Towards an Experimental Testbed to Study Cyber Worm Behaviors in Large Scale Networks

A worm is a malicious agent that propagates across networks of devices creating negative impacts on the devices it is able to reach and infect. Currently, there is very limited information in cybersecurity research regarding worm behavior across real networks of devices, particularly in large scale networks (e.g. campus networks, office networks, IoT etc.). This paper positions an experimental testbed that can be used for studying worm behaviors in large scale networks. In particular, this research aims to setup an infrastructure to empirically study worm generation, propagation, attacks, policies and antidote (intervention) mechanisms through a unified experimental testbed. As a preliminary step towards this goal, this paper presents a case study of an empirical study of the behavior of a worm that attacks through IP address routing in a campus network. Through a 10 node set up where Raspberry Pis are used to emulate a user device in the campus network, we show how a simple worm that uses an exhaustive sequential and/or random selection of IP can lead to infecting devices in ways which can be challenging to track in reality. We also infer that through extensive experimentation it could be possible to develop prediction models for the attack patterns, based on the behavior patterns observed in the experiments

Illicit Activity Detection in Large-Scale Dark and Opaque Web Social Networks

Many online chat applications live in a grey area between the legitimate web and the dark net. The Telegram network in particular can aid criminal activities. Telegram hosts “chats” which consist of varied conversations and advertisements. These chats take place among automated “bots” and human users. Classifying legitimate activity from illegitimate activity can aid law enforcement in finding criminals. Social network analysis of Telegram chats presents a difficult problem. Users can change their username or create new accounts. Users involved in criminal activity often do this to obscure their identity. This makes establishing the unique identity behind a given username challenging. Thus we explored classifying users from their language usage in their chat messages.The volume and velocity of Telegram chat data place it well within the domain of big data. Machine learning and natural language processing (NLP) tools are necessary to classify this chat data. We developed NLP tools for classifying users and the chat group to which their messages belong. We found that legitimate and illegitimate chat groups could be classified with high accuracy. We also were able to classify bots, humans, and advertisements within conversations

Structural Principles in Robo Activation and Auto-Inhibition

This is the author accepted manuscript. The final version is available from Elsevier (Cell Press) via the DOI in this record.Proper brain function requires high-precision neuronal expansion and wiring, processes controlled by the transmembrane Roundabout (Robo) receptor family and their Slit ligands. Despite their great importance, the molecular mechanism by which Robos’ switch from “off” to “on” states remains unclear. Here, we report a 3.6 Å crystal structure of the intact human Robo2 ectodomain (domains D1–8). We demonstrate that Robo cis dimerization via D4 is conserved through hRobo1, 2, and 3 and the C. elegans homolog SAX-3 and is essential for SAX-3 function in vivo. The structure reveals two levels of auto-inhibition that prevent premature activation: (1) cis blocking of the D4 dimerization interface and (2) trans interactions between opposing Robo receptors that fasten the D4-blocked conformation. Complementary experiments in mouse primary neurons and C. elegans support the auto-inhibition model. These results suggest that Slit stimulation primarily drives the release of Robo auto-inhibition required for dimerization and activation.ICRFIS

Models of plastic depinning of driven disordered systems

Two classes of models of driven disordered systems that exhibit history-dependent dynamics are discussed. The first class incorporates local inertia in the dynamics via nonmonotonic stress transfer between adjacent degrees of freedom. The second class allows for proliferation of topological defects due to the interplay of strong disorder and drive. In mean field theory both models exhibit a tricritical point as a function of disorder strength. At weak disorder depinning is continuous and the sliding state is unique. At strong disorder depinning is discontinuous and hysteretic.Comment: 3 figures, invited talk at StatPhys 2

