# Concept Drift Detection Based on Deep Neural Networks and Autoencoders
## 4 Experiment
### Datasets
To validate the effectiveness of the algorithm proposed in this paper, experiments were also conducted on real-world datasets. The details of the real-world datasets are as follows:  
* Weather Dataset ([[ftp://ftp.ncdc.noaa.gov/pub/data/gsod](ftp://ftp.ncdc.noaa.gov/pub/data/gsod)]([ftp://ftp.ncdc.noaa.gov/pub/data/gsod](ftp://ftp.ncdc.noaa.gov/pub/data/gsod))(accessed on 5 March 2023)): This dataset contains 18,159 samples and 8 features and is a binary classification dataset. Each feature represents information such as wind speed, temperature, and visibility on a given day, while the class label indicates whether it rained that day. * Electricity Dataset ([[https://moa.cms.waikato.ac.nz/datasets/](https://moa.cms.waikato.ac.nz/datasets/)]([https://moa.cms.waikato.ac.nz/datasets/](https://moa.cms.waikato.ac.nz/datasets/))(accessed on 5 March 2023)): This dataset comes from the Australian New South Wales Electricity Market, with data recorded every 5 min. It consists of 45,312 samples and 8 features, where each feature represents attributes such as date and price, and the label indicates whether the electricity price increased. * Usenet1 and Usenet2 Datasets ([[http://mlkd.csd.auth.gr/concept_drift.html](http://mlkd.csd.auth.gr/concept_drift.html)]([http://mlkd.csd.auth.gr/concept_drift.html](http://mlkd.csd.auth.gr/concept_drift.html))(accessed on 1 March 2023)): These two datasets describe twenty streams of data from newsgroups, simulating the data streams from twenty different newsgroups. * Email Data Dataset (link in footnote 3): This dataset is a stream containing 1500 samples and 913 features. The classification task is to determine whether the user is interested in the current email. * NSL-KDD Dataset ([[https://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html](https://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html)]([https://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html](https://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html)) (accessed on 24 February 2025)): This dataset was used in the Third International Knowledge Discovery and Data Mining Tools Competition. It consists of 41 features \begin{table}
\begin{tabular}{c c c c c} \hline
**Datasets** & **Number of Features** & **Number of Samples** & **Drift Positions** & **Drift Width** \\ \hline SEA\_a & 3 & 10,000 & 2500, 5000, 7500 & 1 \\ SEA\_g & 3 & 10,000 & 2000, 4500, 7000 & 1000 \\ Sine\_a & 2 & 10,000 & 2500, 5000, 7500 & 1 \\ Sine\_g & 2 & 10,000 & 2000, 4500, 7000 & 1000 \\ Mixed\_A 4 & 4 & 10,000 & 2000, 4000, 6000, 8000 & 1 \\ AGR\_a & 6 & 10,000 & 1000, 2000, 3000, 4000, 5000, & 1 \\ AGR\_g & 6 & 10,000 & 6000, 7000, 8000, 9000 & 1 \\  & & 900, 1900, 2900, 3900, 4900, & \\  & & 5900, 6900, 7900, 8900 & 200 \\ \hline \end{tabular}  
* The word ‘A’ mentioned means the type of drift is multiple abrupt.  
\end{table} 
Table 2: Synthetic data with multiple drifts. and 35,141 instances, with the classification task aimed at identifying the type of network attacks. For real-world data, we selected five real-world datasets with unknown drift points, along with one dataset (NSL-KDD) where drift can be verified, to validate the effectiveness of our proposed method. It is important to note that the original NSL-KDD dataset contains 4,898,431 instances, and the official dataset repository explicitly states that the distribution of the test set differs from that of the training set. Additionally, due to storage constraints, following the processing method in [37], we conducted experiments using only 10% of the training data combined with the test data. Although real-world datasets commonly used for concept drift detection typically exhibit distributional changes, the exact drift locations are unknown, making it challenging to accurately assess the detection algorithm's performance. [PERSON] et al. [13] evaluated the detection delay performance of the algorithm by introducing artificial drift points.