seminar.

System and method for automated monitoring, recognizing, supporting, and responding to the behavior of an actor

The AAAI-07 workshop program was held Sunday and Monday, July 22-23, in Vancouver, British Columbia, Canada. The program included the following thirteen workshops: (1) Acquiring Planning Knowledge via Demonstration; (2) Configuration; (3) Evaluating Architectures for Intelligence; (4) Evaluation Methods for Machine Learning; (5) Explanation-Aware Computing; (6) Human Implications of Human-Robot Interaction; (7) Intelligent Techniques for Web Personalization; (8) Plan, Activity, and Intent Recognition; (9) Preference Handling for Artificial Intelligence; (10) Semantic e-Science; (11) Spatial and Temporal Reasoning; (12) Trading Agent Design and Analysis; and (13) Information Integration on the Web.

A new model of plan recognition

The Workshop program of the Twenty-First Conference on Artificial Intelligence was held July 16-17, 2006 in Boston, Massachusetts. The program was chaired by Joyce Chai and Keith Decker. The titles of the 17 workshops were AIDriven Technologies for Service-Oriented Computing; Auction Mechanisms for Robot Coordination; Cognitive Modeling and Agent-Based Social Simulations, Cognitive Robotics; Computational Aesthetics: Artificial Intelligence Approaches to Beauty and Happiness; Educational Data Mining; Evaluation Methods for Machine Learning; Event Extraction and Synthesis; Heuristic Search, Memory- Based Heuristics, and Their Applications; Human Implications of Human-Robot Interaction; Intelligent Techniques in Web Personalization; Learning for Search; Modeling and Retrieval of Context; Modeling Others from Observations; and Statistical and Empirical Approaches for Spoken Dialogue Systems.