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التميز
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تميز دراسي و بحثي + جائزة تفوقية
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البحوث المنشورة
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البحث (1):
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عنوان البحث:
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Iterative Keystroke Continuous
Authentication: A Time Series Based Approach
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رابط إلى البحث:
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Click here
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تاريخ النشر:
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18/01/2018
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موجز عن البحث:
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Keyboard typing patterns are a
form of behavioural biometric that can be usefully employed for the purpose
of user authentication. The technique has been extensively investigated with
respect to the typing of fixed texts such as passwords and pin numbers, so-called
static authentication. The typical approach is to compare a current “typing
sample” with a typing template expressed in terms of a feature vector
comprised of keystroke dynamics. The feature vector approach has also been
applied in the context of continuous authentication where features are
extracted from free typing. However, the use of feature vectors for keystroke
continuous authentication entails a number of disadvantages, mostly
associated with the size of the feature vectors and their generation, which
need to capture a large number of features to be effective; thus making the
technique unsuitable for iterative (real-time) authentication as would be
required in the case of, for example, online assessments. To address this
issue, a mechanism whereby iterative real-time keystroke continuous
authentication can be achieved is proposed, by considering typing behaviour
as a form of time series, that avoids the disadvantages associated with the
feature vector approach. The reported experimental results show a
significantly improved performance using the proposed method in comparison
with the feature vector based technique.
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المؤتمرات العلمية:
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المؤتمر (1):
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عنوان المؤتمر:
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DaWaK 2016: Big Data Analytics and Knowledge Discovery
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تاريخ الإنعقاد:
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06/09/2016
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مكان
الإنعقاد:
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Porto-Portugal
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طبيعة المشاركة:
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Paper presentation
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عنوان المشاركة:
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Keyboard
Usage Authentication Using Time Series Analysis
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ملخص المشاركة:
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In this paper, we introduce a
new approach to recognising typing behaviour (biometrics) from an arbitrary
text in heterogeneous environments using the context of time series
analytics. Our proposed method differs from previous work directed at
understanding typing behaviour, which was founded on the idea of usage a
feature vector representation to construct user profiles. We represent
keystroke features as sequencing discrete points of events that allow
dynamically detection of suspicious behaviour over the temporal domain. The
significance of the approach is in the context of typing authentication
within open session environments, for example, identifying users in online
assessments and examinations used in eLearning environments and MOOCs, which
are becoming increasingly popular. The proposed representation outperforms
the established feature vector approaches with a recorded accuracy of
98 %, compared to 83 %; a significant result that clearly indicates
the advantage offered by the proposed time series representation.
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المؤتمر (2):
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عنوان المؤتمر:
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SGAI 2016:
Research and Development in Intelligent Systems XXXIII
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تاريخ الإنعقاد:
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12/012/2016
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مكان
الإنعقاد:
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Cambridge-United Kingdom
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طبيعة المشاركة:
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Paper presentation
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عنوان المشاركة:
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Towards Keystroke Continuous Authentication Using Time
Series Analytics
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ملخص المشاركة:
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An approach to Keystroke
Continuous Authentication (KCA) is described founded on a time series
analysis based approach that, unlike previous work on KCA (using feature
vector representations), takes the sequencing of keystrokes into
consideration. The significance of KCA is in the context of online
assessments and examinations used in eLearning environments and MOOCs, which
are becoming increasingly popular. The process is fully described and
analysed, including comparison with established feature vector approaches.
Our proposed method outperforms these other approaches to KCA (with a
detection accuracy of 94 %, compared to 79.53 %), a clear indicator
that the proposed time series analysis based KCA has significant potential.
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المؤتمر (3):
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عنوان المؤتمر:
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9th International Joint
Conference on Knowledge Discovery, Knowledge Engineering and Knowledge
Management -KDIR
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تاريخ الإنعقاد:
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01/11/2017
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مكان
الإنعقاد:
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Madeira, Portugal
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طبيعة المشاركة:
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Paper presentation
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ملخص المشاركة:
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In this paper, we demonstrate a
novel mechanism for continuous authentication of computer users using
keystroke dynamics. The mechanism models keystroke timing features, Flight time
(the time between consecutive keys) and Hold time (the duration of a key
press), as a multivariate time series which serves to dynamically capture
typing patterns in real/continuous time. The proposed method differs from
previous approaches for continuous authentication using keystroke dynamics,
founded on feature vector representations, which limited real-time analysis
due to the computationally expensive processing of the vectors, and which
also yielded poor authentication accuracy. The proposed mechanism is compared
to a feature vector based approach, taken from the literature, over two
datasets. The results indicate superior performance of the proposed
multivariate time series mechanisms for continuous authentication using
keystroke dynamics.
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المؤتمر (4):
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عنوان المؤتمر:
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2017 IEEE International Conference on Data Mining Workshops
(ICDMW)
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تاريخ الإنعقاد:
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18/11/2017
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مكان
الإنعقاد:
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New
Orleans-USA
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طبيعة المشاركة:
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Paper
presentation
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عنوان المشاركة:
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Spectral Keyboard Streams: Towards Effective
and Continuous Authentication
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ملخص المشاركة:
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In this paper, an innovative approach to
keyboard user monitoring (authentication), using keyboard dynamics and
founded on the concept of time series analysis, is presented. The work is
motivated by the need for robust authentication mechanisms in the context of
on-line assessment such as those featured in many online learning platforms.
Four analysis mechanisms are considered: analysis of keystroke time series in
their raw form (without any translation), analysis consequent to translating
the time series into a more compact form using either the Discrete Fourier
Transform or the Discrete Wavelet Transform, and a “benchmark”
feature vector representation of the form typically used in previous related
work. All four mechanisms are fully described and evaluated. A best
authentication accuracy of 99% was obtained using the wavelet transform.
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المؤتمر (5):
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عنوان المؤتمر:
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4th International Conference on Information Systems
Security and Privacy – ICISSP
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تاريخ الإنعقاد:
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22/01/2018
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مكان
الإنعقاد:
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Madeira, Portugal
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طبيعة المشاركة:
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Paper presentation
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عنوان المشاركة:
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Spectral Analysis of Keystroke Streams:
Towards Effective Real-time Continuous User Authentication
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ملخص المشاركة:
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Continuous authentication using keystroke
dynamics is significant for applications where continuous monitoring of a
user’s identity is desirable, for example in the context of the online
assessments and examinations frequently encountered in eLearning environments.
In this paper, a novel approach to realtime keystroke continuous
authentication is proposed that is founded on a sinusoidal signal based
approach that takes into consideration the sequencing of keystrokes. Three
alternative time series representations are considered and compared:
Keystroke Time Series (KTS), Discrete Fourier Transform (DFT) and Discrete
Wavelet Transform (DWT). The proposed process is fully described and analysed
using three keystroke dynamics datasets. The evaluation also includes a
comparison with the established Feature Vector Representation (FVR) approach.
The reported evaluation demonstrates that the proposed method, coupled with
the DWT representation, out-performs other approaches to keystro ke
continuous authentication with a best overall accuracy of 98.24%; a clear
indicator that the proposed keystroke continuous authentication using time
series analysis has significant potential.
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جوائز التكريم:
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الجائزة (1):
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مسمى الجائزة:
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Best
paper student prize
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الجهة المانحة:
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British Computer Society
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تاريخ الجائزة:
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14/12/2016
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مجال التكريم:
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Best Student Paper at AI 2016: Winner
of best application stream student paper prize in the AI 2016.
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