RNNSecureNet: Recurrent neural networks for Cyber security use-cases
This work addresses cybersecurity problems like malware detection and fraud, but is incremental as it applies existing RNN methods to new data domains.
The paper applied recurrent neural networks (RNNs) to cybersecurity tasks including incident detection, fraud detection, and Android malware classification, achieving better accuracy compared to classical machine learning algorithms through experiments with up to 1000 epochs and learning rates from 0.01 to 0.5.
Recurrent neural network (RNN) is an effective neural network in solving very complex supervised and unsupervised tasks. There has been a significant improvement in RNN field such as natural language processing, speech processing, computer vision and other multiple domains. This paper deals with RNN application on different use cases like Incident Detection, Fraud Detection, and Android Malware Classification. The best performing neural network architecture is chosen by conducting different chain of experiments for different network parameters and structures. The network is run up to 1000 epochs with learning rate set in the range of 0.01 to 0.5.Obviously, RNN performed very well when compared to classical machine learning algorithms. This is mainly possible because RNNs implicitly extracts the underlying features and also identifies the characteristics of the data. This helps to achieve better accuracy.