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With its ability to deliver data to Amazon S3 as well as Amazon Redshift, Kinesis Data Firehose provides a unified Lake House storage writer interface to near-real-time ETL pipelines in the processing layer. On Amazon S3, Kinesis Data Firehose can store data in efficient Parquet or ORC files that are compressed using open-source codecs such as ZIP, GZIP, and Snappy.
While there is a plethora of methods dedicated to explicit or implicit concept drift detection, they all assume that the occurrence of the drift originates purely in the underlying changes in data sources (Sethi and Kantardzic, 2018). But what would happen if we considered the potential presence of a malicious party, aiming at attacking our data stream mining system (Biggio and Roli, 2018)? Adversarial learning in the presence of malicious data and poisoning attacks recently gained a significant attention (Miller et al., 2020). However, this area of research focuses on dealing with corrupted training/testing sets (Biggio et al., 2012; Xiao et al., 2015) and handling potentially dangerous instances present there (Umer et al., 2019). Creating artificial adversarial instances (Gao et al., 2020) or using evasion (Mahloujifar et al., 2019) are considered as the most efficient approaches. The adversarial learning set-up has rarely been discussed in the data stream context, where adversarial instances may be injected into the stream at any point. One should consider the dangerous potential of introducing adversarial concept drift into the stream. Such a fake change may either lead to a premature and unnecessary adaptation to false changes, or slow down the adaptation to the real concept drift. Analyzing the presence of adversarial data and poisoning attack in the streaming setting is a challenging, yet important direction towards making modern machine learning systems truly robust.
When all instances arriving over time originate from the same distribution, we deal with a stationary stream that requires only incremental learning and no adaptation. However, in real-world applications data very rarely falls under stationary assumptions (Masegosa et al., 2020). It is more likely to evolve over time and form temporary concepts, being subject to concept drift (Lu et al., 2019). This phenomenon affects various aspects of a data stream and thus can be analyzed from multiple perspectives. One cannot simply claim that a stream is subject to the drift. It needs to be analyzed and understood in order to be handled adequately to specific changes that occur (Goldenberg and Webb, 2019, 2020). More precise approaches may help us achieving faster and more accurate adaptation (Shaker and Hüllermeier, 2015). Let us now discuss the major aspects of concept drift and its characteristics.
Influence on decision boundaries. Firstly, we need to take into account how concept drift impacts the learned decision boundaries, distinguishing between real and virtual concept drifts (Oliveira et al., 2019). The former influences previously learned decision rules or classification boundaries, decreasing their relevance for newly incoming instances. Real drift affects posterior probabilities \(p_j(y\mathbf x)\) and additionally may impact unconditional probability density functions. It must be tackled as soon as it appears, since it impacts negatively the underlying classifier. Virtual concept drift affects only the distribution of features \(\mathbf x\) over time:
where Y is a set of possible values taken by \(S_j\). While it seems less dangerous than real concept drift, it cannot be ignored. Despite the fact that only the values of features change, it may trigger false alarms and thus force unnecessary and costly adaptations.
Feature drift. This is a type of change that happens when a subset of features becomes, or stops to be, relevant to the learning task (Barddal et al. ,2017). This can be directly related to real concept drift, as such changes will affect decision boundaries. Additionally, new features may emerge (thus extending the feature space), while the old ones may cease to arrive.
State-of-the-art drift detectors, discussed in details in Sect. 2, share common principles. They all monitor some characteristics of newly arriving instances from the data stream and use various thresholds to decide if the new data are different enough to signal concept drift presence. It is easy to notice that the used measures and statistical tests are very sensitive to any perturbations in data and offer no robustness to poisoning attacks. Existing drift detectors concentrate on checking the level of difference between two distributions, without actually analyzing the content of the newly arriving instances. Furthermore, they are realized as simple thresholding modules, not being able to adapt themselves to data at hand. This calls for new drift detectors that have enhanced robustness and can learn properties of data, instead of just measuring some simple statistics.
