Getting Started Computing at the AI Lab

This document describes the computing facilities at M.I.T. Artificial Intelligence Laboratory, and explains how to get started using them. It is intended as an orientation document for newcomers to the lab, and will be updated by the author from time to time.MIT Artificial Intelligence Laborator

Operating the Lisp Machine

This document is a draft copy of a portion of the Lisp Machine window system manual. It is being published in this form now to make it available, since the complete window system manual is unlikely to be finished in the near future. The information in this document is accurate as of system 67, but is not guaranteed to remain 100% accurate. This document explains how to use the Lisp Machine from a non-programmer's point of view. It explains the general characteristics of the user interface, particularly the window system and the program-control commands. This document is intended to tell you everything you need to know to sit down at a Lisp machine and run programs, but does not deal with the writing of programs. Many arcane commands and user-interface features are also documented herein, although the beginning user can safely ignore them.MIT Artificial Intelligence Laborator

A Network Compatible Display Protocol

At present IIASA offers to its scientific community a set of basic, but limited computer graphics capabilities. Recent computer-program development in the graphics area has created greater demand and more awareness of the possibilities of computer graphics as a visualization tool in modeling. Some other application fields, like the use of graphics as a tool for program development, were still not touched at IIASA. In response to this demand, IIASA decided to order additional graphics hardware (display terminals and graphics compatible printers) and to invest a certain amount of resources into the generation of appropriate software. It is not the aim of this paper to discuss the benefits of graphics, but rather to identify the general graphics software requirements for IIASA and to propose a possible way of meeting them

Use of Entropy for Feature Selection with Intrusion Detection System Parameters

The metric of entropy provides a measure about the randomness of data and a measure of information gained by comparing different attributes. Intrusion detection systems can collect very large amounts of data, which are not necessarily manageable by manual means. Collected intrusion detection data often contains redundant, duplicate, and irrelevant entries, which makes analysis computationally intensive likely leading to unreliable results. Reducing the data to what is relevant and pertinent to the analysis requires the use of data mining techniques and statistics. Identifying patterns in the data is part of analysis for intrusion detections in which the patterns are categorized as normal or anomalous. Anomalous data needs to be further characterized to determine if representative attacks to the network are in progress. Often time subtleties in the data may be too muted to identify certain types of attacks. Many statistics including entropy are used in a number of analysis techniques for identifying attacks, but these analyzes can be improved upon. This research expands the use of Approximate entropy and Sample entropy for feature selection and attack analysis to identify specific types of subtle attacks to network systems. Through enhanced analysis techniques using entropy, the granularity of feature selection and attack identification is improved

Modelo basado en las técnicas de minería de datos aplicada a la detección de ataques en las redes de datos de la Facultad de Informática y Electrónica

Se desarrolló un modelo de detección de intrusos basado en técnicas de minería de datos para detección de ataques en redes de datos en la Facultad de Informática y Electrónica (FIE) de la Escuela Superior Politécnica de Chimborazo. La investigación se realizó mediante método inductivo, aplicando la observación como técnica para analizar el funcionamiento del modelo desarrollado. Se utilizó herramienta de minería de datos weka y el motor detector de intrusos Suricata. Para validar el modelo se contó con un puerto spam y un puerto de administración, que fueron configurados en el switch principal para monitorear el tráfico de red en la facultad, sirviéndonos de una computadora como servidor IDS. El modelo consiste en aplicar técnicas de minería de datos a un conjunto de datos previamente preparados para extraer conocimiento y generar reglas de detección de intrusos, las mismas que serán cargadas en la base de datos del IDS el cual analiza el tráfico de la red de datos y genera las alertas. De la implantación del modelo se obtuvo como resultado que el 86.10% de ataques fueron detectados en menos de 0.008 segundos que es equivalente a muy bueno generando alertas para la correcta administración y seguridad de la red de datos. Se concluye que el modelo desarrollado permite la detección oportuna de los ataques a una red de datos. Se recomienda a los investigadores que para implementar el modelo de detección de intrusos se cuente con equipos de gran capacidad de almacenamiento y procesamiento, de acuerdo a la cantidad de datos a ser monitoreados

Applications of Deep Neural Networks

Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN), and reinforcement learning. Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High-Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction to mathematical foundations. Readers will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this book; however, familiarity with at least one programming language is assumed