Abstract
The work presented in this paper deals with a proactive network monitoring for security and protection of computing infrastructures. We provide an exploitation of an intelligent module, in the form of a as a machine learning application using deep learning modeling, in order to enhance functionality of intrusion detection system supervising network traffic flows. Currently, intrusion detection systems work well for network monitoring in near real-time and they effectively deal with threats in a reactive way. Deep learning is the emerging generation of artificial intelligence techniques and one of the most promising candidates for intelligence integration into traditional solutions leading to quality improvement of the original solutions. The work presented in this paper faces the challenge of cooperation between deep learning techniques and large-scale data processing. The outcomes obtained from extensive and careful experiments show the applicability and feasibility of simultaneously modelled multiple monitoring channels using deep learning techniques. The proper joining of deep learning modelling with scalable data preprocessing ensures high quality and stability of model performance in dynamic and fast-changing environments such as network traffic flow monitoring.
Highlights
Computing infrastructures are constant targets of cyber attacks in order to gain access to their valuable assets such as data or computing power [1]–[3]
In order to enforce security policies such as confidentiality, integrity and availability; information risk management has to be built based on cyber security strategy consisting of the following steps: network monitoring, security protection, intrusion prevention, incident management, user education and awareness, and secure configuration [4], [5]
MOTIVATION AND CONTRIBUTIONS OF THE WORK Based on the context presented in Section II-A and II-B, the motivation of our work is to provide a proactive network monitoring solution, which collaborates with Intrusion detection systems (IDSs) supervising computing infrastructure
Summary
Computing infrastructures are constant targets of cyber attacks in order to gain access to their valuable assets such as data or computing power [1]–[3]. In order to enforce security policies such as confidentiality, integrity and availability; information risk management has to be built based on cyber security strategy consisting of (at least) the following steps: network monitoring, security protection, intrusion prevention, incident management, user education and awareness, and secure configuration [4], [5] Each of these steps is challenging and comprises a large portion of research and development. The work presents the close to the production state of the intelligent module deployment in cooperation with scalable data processing and IDS supervising network flows.
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