46 research outputs found
Selective Jamming of LoRaWAN using Commodity Hardware
Long range, low power networks are rapidly gaining acceptance in the Internet
of Things (IoT) due to their ability to economically support long-range sensing
and control applications while providing multi-year battery life. LoRa is a key
example of this new class of network and is being deployed at large scale in
several countries worldwide. As these networks move out of the lab and into the
real world, they expose a large cyber-physical attack surface. Securing these
networks is therefore both critical and urgent. This paper highlights security
issues in LoRa and LoRaWAN that arise due to the choice of a robust but slow
modulation type in the protocol. We exploit these issues to develop a suite of
practical attacks based around selective jamming. These attacks are conducted
and evaluated using commodity hardware. The paper concludes by suggesting a
range of countermeasures that can be used to mitigate the attacks.Comment: Mobiquitous 2017, November 7-10, 2017, Melbourne, VIC, Australi
Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis
An electroencephalography (EEG) based brain activity recognition is a
fundamental field of study for a number of significant applications such as
intention prediction, appliance control, and neurological disease diagnosis in
smart home and smart healthcare domains. Existing techniques mostly focus on
binary brain activity recognition for a single person, which limits their
deployment in wider and complex practical scenarios. Therefore, multi-person
and multi-class brain activity recognition has obtained popularity recently.
Another challenge faced by brain activity recognition is the low recognition
accuracy due to the massive noises and the low signal-to-noise ratio in EEG
signals. Moreover, the feature engineering in EEG processing is time-consuming
and highly re- lies on the expert experience. In this paper, we attempt to
solve the above challenges by proposing an approach which has better EEG
interpretation ability via raw Electroencephalography (EEG) signal analysis for
multi-person and multi-class brain activity recognition. Specifically, we
analyze inter-class and inter-person EEG signal characteristics, based on which
to capture the discrepancy of inter-class EEG data. Then, we adopt an
Autoencoder layer to automatically refine the raw EEG signals by eliminating
various artifacts. We evaluate our approach on both a public and a local EEG
datasets and conduct extensive experiments to explore the effect of several
factors (such as normalization methods, training data size, and Autoencoder
hidden neuron size) on the recognition results. The experimental results show
that our approach achieves a high accuracy comparing to competitive
state-of-the-art methods, indicating its potential in promoting future research
on multi-person EEG recognition.Comment: 10 page
Identifying Recent Behavioral Data Length in Mobile Phone Log
Mobile phone log data (e.g., phone call log) is not static as it is
progressively added to day-by-day according to individ- ual's diverse behaviors
with mobile phones. Since human behavior changes over time, the most recent
pattern is more interesting and significant than older ones for predicting in-
dividual's behavior. The goal of this poster paper is to iden- tify the recent
behavioral data length dynamically from the entire phone log for recency-based
behavior modeling. To the best of our knowledge, this is the first dynamic
recent log-based study that takes into account individual's recent behavioral
patterns for modeling their phone call behaviors.Comment: 14th EAI International Conference on Mobile and Ubiquitous Systems:
Computing, Networking and Services (MobiQuitous 2017), Melbourne, Australi
PROFICIENT: Productivity Tool for Semantic Interoperability in an Open IoT Ecosystem
The Internet of Things (IoT) is promising to open up opportunities for businesses to offer new services to uncover untapped needs. However, before taking advantage of such opportunities, there are still challenges ahead, one of which is the development of strategies to abstract from the heterogeneity of APIs that shape today's IoT. It is becoming increasingly complex for developers and smart connected objects to efficiently discover, parse, aggregate and process data from disparate information systems, as different protocols, data models, and serializations for APIs exist on the market. Standards play an indisputable role in reducing such a complexity, but will not solve all problems related to interoperability. For example, it will remain a permanent need to help and guide data/service providers to efficiently describe the data/services they would like to expose to the IoT. This paper presents PROFICIENT, a productivity tool that fulfills this need, which is showcased and evaluated considering recent open messaging standards and a smart parking scenario
Next2Me: Capturing Social Interactions through Smartphone Devices using WiFi and Audio signals
Typical approaches in detecting social interactions consider the use of co-location as a proxy for real-world interactions. Such approaches can underperform in challenging situations where multiple social interactions can occur in close proximity to each other. In this paper, we present a novel approach to detect co-located social interactions using smartphones. Next2Me relies on the use of WiFi signals and audio signals to accurately distinguish social groups interacting within a few meters from each other. Through a range of real-world experiments, we demonstrate a technique that utilises WiFi fingerprinting, along with sound fingerprinting to identify social groups. Experimental results show that Next2Me can achieve a precision of 88% within noisy environments, including smartphones that are placed in users’ pockets, whilst maintaining a very low energy footprint (<3% of battery capacity per day)
IoT and semantic web technologies for event detection in natural disasters
This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.Natural disasters cannot be predicted well in advance, but it is still possible to decrease the loss of life and mitigate the damages, exploiting some peculiarities that distinguish them. Smart collection, integration, and analysis of data produced by distributed sensors and services are key elements for understanding the context and supporting decision making process for disaster prevention and management. In this paper, we demonstrate how Internet of Things and Semantic Web technologies can be effectively used for abnormal event detection in the contest of an earthquake. In our proposal, a prototype system, which retrieves the data streams from IoT sensors and web services, is presented. In order to contextualize and give a meaning to the data, semantic web technologies are applied for data annotation. We evaluate our system performances by measuring the response time and other parameters that are important in a disaster detection scenario.Peer ReviewedPostprint (author's final draft
Mobile network anomaly detection and mitigation: The NEMESYS approach
Mobile malware and mobile network attacks are becoming a significant threat that accompanies the increasing popularity of smart phones and tablets. Thus in this paper we present our research vision that aims to develop a network-based security solution combining analytical modelling, simulation and learning, together with billing and control-plane data, to detect anomalies and attacks, and eliminate or mitigate their effects, as part of the EU FP7 NEMESYS project. These ideas are supplemented with a careful review of the state-of-the-art regarding anomaly detection techniques that mobile network operators may use to protect their infrastructure and secure users against malware