Anomaly Detection in Credit Card Transaction using Deep Learning Techniques

IDS (intrusion detection systems) use analysis of
network traffic patterns to detect incidents of hacking. It is
essential to do feature extraction in order to minimize the
computational cost associated with processing raw data in the
IDS. Feature extraction decreases the number of features,
which decreases the time it takes to train and increases
accuracy. This research employs a simple LSTM autoencoder
and a Random Forest to recognize intrusion attempts by IDSs.
By activating and disabling various characteristics, the extent
to which this feature extraction function can enhance accuracy
is examined. To find out if detection algorithms are effective
after feature extraction, the NSL-KDD dataset has been
employed. Autoencoder hyperparameters contain the two
activation functions. The loss and activation functions of the
ReLU and the SoftMax have the greatest accuracy rating of
any function. The use of a Long Short-Term Memory
Autoencoder (LSTMAE) and a Random Forest (RF) for
identifying the best features is a goal of this study. According
to preliminary experimental data, classifiers that employ these
variables have a prediction rate of 94.74 percent.