2026-06-02T07:12:37Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/38612022-04-29T12:02:47Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848

00925njm 22002777a 4500 dc Herrero Cosío, Álvaro author Zurutuza, Urko author Corchado, Emilio author 2012-04 Neural intelligent systems can provide a visualization of the network traffic for security staff, in order to reduce the widely known high false-positive rate associated with misuse-based Intrusion Detection Systems (IDSs). Unlike previous work, this study proposes an unsupervised neural models that generate an intuitive visualization of the captured traffic, rather than network statistics. These snapshots of network events are immensely useful for security personnel that monitor network behavior. The system is based on the use of different neural projection and unsupervised methods for the visual inspection of honeypot data, and may be seen as a complementary network security tool that sheds light on internal data structures through visual inspection of the traffic itself. Furthermore, it is intended to facilitate verification and assessment of Snort performance (a well-known and widely-used misuse-based IDS), through the visualization of attack patterns. Empirical verification and comparison of the proposed projection methods are performed in a real domain, where two different case studies are defined and analyzed 0129-0657 http://hdl.handle.net/10259/3861 10.1142/S0129065712500050 Artificial Neural Networks Unsupervised Learning Projection Models Network & Computer Security Intrusion Detection Honeypots A neural-visualization IDS for honeynet data