Our class project for the 16–721 Advanced Perception was an entry in the PASCAL Visual Object Classes Challenge 2006. The goal of this challenge is to determine whether an object from one of ten classes appears in a given image. Labelled training data was provided, and participants were free to use any method. We chose to implement a bag–of–words classifier using color, shape, and texture infor...

Recently machine learning based intrusion detection system developments have been subjected to extensive researches because they can detect both misuse detection and anomaly detection. In this paper, we propose an AdaBoost based algorithm for network intrusion detection system with single weak classifier. In this algorithm, the classifiers such as Bayes Net, Naïve Bayes and Decision tree are us...

This paper presents a unified Steganalyzer that can work with different media types such as images and audios. It is also capable of providing improved accuracy in stego detection through the use of multiple algorithms. The designed system integrates different steganalysis techniques in a reliable Steganalyzer by using a Services Oriented Architecture (SOA). Other contributions of the research ...

Network Intrusion Detection aims at distinguishing the behavior of the network. It is an inseparable part of the information security system. Due to rapid development of attack pattern it is necessary to develop a system which can upgrade itself as new threats are detected. Also detection rate should be high because the rate with which attack is carried out on the network is very high. In respo...

The Decision Tree algorithm is a data mining method that often applied as solution to problem for classification. C5.0 has several weaknesses, including: the and other decision tree methods are biased towards modeling whose features have many levels, some problems model can occur such over-fit or under-fit challenges, big changes logic result in small training, experience inconvenience, imbalan...

In a previous publication we proposed discrete global optimization as a method to train a strong binary classifier constructed as a thresholded sum over weak classifiers. Our motivation was to cast the training of a classifier into a format amenable to solution by the quantum adiabatic algorithm. Applying adiabatic quantum computing (AQC) promises to yield solutions that are superior to those w...