A Study on Hierarchical Model of a Computer Worm Defense System

This research addresses the problem of computer worms in the modern Internet. A worm is similar to a virus. A worm is a self-propagating computer program that is being increasingly and widely used to attack the Internet. It is considered as a sub-class of a virus because it is also capable of spreading from one computer to another. Worms are also computer programs that are capable of replicating copies of themselves via network connections. What makes it different however is that unlike a computer virus a computer worm can run itself without any human intervention? Because of these two qualities of a worm, it is possible that there will be thousands of worms in a computer even if only one computer worm is transferred. For instance, the worm may send a copy of itself to every person listed in the e-mail address book. The worm sent may then send a copy of itself to every person who is listed in the address book of the person who receives the email. Because this may go on ad infinitum worms can not only cause damage to a single computer and to other persons computer but it can only affect the functionality of Web servers and network servers to the point that they can no longer function efficiently. One example is the .blaster worm

Worm Epidemics in Wireless Adhoc Networks

A dramatic increase in the number of computing devices with wireless communication capability has resulted in the emergence of a new class of computer worms which specifically target such devices. The most striking feature of these worms is that they do not require Internet connectivity for their propagation but can spread directly from device to device using a short-range radio communication technology, such as WiFi or Bluetooth. In this paper, we develop a new model for epidemic spreading of these worms and investigate their spreading in wireless ad hoc networks via extensive Monte Carlo simulations. Our studies show that the threshold behaviour and dynamics of worm epidemics in these networks are greatly affected by a combination of spatial and temporal correlations which characterize these networks, and are significantly different from the previously studied epidemics in the Internet

Celeganser: Automated Analysis of Nematode Morphology and Age

The nematode Caenorhabditis elegans (C. elegans) serves as an important model organism in a wide variety of biological studies. In this paper we introduce a pipeline for automated analysis of C. elegans imagery for the purpose of studying life-span, health-span and the underlying genetic determinants of aging. Our system detects and segments the worm, and predicts body coordinates at each pixel location inside the worm. These coordinates provide dense correspondence across individual animals to allow for meaningful comparative analysis. We show that a model pre-trained to perform body-coordinate regression extracts rich features that can be used to predict the age of individual worms with high accuracy. This lays the ground for future research in quantifying the relation between organs' physiologic and biochemical state, and individual life/health-span.Comment: Computer Vision for Microscopy Image Analysis (CVMI) 202

Geometry-based Detection of Flash Worms

While it takes traditional internet worms hours to infect all the vulnerable hosts on the Internet, a flash worm takes seconds. Because of the rapid rate with which flash worms spread, the existing worm defense mechanisms cannot respond fast enough to detect and stop the flash worm infections. In this project, we propose a geometric-based detection mechanism that can detect the spread of flash worms in a short period of time. We tested the mechanism on various simulated flash worm traffics consisting of more than 10,000 nodes. In addition to testing on flash worm traffics, we also tested the mechanism on non-flash worm traffics to see if our detection mechanism produces false alarms. In order to efficiently analyze bulks of various network traffics, we implemented an application that can be used to convert the network traffic data into graphical notations. Using the application, the analysis can be done graphically as it displays the large amount of network relationships as tree structures