Detecting malicious attacks using Cyber-security models using Deep learning approach

Abstract

Abstract In recent years, invaders have been increasing rapidly in the internet world. Gen- erally, to detect anonymous attackers, the algorithm needs more features. Many algorithms fail in the efficiency of detecting malicious activity. The deep learning approach has been used in cyber security use cases, namely, intrusion detection, malware analysis, traffic analysis, spam and phishing detection etc. In this work, to leverage the application of deep learning architectures towards cyber secu- rity, we consider malicious activity detection using Bi-LSTM. In the experiments of intrusion detection using the dataset UGR’16, the deep learning approach performed better when compared to the combination of Bi-LSTM with an autoen- coder neural network model. Moreover, the approach without autoencoder, both precision and recall are 99 Percentage for just the Bi-LSTM model in detecting malicious activities in cyber security. Moreover by using Autoencoder as feature enginerring does not yeild any higher performance when modelling deep learn- ing algorithm using Bi-directional LSTM. However, when using with Bi-LSTM without Autoencoder, the performace are more efficient and better.