Algorithm Selection Framework for Cyber Attack Detection

The number of cyber threats against both wired and wireless computer systems and other components of the Internet of Things continues to increase annually. In this work, an algorithm selection framework is employed on the NSL-KDD data set and a novel paradigm of machine learning taxonomy is presented. The framework uses a combination of user input and meta-features to select the best algorithm to detect cyber attacks on a network. Performance is compared between a rule-of-thumb strategy and a meta-learning strategy. The framework removes the conjecture of the common trial-and-error algorithm selection method. The framework recommends five algorithms from the taxonomy. Both strategies recommend a high-performing algorithm, though not the best performing. The work demonstrates the close connectedness between algorithm selection and the taxonomy for which it is premised.Comment: 6 pages, 7 figures, 1 table, accepted to WiseML '2

RAPID : research on automated plankton identification

Author Posting. © Oceanography Society, 2007. This article is posted here by permission of Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 20, 2 (2007): 172-187.When Victor Hensen deployed the first true plankton1 net in 1887, he and his colleagues were attempting to answer three fundamental questions: What planktonic organisms are present in the ocean? How many of each type are present? How does the plankton’s composition change over time? Although answering these questions has remained a central goal of oceanographers, the sophisticated tools available to enumerate planktonic organisms today offer capabilities that Hensen probably could never have imagined.This material is based upon work supported by the National Science Foundation under Grants OCE-0325018, OCE-0324937, OCE-0325167 and OCE-9423471, and the European Union under grants Q5CR-2002-71699, MAS3-ct98-0188, and MAS2-ct92-0015

Feature Construction for Game Playing 1

Abstract: To build an evaluation function for game-playing, one needs to construct informative features that enable accurate relative assessment of a game state. This chapter describes the feature construction problem, and suggests directions for dealing with shortcomings in the present state of the art.

Incremental Induction of Decision Trees 1

Abstract: This article presents an incremental algorithm for inducing decision trees equivalent to those formed by Quinlan’s nonincremental ID3 algorithm, given the same training instances. The new algorithm, named ID5R, lets one apply the ID3 induction process to learning tasks in which training instances are presented serially. Although the basic tree-building algorithms differ only in how the decision trees are constructed, experiments show that incremental training makes it possible to select training instances more carefully, which can result in smaller decision trees. The ID3 algorithm and its variants are compared in terms of theoretical complexity and empirical behavior

Decision Tree Induction Based on Efficient Tree Restructuring

The ability to restructure a decision tree efficiently enables a variety of approaches to decision tree induction that would otherwise be prohibitively expensive. This report describes two such approaches, one being incremental tree induction, and the other being non-incremental tree induction using a measure of tree quality instead of test quality. The algorithm ITI for incremental tree induction includes several significant advances from its predecessor ID5R, and the algorithm DMTI for employing a direct metric of tree quality is entirely new. 1 Introduction Decision tree induction offers a highly practical method for generalizing from instances whose class membership is known. The most common approach to inducing a decision tree is to partition the labelled instances recursively until a stopping criterion is met. The partition is defined by way of selecting a test that has a manageable set of outcomes, creating a branch for each possible outcome, passing each instance down the cor..

Feature Construction for Game Playing

To build an evaluation function for game-playing, one needs to construct informative features that enable accurate relative assessment of a game state. This chapter describes the feature construction problem, and suggests directions for dealing with shortcomings in the present state of the art

Approximation Via Value Unification 1

Abstract: Numerical function approximation over a Boolean domain is a classical problem with wide application to data modeling tasks and various forms of learning. A great many function approximation algorithms have been devised over the years. Because the goal is to produce an approximating function that has low expected error, algorithms are typically guided by error reduction. This guiding force, to reduce error, can bias the algorithm in a detrimental manner. We illustrate this bias, and then propose an alternative approach based on a notion of value unification.

Many-Layered Learning

We explore incremental assimilation of new knowledge by sequential learning. Of particular interest is how a network of many knowledge layers can be constructed in an on-line manner, such that the learned units represent building blocks of knowledge that serve to compress the overall representation and facilitate transfer. We motivate the need for many layers of knowledge, and we advocate sequential learning as an avenue for promoting construction of layered knowledge structures. Finally, our novel STL algorithm demonstrates an efficient method for simultaneously acquiring and organizing a collection of concepts and functions from a stream of rich but otherwise unstructured information.