Motmot, an open-source toolkit for realtime video acquisition and analysis

<p>Abstract</p> <p>Background</p> <p>Video cameras sense passively from a distance, offer a rich information stream, and provide intuitively meaningful raw data. Camera-based imaging has thus proven critical for many advances in neuroscience and biology, with applications ranging from cellular imaging of fluorescent dyes to tracking of whole-animal behavior at ecologically relevant spatial scales.</p> <p>Results</p> <p>Here we present 'Motmot': an open-source software suite for acquiring, displaying, saving, and analyzing digital video in real-time. At the highest level, Motmot is written in the Python computer language. The large amounts of data produced by digital cameras are handled by low-level, optimized functions, usually written in C. This high-level/low-level partitioning and use of select external libraries allow Motmot, with only modest complexity, to perform well as a core technology for many high-performance imaging tasks. In its current form, Motmot allows for: (1) image acquisition from a variety of camera interfaces (package motmot.cam_iface), (2) the display of these images with minimal latency and computer resources using wxPython and OpenGL (package motmot.wxglvideo), (3) saving images with no compression in a single-pass, low-CPU-use format (package motmot.FlyMovieFormat), (4) a pluggable framework for custom analysis of images in realtime and (5) firmware for an inexpensive USB device to synchronize image acquisition across multiple cameras, with analog input, or with other hardware devices (package motmot.fview_ext_trig). These capabilities are brought together in a graphical user interface, called 'FView', allowing an end user to easily view and save digital video without writing any code. One plugin for FView, 'FlyTrax', which tracks the movement of fruit flies in real-time, is included with Motmot, and is described to illustrate the capabilities of FView.</p> <p>Conclusion</p> <p>Motmot enables realtime image processing and display using the Python computer language. In addition to the provided complete applications, the architecture allows the user to write relatively simple plugins, which can accomplish a variety of computer vision tasks and be integrated within larger software systems. The software is available at <url>http://code.astraw.com/projects/motmot</url></p

Intrinsic activity in the fly brain gates visual information during behavioral choices

The small insect brain is often described as an input/output system that executes reflex-like behaviors. It can also initiate neural activity and behaviors intrinsically, seen as spontaneous behaviors, different arousal states and sleep. However, less is known about how intrinsic activity in neural circuits affects sensory information processing in the insect brain and variability in behavior. Here, by simultaneously monitoring Drosophila's behavioral choices and brain activity in a flight simulator system, we identify intrinsic activity that is associated with the act of selecting between visual stimuli. We recorded neural output (multiunit action potentials and local field potentials) in the left and right optic lobes of a tethered flying Drosophila, while its attempts to follow visual motion (yaw torque) were measured by a torque meter. We show that when facing competing motion stimuli on its left and right, Drosophila typically generate large torque responses that flip from side to side. The delayed onset (0.1-1 s) and spontaneous switch-like dynamics of these responses, and the fact that the flies sometimes oppose the stimuli by flying straight, make this behavior different from the classic steering reflexes. Drosophila, thus, seem to choose one stimulus at a time and attempt to rotate toward its direction. With this behavior, the neural output of the optic lobes alternates; being augmented on the side chosen for body rotation and suppressed on the opposite side, even though the visual input to the fly eyes stays the same. Thus, the flow of information from the fly eyes is gated intrinsically. Such modulation can be noise-induced or intentional; with one possibility being that the fly brain highlights chosen information while ignoring the irrelevant, similar to what we know to occur in higher animals

Depinning and plasticity of driven disordered lattices

We review in these notes the dynamics of extended condensed matter systesm, such as vortex lattices in type-II superconductors and charge density waves in anisotropic metals, driven over quenched disorder. We focus in particular on the case of strong disorder, where topological defects are generated in the driven lattice. In this case the repsonse is plastic and the depinning transition may become discontinuous and hysteretic.Comment: 21 pages, 6 figures. Proceedings the XIX Sitges Conference on Jamming, Yielding, and Irreversible Deformations in Condensed Matter, Sitges, Barcelona, Spain, June 14-18, 200

Relating Neuronal to Behavioral Performance: Variability of Optomotor Responses in the Blowfly

Behavioral responses of an animal vary even when they are elicited by the same stimulus. This variability is due to stochastic processes within the nervous system and to the changing internal states of the animal. To what extent does the variability of neuronal responses account for the overall variability at the behavioral level? To address this question we evaluate the neuronal variability at the output stage of the blowfly's (Calliphora vicina) visual system by recording from motion-sensitive interneurons mediating head optomotor responses. By means of a simple modelling approach representing the sensory-motor transformation, we predict head movements on the basis of the recorded responses of motion-sensitive neurons and compare the variability of the predicted head movements with that of the observed ones. Large gain changes of optomotor head movements have previously been shown to go along with changes in the animals' activity state. Our modelling approach substantiates that these gain changes are imposed downstream of the motion-sensitive neurons of the visual system. Moreover, since predicted head movements are clearly more reliable than those actually observed, we conclude that substantial variability is introduced downstream of the visual system