Efficient handling of concept drift and concept evolution over Stream Data

Haque, Ahsanul; Khan, Latifur; Baron, Michael; Thuraisingham, Bhavani; Aggarwal, Charų C.

doi:10.1109/icde.2016.7498264

Cited by 77 publications

(35 citation statements)

References 19 publications

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“…To address these challenges, in future we would like to identify the right point in the incoming stream from where we need to re-train the model incrementally (i.e., keeping old useful data) in an unsupervised manner (i.e., without labels). Hence, one of the future directions of BIND is to apply the concept of Change Point Detection (CPD) [19,20] to decide when to update the model in an unsupervised fashion and re-train incrementally.…”

Section: Discussionmentioning

confidence: 99%

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Adaptive encrypted traffic fingerprinting with bi-directional dependence

Al-Naami

Chandra

Mustafa

et al. 2016

Proceedings of the 32nd Annual Conference on Computer Security Applications

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Recently, network traffic analysis has been increasingly used in various applications including security, targeted advertisements, and network management. However, data encryption performed on network traffic poses a challenge to these analysis techniques. In this paper, we present a novel method to extract characteristics from encrypted traffic by utilizing data dependencies that occur over sequential transmissions of network packets. Furthermore, we explore the temporal nature of encrypted traffic and introduce an adaptive model that considers changes in data content over time. We evaluate our analysis on two packet encrypted applications: website fingerprinting and mobile application (app) fingerprinting. Our evaluation shows how the proposed approach outperforms previous works especially in the open-world scenario and when defense mechanisms are considered.

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Section: Discussionmentioning

confidence: 99%

“…As discussed in §3.2.1, over time, the data patterns of the current traces may be different from the patterns in previously seen training traces. This is known as concept drift [19,20]. To address this challenge, the model has to be updated (re-trained) regularly.…”

Section: Adaptive Learningmentioning

confidence: 99%

Adaptive encrypted traffic fingerprinting with bi-directional dependence

Al-Naami

Chandra

Mustafa

et al. 2016

Proceedings of the 32nd Annual Conference on Computer Security Applications

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“…Existing algorithms detect drifts in terms of the entire sample set, but do not consider any regional changes in sub-sample sets. As a result, the test statistics of regional drifts may eventually be diluted by stable regions, which decreases sensitivity [24]. Even if the algorithms can successfully capture a distribution drift caused by regional density inequality, they are not able to distinguish whether this drift is caused by a serious regional drift or a moderate global drift ;…”

Section: Introductionmentioning

confidence: 99%

“…People's daily routines are easily changed due to special events, as well as the impact of companies' or countries' new policies. Learning models trained to discover knowledge patterns have to consider pattern drifts as time shifts [24,39]. A wide range of machine learning problems need proper solutions to handle such a dynamic environment, for example, personal assistance applications that deal with information filtering, macroeconomic forecasts, bankruptcy prediction and individual credit scoring [15].…”

Section: Introductionmentioning

confidence: 99%

Accumulating regional density dissimilarity for concept drift detection in data streams

Liu

Zhang

2018

Pattern Recognition

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In a non-stationary environment, newly received data may have different knowledge patterns to the data used to train learning models. As time passes, the performance of learning models becomes increasingly unreliable. This problem is known as concept drift and is a common issue in real-world domains. Concept drift detection has attracted increasing attention in recent years, however, hardly any existing methods pay attention to small regional drifts, and their drift detection accuracy may vary due to different statistical significance test. To address these problems, this paper presents a novel concept drift detection method that is based on regional density estimation, named nearest neighbor-based density variation identification (NN-DVI). It consists of three components. The first one is a k-nearest neighborbased space partitioning schema (NNPS) which transforms unmeasurable discrete data instances into a set of shared subspaces for density estimation. The second one is a distance function that accumulates the density discrepancies in these subspaces and quantifies the overall discrepancies. The last component is a tailored statistical significant test by which the confidence interval of a concept drift can be accurately determined. The distance applied in NN-DVI is sensitive to regional drift, and has been proven to follow a normal distribution. As a result, both the accuracy and false alarm rate of NN-DVI are statistically guaranteed. In addition, several benchmarks have been used to evaluate the method, including both synthetic and real-world datasets. The overall results show that NN-DVI has better performance in terms of addressing concept-drift-detection-related problems.

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“…Data stream classification is a challenging task due to three properties of data streams [1]: speed and size, which are concerning to a restricted amount of memory and time, forcing the learning algorithms to hold the incoming data temporarily and operate on them not more than once, and the third, which is the most critical, is variability, which indicates the evolving nature of data streams. The event of evolving incoming data is known as a concept drift [2,3]. Informally, concept drift occurs when the class labels of a set of examples change over time.…”

Section: Introductionmentioning

confidence: 99%

Fast Reaction to Sudden Concept Drift in the Absence of Class Labels

et al. 2020

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A data stream can be considered as a sequence of examples that arrive continuously and are potentially unbounded, such as web page visits, sensor readings and call records. One of the serious and challenging problems that appears in a data stream is concept drift. This problem occurs when the relation between the input data and the target variable changes over time. Most existing works make an optimistic assumption that all incoming data are labelled and the class labels are available immediately. However, such an assumption is not always valid. Therefore, a lack of class labels aggravates the problem of concept drift detection. With this motivation, we propose a drift detector that reacts naturally to sudden drifts in the absence of class labels. In a novel way, the proposed detector reacts to concept drift in the absence of class labels, where the true label of an example is not necessary. Instead of monitoring the error estimates, the proposed detector monitors the diversity of a pair of classifiers, where the true label of an example is not necessary to determine whether components disagree. Using several datasets, an experimental evaluation and comparison is conducted against several existing detectors. The experiment results show that the proposed detector can detect drifts with less delay, runtime and memory usage.

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Efficient handling of concept drift and concept evolution over Stream Data

Cited by 77 publications

References 19 publications

Adaptive encrypted traffic fingerprinting with bi-directional dependence

Adaptive encrypted traffic fingerprinting with bi-directional dependence

Accumulating regional density dissimilarity for concept drift detection in data streams

Fast Reaction to Sudden Concept Drift in the Absence of Class Labels

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