73,373 research outputs found
Machine Learning DDoS Detection for Consumer Internet of Things Devices
An increasing number of Internet of Things (IoT) devices are connecting to
the Internet, yet many of these devices are fundamentally insecure, exposing
the Internet to a variety of attacks. Botnets such as Mirai have used insecure
consumer IoT devices to conduct distributed denial of service (DDoS) attacks on
critical Internet infrastructure. This motivates the development of new
techniques to automatically detect consumer IoT attack traffic. In this paper,
we demonstrate that using IoT-specific network behaviors (e.g. limited number
of endpoints and regular time intervals between packets) to inform feature
selection can result in high accuracy DDoS detection in IoT network traffic
with a variety of machine learning algorithms, including neural networks. These
results indicate that home gateway routers or other network middleboxes could
automatically detect local IoT device sources of DDoS attacks using low-cost
machine learning algorithms and traffic data that is flow-based and
protocol-agnostic.Comment: 7 pages, 3 figures, 3 tables, appears in the 2018 Workshop on Deep
Learning and Security (DLS '18
A Forensically Sound Adversary Model for Mobile Devices
In this paper, we propose an adversary model to facilitate forensic
investigations of mobile devices (e.g. Android, iOS and Windows smartphones)
that can be readily adapted to the latest mobile device technologies. This is
essential given the ongoing and rapidly changing nature of mobile device
technologies. An integral principle and significant constraint upon forensic
practitioners is that of forensic soundness. Our adversary model specifically
considers and integrates the constraints of forensic soundness on the
adversary, in our case, a forensic practitioner. One construction of the
adversary model is an evidence collection and analysis methodology for Android
devices. Using the methodology with six popular cloud apps, we were successful
in extracting various information of forensic interest in both the external and
internal storage of the mobile device
Implicit Sensor-based Authentication of Smartphone Users with Smartwatch
Smartphones are now frequently used by end-users as the portals to
cloud-based services, and smartphones are easily stolen or co-opted by an
attacker. Beyond the initial log-in mechanism, it is highly desirable to
re-authenticate end-users who are continuing to access security-critical
services and data, whether in the cloud or in the smartphone. But attackers who
have gained access to a logged-in smartphone have no incentive to
re-authenticate, so this must be done in an automatic, non-bypassable way.
Hence, this paper proposes a novel authentication system, iAuth, for implicit,
continuous authentication of the end-user based on his or her behavioral
characteristics, by leveraging the sensors already ubiquitously built into
smartphones. We design a system that gives accurate authentication using
machine learning and sensor data from multiple mobile devices. Our system can
achieve 92.1% authentication accuracy with negligible system overhead and less
than 2% battery consumption.Comment: Published in Hardware and Architectural Support for Security and
Privacy (HASP), 201
QCDGPU: open-source package for Monte Carlo lattice simulations on OpenCL-compatible multi-GPU systems
The multi-GPU open-source package QCDGPU for lattice Monte Carlo simulations
of pure SU(N) gluodynamics in external magnetic field at finite temperature and
O(N) model is developed. The code is implemented in OpenCL, tested on AMD and
NVIDIA GPUs, AMD and Intel CPUs and may run on other OpenCL-compatible devices.
The package contains minimal external library dependencies and is OS
platform-independent. It is optimized for heterogeneous computing due to the
possibility of dividing the lattice into non-equivalent parts to hide the
difference in performances of the devices used. QCDGPU has client-server part
for distributed simulations. The package is designed to produce lattice gauge
configurations as well as to analyze previously generated ones. QCDGPU may be
executed in fault-tolerant mode. Monte Carlo procedure core is based on PRNGCL
library for pseudo-random numbers generation on OpenCL-compatible devices,
which contains several most popular pseudo-random number generators.Comment: Presented at the Third International Conference "High Performance
Computing" (HPC-UA 2013), Kyiv, Ukraine; 9 pages, 2 figure
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