Hindering label query process in partially labeled streams In real-life scenarios, one does not have access to fully labeled streams. As it is impossible to obtain the ground truth for each newly arriving instance, active learning is being used to select the most useful instances for the label query (Lughofer, 2017). Obtaining labels is connected with budget management (i.e., the monetary cost for paying the annotator or domain expert) and time (i.e., how quickly the expert can label instance under the consideration). Adversarial concept drift will produce a number of instances that will pretend to be useful for the classifier (as they seem to originate from a novel concept). Even if the domain expert will correctly identify them as adversarial instances, the budget and time have already been spent. Therefore, adversarial concept drift is particularly dangerous for partially labeled streams, where it may force misuse of already scarce resources (Zliobaite et al., 2015).
Adversarial concept drift with instance-based poisoning attacks The first type assumes that the malicious party injects singular corrupted instances into the stream. They may be corrupted original instances with flipped labels or with modified feature values. Instance-based attacks are common when the attacker wants to test the robustness of the system and still needs to learn about the distribution of data in the stream. Additionally, supplying independent poisoned instances, especially when done from multiple sources, may be harder to detect that injecting a large number of instances at once. Such attacks, at lower rates, may be picked up by noise/outlier detection techniques. However, these attacks will appear more frequently than natural anomalies and will be crafted with malicious intent, requiring dedicated methods to filter them out. Instance-based poisoning attacks will not cause a false drift detection, but may significantly impair the adaptation to the actual concept drift. Figure 1 depicts the correct adaptation to a valid concept drift, while Fig. 2 depicts the same scenario with a hindered or exaggerated adaptation affected by the instance-based poisoning attacks. Figure 2 shows singular adversarial examples injected into the incoming data stream that may lead to one of the two possible poisoning situations: (i) slower adaptation; or (ii) overfitting. The hindrance of adaptation speed (see Fig. 2b) will take place when adversarial examples come from the previous concept and are purposefully injected to falsely inform the system that the old concept is still valid. This will negatively affect both drift detectors (that will underestimate the magnitude of change) and adaptive classifiers (that will impact the quality of decision boundary estimation). The second situation, overfitting (see Fig. 2c), can be caused by purposefully injecting adversarial instances to increase the presence of the noise and cause the streaming classifier to fit too closely to the fake state of the stream. This will reduce the generalization capabilities of the classifier, which in turn disables its adaptation capabilities to new, unseen data after the real drift.
Adversarial concept drift with concept-based poisoning attacks The second type assumes that the malicious party have crafted poisoned instances that form a coherent concept. This can be seen as injecting an adversarial distribution of data that fulfills the cluster and smoothness assumptions. Therefore, now we must handle a difficult attack that will elude any outlier/noise/novelty detection methods. With the concept-based poisoning attack, we may assume that the malicious party poses a significant knowledge about the real data distributions and is able to craft such concepts that are going to directly cause false alarms and conflicts with valid concept drift. The effects of concept-based poisoning attacks are much more significant that its instance-based counterparts and may result, if undetected, in significant harm to the learning system and increased recovery times for rebuilding the model. Such attacks can both cause false drift detection and hinder, critically misguide or even completely nullify, the adaptation of the learning algorithm. Figure 1 depicts the correct adaptation to a valid concept drift, while Fig. 3 depicts the same scenario with an incorrect adaptation thwarted by the concept-based poisoning attacks. Figure 3 shows structured adversarial examples that form a (sub)concept injected into the incoming data stream that may lead to one of the two possible poisoning situations: (i) false adaptation; or (ii) lack of adaptation. The false adaptation (see Fig. 3b) will take place when data stream has been poisoned by a collection of adversarial instances forming a structured concept. In such a case, the underlying classifier will adapt to this false concept, treating it as an actual change in distributions. This is especially dangerous, as this adversarial concept may either compete with a real one (mistaking drift detectors as to which distribution the classifier should adapt), or force a false adaptation where no actual drift takes place (rendering the classifier useless and impairing the entire classification system). The second situation, lack of adaptation (see Fig. 3c), can be caused by purposefully injecting adversarial concepts that reinforce the old concept. This will mask the appearance of the real concept drift and inhibit any drift detection or adaptation mechanisms. This will prohibit the classifier from following the changes in data stream and adapting to novel information. 2ff7e9595c
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