Intrusion detection plays a critical role in cyber-security domain since
malicious attacks cause irreparable damages to cyber-systems. In this
work, we propose the I2SP prototype, which is a novel Information
Sharing Platform, able to gather, pre-process, model, and distribute
network-traffic information. Within the I2SP prototype we build several
challenging deep feature learning models for network-traffic intrusion
detection. The learnt representations will be utilized for classifying
each new network measurement into its corresponding threat level. We
evaluate our prototype's performance by conducting case studies using
cyber-security data extracted from the Malware Information Sharing
Platform (MISP)-API. To the best of our knowledge, we are the first that
combine the MISP-API in order to construct an information sharing
mechanism that supports multiple novel deep feature learning
architectures for intrusion detection. Experimental results justify that
the proposed deep feature learning techniques are able to predict
accurately MISP threat-levels
