ANANAS - A Framework For Analyzing Android Applications

Android is an open software platform for mobile devices with a large market share in the smartphone sector. The openness of the system as well as its wide adoption lead to an increasing amount of malware developed for this platform. ANANAS is an expandable and modular framework for analyzing Android applications. It takes care of common needs for dynamic malware analysis and provides an interface for the development of plugins. Adaptability and expandability have been main design goals during the development process. An abstraction layer for simple user interaction and phone event simulation is also part of the framework. It allows an analyst to script the required user simulation or phone events on demand or adjust the simulation to his needs. Six plugins have been developed for ANANAS. They represent well known techniques for malware analysis, such as system call hooking and network traffic analysis. The focus clearly lies on dynamic analysis, as five of the six plugins are dynamic analysis methods.Comment: Paper accepted at First Int. Workshop on Emerging Cyberthreats and Countermeasures ECTCM 201

Learning from AI : new trends in database technology

Recently some researchers in the areas of database data modelling and knowledge representations in artificial intelligence have recognized that they share many common goals. In this survey paper we show the relationship between database and artificial intelligence research. We show that there has been a tendency for data models to incorporate more modelling techniques developed for knowledge representations in artificial intelligence as the desire to incorporate more application oriented semantics, user friendliness, and flexibility has increased. Increasing the semantics of the representation is the key to capturing the "reality" of the database environment, increasing user friendliness, and facilitating the support of multiple, possibly conflicting, user views of the information contained in a database

The Use of Ontologies in Contextually Aware Environments

In this paper we outline work in progress related to the construction of contextually aware pervasive computing environments, through the use of semantic and knowledge technologies. Key to this activity is modelling both where and what a user is doing at any given time. We present a prototype application to illustrate this work and describe part of its implementation

Towards End-to-End Acoustic Localization using Deep Learning: from Audio Signal to Source Position Coordinates

This paper presents a novel approach for indoor acoustic source localization using microphone arrays and based on a Convolutional Neural Network (CNN). The proposed solution is, to the best of our knowledge, the first published work in which the CNN is designed to directly estimate the three dimensional position of an acoustic source, using the raw audio signal as the input information avoiding the use of hand crafted audio features. Given the limited amount of available localization data, we propose in this paper a training strategy based on two steps. We first train our network using semi-synthetic data, generated from close talk speech recordings, and where we simulate the time delays and distortion suffered in the signal that propagates from the source to the array of microphones. We then fine tune this network using a small amount of real data. Our experimental results show that this strategy is able to produce networks that significantly improve existing localization methods based on \textit{SRP-PHAT} strategies. In addition, our experiments show that our CNN method exhibits better resistance against varying gender of the speaker and different window sizes compared with the other methods.Comment: 18 pages, 3 figures, 8 table

Comprehensive rotorcraft analysis methods

The development and application of comprehensive rotorcraft analysis methods in the field of rotorcraft technology are described. These large scale analyses and the resulting computer programs are intended to treat the complex aeromechanical phenomena that describe the behavior of rotorcraft. They may be used to predict rotor aerodynamics, acoustic, performance, stability and control, handling qualities, loads and vibrations, structures, dynamics, and aeroelastic stability characteristics for a variety of applications including research, preliminary and detail design, and evaluation and treatment of field problems. The principal comprehensive methods developed or under development in recent years and generally available to the rotorcraft community because of US Army Aviation Research and Technology Activity (ARTA) sponsorship of all or part of the software systems are the Rotorcraft Flight Simulation (C81), Dynamic System Coupler (DYSCO), Coupled Rotor/Airframe Vibration Analysis Program (SIMVIB), Comprehensive Analytical Model of Rotorcraft Aerodynamics and Dynamics (CAMRAD), General Rotorcraft Aeromechanical Stability Program (GRASP), and Second Generation Comprehensive Helicopter Analysis System (2GCHAS)

Techniques for the dynamic randomization of network attributes

Critical infrastructure control systems continue to foster predictable communication paths and static configurations that allow easy access to our networked critical infrastructure around the world. This makes them attractive and easy targets for cyber-attack. We have developed technologies that address these attack vectors by automatically reconfiguring network settings. Applying these protective measures will convert control systems into «moving targets» that proactively defend themselves against attack. This «Moving Target Defense» (MTD) revolves about the movement of network reconfiguration, securely communicating reconfiguration specifications to other network nodes as required, and ensuring that connectivity between nodes is uninterrupted. Software-defined Networking (SDN) is leveraged to meet many of these goals. Our MTD approach eliminates adversaries targeting known static attributes of network devices and systems, and consists of the following three techniques: (1) Network Randomization for TCP/UDP Ports; (2) Network Randomization for IP Addresses; (3) Network Randomization for Network Paths In this paper, we describe the implementation of the aforementioned technologies. We also discuss the individual and collective successes for the techniques, challenges for deployment, constraints and assumptions, and the performance implications for each technique