@article {Baldassarri2007367,
	title = {Detecting anomalous traffic using statistical discriminator and neural decisional motor},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {4527 LNCS},
	number = {PART 1},
	year = {2007},
	note = {cited By 0},
	pages = {367-376},
	abstract = {One of the main challenges in the information security concerns the introduction of systems able to identify intrusions. In this ambit this work takes place describing a new Intrusion Detection System based on anomaly approach. We realized a system with a hybrid solution between host-based and network-based approaches, and it consisted of two subsystems: a statistical system and a neural one. The features extracted from the network traffic belong only to the IP Header and their trend allows us detecting through a simple visual inspection if an attack occurred. Really the two-tier neural system has to indicate the status of the system. It classifies the traffic of the monitored host, distinguishing the background traffic from the anomalous one. Besides, a very important aspect is that the system is able to classify different instances of the same attack in the same class, establishing which attack occurs. {\textcopyright} Springer-Verlag Berlin Heidelberg 2007.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-38149012228\&partnerID=40\&md5=a6b1888f705a83520f222e21f9d88e66},
	author = {Paola Baldassarri and A. Montesanto and Paolo Puliti}
}
