133,882 research outputs found
Applying advanced machine learning models to classify electro-physiological activity of human brain for use in biometric identification
In this article we present the results of our research related to the study
of correlations between specific visual stimulation and the elicited brain's
electro-physiological response collected by EEG sensors from a group of
participants. We will look at how the various characteristics of visual
stimulation affect the measured electro-physiological response of the brain and
describe the optimal parameters found that elicit a steady-state visually
evoked potential (SSVEP) in certain parts of the cerebral cortex where it can
be reliably perceived by the electrode of the EEG device. After that, we
continue with a description of the advanced machine learning pipeline model
that can perform confident classification of the collected EEG data in order to
(a) reliably distinguish signal from noise (about 85% validation score) and (b)
reliably distinguish between EEG records collected from different human
participants (about 80% validation score). Finally, we demonstrate that the
proposed method works reliably even with an inexpensive (less than $100)
consumer-grade EEG sensing device and with participants who do not have
previous experience with EEG technology (EEG illiterate). All this in
combination opens up broad prospects for the development of new types of
consumer devices, [e.g.] based on virtual reality helmets or augmented reality
glasses where EEG sensor can be easily integrated. The proposed method can be
used to improve an online user experience by providing [e.g.] password-less
user identification for VR / AR applications. It can also find a more advanced
application in intensive care units where collected EEG data can be used to
classify the level of conscious awareness of patients during anesthesia or to
automatically detect hardware failures by classifying the input signal as
noise
Acoustic and Device Feature Fusion for Load Recognition
Appliance-specific Load Monitoring (LM) provides a possible solution to the problem of energy conservation which is becoming increasingly challenging, due to growing energy demands within offices and residential spaces. It is essential to perform automatic appliance recognition and monitoring for optimal resource utilization. In this paper, we study the use of non-intrusive LM methods that rely on steady-state appliance signatures for classifying most commonly used office appliances, while demonstrating their limitation in terms of accurately discerning the low-power devices due to overlapping load signatures. We propose a multilayer decision architecture that makes use of audio features derived from device sounds and fuse it with load signatures acquired from energy meter. For the recognition of device sounds, we perform feature set selection by evaluating the combination of time-domain and FFT-based audio features on the state of the art machine learning algorithms. The highest recognition performance however is shown by support vector machines, for the device and audio recognition experiments. Further, we demonstrate that our proposed feature set which is a concatenation of device audio feature and load signature significantly improves the device recognition accuracy in comparison to the use of steady-state load signatures only
The Potential for cross-drive analysis using automated digital forensic timelines
Cross-Drive Analysis (CDA) is a technique designed to allow an investigator to “simultaneously consider information from across a corpus of many data sources”. Existing approaches include multi-drive correlation using text searching, e.g. email addresses, message IDs, credit card numbers or social security numbers. Such techniques have the potential to identify drives of interest from a large set, provide additional information about events that occurred on a single disk, and potentially determine social network membership. Another analysis technique that has significantly advanced in recent years is the use of timelines. Tools currently exist that can extract dates and times from the file system metadata (i.e. MACE times) and also examine the content of certain file types and extract metadata from within. This approach provides a great deal of data that can assist with an investigation, but also compounds the problem of having too much data to examine. A recent paper adds an additional timeline analysis capability, by automatically producing a high-level summary of the activity on a computer system, by combining sets of low-level events into high-level events, for example reducing a setupapi event and several events from the Windows Registry to a single event of ‘a USB stick was connected’. This paper provides an investigation into the extent to which events in such a high-level timeline have the properties suitable to assist with Cross-Drive Analysis. The paper provides several examples that use timelines generated from multiple disk images, including USB stick connections, Skype calls, and access to files on a memory card
Multichannel in a complex world
The proliferation of devices and channels has brought new challenges to just about every
organisation in delivering consistently good customer experiences and effectively joining up
service provision with marketing activity, data and content. A good multichannel strategy and
execution is increasingly becoming essential to marketers and customer experience
professionals from every sector. This report seeks to identify the key issues, challenges and opportunities that surround
multichannel and provide some best practice insight and principles on the elements that are
key to multichannel success. As part of the research for this report, we spoke to six
experienced customer experience and marketing practitioners from large organisations
across different sectors.
In Multichannel Marketing: Metrics and Methods for On and Offline Success, Akin Arikan
(2008) said:
‘Because customers are multichannel beings and demand relevant, consistent experiences
across all channels, businesses need to adopt a multichannel mind-set when listening to
their customers.’
It was clear from the companies interviewed for this report that it remains challenging for
many organisations to maintain consistency across so many customer touchpoints. Not only
that, but the ability to balance consistency with the capability to fully exploit the unique
attributes of each channel remains an aspiration for many.
The proliferation of devices and digital channels has added complexity to customer journeys,
making issues around the joining up of customer experience and the attribution of value of
key importance to many. Whilst senior leaders within the organisations spoken to seem to be
bought in to multichannel, this buy-in was not always replicated across the rest of the
organisation and did not always translate into a cohesive multichannel strategy. A number of companies were undertaking work around customer journey mapping and
customer segmentation, using a variety of passive and actively collected data in order to
identify specific areas of poor customer experience and create action plans for improvement.
Others were undertaking projects using sophisticated tracking and tagging technologies to
develop an understanding of the value and role of specific channels and to provide better
intelligence to the business on attribution that might be used to inform future investment
decisions.
A consistent barrier to improving customer experience is the ability to join up many different
legacy systems and data in order to provide a single customer view and form the basis for
delivery of a more consistent and cohesive multichannel approach.
Whilst there remain significant challenges around multichannel, there are some useful
technologies allowing businesses to develop better insight into customer motivation and
activity. Nonetheless, delivery of seamless multichannel experience remains a work-inprogress
for many
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
- …