The Squeaky wheel: Machine learning for anomaly detection in subjective thermal comfort votes

Improved Squeaky Wheel Optimisation for Driver Scheduling

This paper presents a technique called Improved Squeaky Wheel Optimisation (ISWO) for driver scheduling problems. It improves the original Squeaky Wheel Optimisation’s (SWO) effectiveness and execution speed by incorporating two additional steps of Selection and Mutation which implement evolution within a single solution. In the ISWO, a cycle of Analysis-SelectionMutation-Prioritization-Constru...

"Squeaky Wheel" Optimization

We describe a general approach to optimization which we term "Squeaky Wheel" Optimization (swo). swo, a greedy algorithm is used to construct a solution which is then analyzed to find the trouble spots, i.e., those elements, that, if improved, axe likely to improve the objective function score. That analysis is used to generate new priorities that determine the order in which the greedy algorit...

Anomaly detection with Machine learning

Machine Learning for Host-based Anomaly Detection

Machine Learning for Host-based Anomaly Detection by Gaurav Tandon Dissertation Advisor: Philip K. Chan, Ph.D. Anomaly detection techniques complement signature based methods for intrusion detection. Machine learning approaches are applied to anomaly detection for automated learning and detection. Traditional host-based anomaly detectors model system call sequences to detect novel attacks. This...

A Machine Learning Approach to Anomaly Detection

Much of the intrusion detection research focuses on signature (misuse) detection, where models are built to recognize known attacks. However, signature detection, by its nature, cannot detect novel attacks. Anomaly detection focuses on modeling the normal behavior and identifying significant deviations, which could be novel attacks. In this paper we explore two machine learning methods that can...