Biblio

Deep Learning is an area of Machine Learning research, which can be used to manipulate large amount of information in an intelligent way by using the functionality of computational intelligence. A deep learning system is a fully trainable system beginning from raw input to the final output of recognized objects. Feature selection is an important aspect of deep learning which can be applied for dimensionality reduction or attribute reduction and making the information more explicit and usable. Deep learning can build various learning models which can abstract unknown information by selecting a subset of relevant features. This property of deep learning makes it useful in analysis of highly complex information one which is present in intrusive data or information flowing with in a web system or a network which needs to be analyzed to detect anomalies. Our approach combines the intelligent ability of Deep Learning to build a smart Intrusion detection system.

Security in Mobile Ad Hoc networks is still ongoing research in the scientific community and it is difficult bring an overall security solution. In this paper we assess feasibility of distributed firewall solutions in the Mobile Ad Hoc Networks. Attention is also focused on different security solutions in the Ad Hoc networks. We propose a security architecture which secures network on the several layers and is the most secured solution out of analyzed materials. For this purpose we use distributed public key infrastructure, distributed firewall and intrusion detection system. Our architecture is using both symmetric and asymmetric cryptography and in this paper we present performance measurements and the security analysis of our solution.