2,844 research outputs found
Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams
The last decade has seen a surge of interest in adaptive learning algorithms
for data stream classification, with applications ranging from predicting ozone
level peaks, learning stock market indicators, to detecting computer security
violations. In addition, a number of methods have been developed to detect
concept drifts in these streams. Consider a scenario where we have a number of
classifiers with diverse learning styles and different drift detectors.
Intuitively, the current 'best' (classifier, detector) pair is application
dependent and may change as a result of the stream evolution. Our research
builds on this observation. We introduce the \mbox{Tornado} framework that
implements a reservoir of diverse classifiers, together with a variety of drift
detection algorithms. In our framework, all (classifier, detector) pairs
proceed, in parallel, to construct models against the evolving data streams. At
any point in time, we select the pair which currently yields the best
performance. We further incorporate two novel stacking-based drift detection
methods, namely the \mbox{FHDDMS} and \mbox{FHDDMS}_{add} approaches. The
experimental evaluation confirms that the current 'best' (classifier, detector)
pair is not only heavily dependent on the characteristics of the stream, but
also that this selection evolves as the stream flows. Further, our
\mbox{FHDDMS} variants detect concept drifts accurately in a timely fashion
while outperforming the state-of-the-art.Comment: 42 pages, and 14 figure
Longitudinal performance analysis of machine learning based Android malware detectors
This paper presents a longitudinal study of the performance of machine learning classifiers for Android malware detection. The study is undertaken using features extracted from Android applications first seen between 2012 and 2016. The aim is to investigate the extent of performance decay over time for various machine learning classifiers trained with static features extracted from date-labelled benign and malware application sets. Using date-labelled apps allows for true mimicking of zero-day testing, thus providing a more realistic view of performance than the conventional methods of evaluation that do not take date of appearance into account. In this study, all the investigated machine learning classifiers showed progressive diminishing performance when tested on sets of samples from a later time period. Overall, it was found that false positive rate (misclassifying benign samples as malicious) increased more substantially compared to the fall in True Positive rate (correct classification of malicious apps) when older models were tested on newer app samples
AIDPS:Adaptive Intrusion Detection and Prevention System for Underwater Acoustic Sensor Networks
Underwater Acoustic Sensor Networks (UW-ASNs) are predominantly used for
underwater environments and find applications in many areas. However, a lack of
security considerations, the unstable and challenging nature of the underwater
environment, and the resource-constrained nature of the sensor nodes used for
UW-ASNs (which makes them incapable of adopting security primitives) make the
UW-ASN prone to vulnerabilities. This paper proposes an Adaptive decentralised
Intrusion Detection and Prevention System called AIDPS for UW-ASNs. The
proposed AIDPS can improve the security of the UW-ASNs so that they can
efficiently detect underwater-related attacks (e.g., blackhole, grayhole and
flooding attacks). To determine the most effective configuration of the
proposed construction, we conduct a number of experiments using several
state-of-the-art machine learning algorithms (e.g., Adaptive Random Forest
(ARF), light gradient-boosting machine, and K-nearest neighbours) and concept
drift detection algorithms (e.g., ADWIN, kdqTree, and Page-Hinkley). Our
experimental results show that incremental ARF using ADWIN provides optimal
performance when implemented with One-class support vector machine (SVM)
anomaly-based detectors. Furthermore, our extensive evaluation results also
show that the proposed scheme outperforms state-of-the-art bench-marking
methods while providing a wider range of desirable features such as scalability
and complexity
Development of a strontium optical lattice clock for the SOC mission on the ISS
The ESA mission "Space Optical Clock" project aims at operating an optical
lattice clock on the ISS in approximately 2023. The scientific goals of the
mission are to perform tests of fundamental physics, to enable space-assisted
relativistic geodesy and to intercompare optical clocks on the ground using
microwave and optical links. The performance goal of the space clock is less
than uncertainty and
instability. Within an EU-FP7-funded project, a strontium optical lattice clock
demonstrator has been developed. Goal performances are instability below and fractional inaccuracy .
For the design of the clock, techniques and approaches suitable for later space
application are used, such as modular design, diode lasers, low power
consumption subunits, and compact dimensions. The Sr clock apparatus is fully
operational, and the clock transition in Sr was observed with linewidth
as small as 9 Hz.Comment: 12 pages, 8 figures, SPIE Photonics Europe 201
A Broad Ensemble Learning System for Drifting Stream Classification
In a data stream environment, classification models must handle concept drift
efficiently and effectively. Ensemble methods are widely used for this purpose;
however, the ones available in the literature either use a large data chunk to
update the model or learn the data one by one. In the former, the model may
miss the changes in the data distribution, and in the latter, the model may
suffer from inefficiency and instability. To address these issues, we introduce
a novel ensemble approach based on the Broad Learning System (BLS), where mini
chunks are used at each update. BLS is an effective lightweight neural
architecture recently developed for incremental learning. Although it is fast,
it requires huge data chunks for effective updates, and is unable to handle
dynamic changes observed in data streams. Our proposed approach named Broad
Ensemble Learning System (BELS) uses a novel updating method that significantly
improves best-in-class model accuracy. It employs an ensemble of output layers
to address the limitations of BLS and handle drifts. Our model tracks the
changes in the accuracy of the ensemble components and react to these changes.
We present the mathematical derivation of BELS, perform comprehensive
experiments with 20 datasets that demonstrate the adaptability of our model to
various drift types, and provide hyperparameter and ablation analysis of our
proposed model. Our experiments show that the proposed approach outperforms
nine state-of-the-art baselines and supplies an overall improvement of 13.28%
in terms of average prequential accuracy.Comment: Submitted to IEEE Acces
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