|讲座名称||Emerging Challenges in Anomaly Detection: How to Deal with Recent Security Threats|
Aniello Castiglione (S’04–M’08) received the Ph.D. degree in Computer Science from the University of Salerno, Italy. He is currently a Research Assistant with the University of Salerno and received the Italian National Habilitation as Associate Professor in Computer Science. He is Adjunct Professor at the Department of Educational Science and at the Department of Computer Science of University of Salerno (Italy). He has published more than 125 papers in international journals and conferences. His current research interests include Information Forensics, Digital Forensics, Security and Privacy on Cloud, Communication Networks, and Applied Cryptography. He is a member of several associations, including ACM and IEEE.
|讲座内容||With the widespread development of the Internet and the ever increasing success of network-centric applications in substantially any sector of the everyday’s life, the need of detecting and preventing network-originated abuses in real time is more and more important, in order to guarantee appropriate protection and reaction capabilities against the huge amount of emerging security threats. Many solutions and tools are already available for checking the traffic flows and spotting anomalous usage patterns or malicious behaviors, but most of them are substantially unable to cope with unknown phenomena (e.g., 0-day attacks). In fact, the concept of normal or anomalous behavior is a very elusive one, since it depends on a large number of variable factors, often not immediately evident, associated to usual network activities and resource usage. Hence, a new generation of self-learning models that adaptively consider and understand the hidden relationships between these factors and the innermost dynamics underlying the involved networks and applications, is needed in order to effectively recognize security threats and react to them.|