Cyber Intrusion Prediction and Taxonomy System Using Deep Learning And Distributed Big Data Processing
Hamzah Al Najada, Imad Mahgoub, Imran Mohammed · 2018
The issue of cybersecurity is becoming more and more serious every day at all levels and in all domains. Cyber-attacks threaten the national security of every country and nation. Furthermore, cyber-attacks can significantly harm the economy. With the rapid and continuous growth of the cyber-universe, more software is being created, more data is being generated, and cybersecurity breaches and defense strategies are getting more complex. For such a problem, considering the size and complexity of the cyber-universe, big data mining techniques and advanced machine learning solutions will be most suitable to use for predicting brand-new attacks. This is because traditional machine learning methods would not help combat today's cybersecurity issues. Anomaly-based intrusion detection systems are receiving tremendous attention nowadays. This is because of the vast improvement and development in big data solutions. This paper utilizes highly imbalanced real-life benchmark network traffic datasets of multiple types of attacks. After resolving the class imbalance issue in our datasets by applying oversampling approach, our study becomes twofold. First, we are building prediction models for each type of attacks separately and optimizing the model with the highest accuracy. Then, we build a prediction model for all attacks together using deep learning with the smallest number of features and we optimize the model to achieve the highest accuracy. Our developed model can accurately predict the threat and the type of attack.