@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}
}
@conference {Baldassarri200774,
	title = {Detecting anomalous traffic using statistical processing and self-organizing maps},
	booktitle = {SECRYPT 2007 - International Conference on Security and Cryptography, Proceedings},
	year = {2007},
	note = {cited By 0},
	pages = {74-79},
	abstract = {The main idea of the present work is to create a system able to detect intrusions in computer networks. For this purpose we propose a novel intrusion detection system (IDS) based on an anomaly approach. We analyzed the network traffic from (outbound traffic) and towards (inbound traffic) a victim host through another host. Besides we realized an architecture consisted of two subsystems: a statistical subsystem and a neural networks based subsystem. The first elaborates chosen features extracted from the network traffic and it allows determining if an attack occurs through a preliminary visual inspection. The neural subsystem receives in input the output of the statistical subsystem and it has to indicate the status of the monitored host. It classifies the network traffic distinguishing the background traffic from the anomalous one. Moreover the system has to be able to classify different instances of the same attack in the same class, distinguishing in a completely autonomous way different typology of attack.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-67649764419\&partnerID=40\&md5=9a732b07f49d01bbb12964089390b414},
	author = {Paola Baldassarri and A. Montesanto and Paolo Puliti}
}
