Machine learning-based gait anomaly detection using a sensorized tip: an individualized approach

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...

Behaviour Based Anomaly Detection for Smartphones Using Machine Learning Algorithm

Since the first handheld cellular phone was introduced in 1973, the mobile phones have evolved into immensely popular smartphones. These devices provide all-in-one expediency by integrating traditional mobile phones with handheld computing devices making them more open and general purpose. Smartphones have become hosts for sensitive or personal data and applications. However many smartphones ar...

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 hybrid machine learning approach to network anomaly detection

Zero-day cyber attacks such as worms and spy-ware are becoming increasingly widespread and dangerous. The existing signature-based intrusion detection mechanisms are often not sufficient in detecting these types of attacks. As a result, anomaly intrusion detection methods have been developed to cope with such attacks. Among the variety of anomaly detection approaches, the Support Vector Machine...

Anomaly detection with Machine learning