Peter Lichodzijewski

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Hierarchical SOMs are applied to the problem of host based intrusion detection on computer networks. Unlike systems based on operating system audit trails, the approach operates on real-time data without extensive off-line training and with minimal expert knowledge. Specific recommendations are made regarding the representation of time, network parameters(More)
Bid-based Genetic Programming (GP) provides an elegant mechanism for facilitating cooperative problem decomposition without an <i>a priori</i> specification of the number of team members. This is in contrast to existing teaming approaches where individuals learn a direct input-output map (e.g., from exemplars to class labels), allowing the approach to scale(More)
The computational overhead of genetic programming (GP) may be directly addressed without recourse to hardware solutions using active learning algorithms based on the random or dynamic subset selection heuristics (RSS or DSS). This correspondence begins by presenting a family of hierarchical DSS algorithms: RSS-DSS, cascaded RSS-DSS, and the balanced block(More)
The 3 × 3 Rubik cube represents a potential benchmark for temporal sequence learning under a discrete application domain with multiple actions. Challenging aspects of the problem domain include the large state space and a requirement to learn invariances relative to the specific colours present. The latter element of the domain making it difficult to evolve(More)
A bid-based approach for coevolving Genetic Programming classifiers is presented. The approach coevolves a population of learners thatdecompose the instance space by way of their aggregate bidding behaviour. To reduce computation overhead, a small, relevant, subsetof training exemplars is (competitively) coevolved alongside the learners. The approach solves(More)
In this work a cooperative, bid-based, model for problem decomposition is proposed with application to discrete action domains such as classification. This represents a significant departure from models where each individual constructs a direct input-outcome map, for example, from the set of exemplars to the set of class labels as is typical under the(More)
The classification of Encrypted Traffic, namely Secure Shell (SSH), on the fly from network TCP traffic represents a particularly challenging application domain for machine learning. Solutions should ideally be both simple - therefore efficient to deploy - and accurate. Recent advances to teambased Genetic Programming provide the opportunity to decompose(More)
Adopting a symbiotic model of evolution separates context for deploying an action from the action itself. Such a separation provides a mechanism for task decomposition in temporal sequence learning. Moreover, previously learned policies are taken to be synonymous with meta actions (actions that are themselves policies). Should solutions to the task not be(More)