In order to fully understand the impact of a breach, the detection of malware threats needs to be complemented by the proactive exploration of anomalous network behavior and inter-artifact relationships. This activity is supported by machine learning techniques, which can be leveraged to aggregate and classify events at an unprecedented scale.
Watch this Webinar which highlights how machine learning and network anomaly detection can be valuable tools in identifying breaches.
Speaker: Giovanni Vigna
Vigna has been researching and developing security technology for more than 20 years, working on malware analysis, web security, vulnerability assessment, and intrusion detection. He is currently a professor in the Department of Computer Science at the University of California in Santa Barbara and is the director of the Computer Security Group at UCSB. He is the author of more than 200 publications, including peer-reviewed papers in journals, conferences, and workshops, a book on intrusion correlation, and (as an editor) a book on mobile code security. Vigna has been the program chair of the International Symposium on Recent Advances in Intrusion Detection (RAID 2003), of the ISOC Symposium on Network and Distributed Systems Security (NDSS 2009), and of the IEEE Symposium on Security and Privacy (in 2010 and 2011). He is known for organizing and running an annual inter-university Capture The Flag (iCTF) hacking contest that involves dozens of institutions and hundreds of students around the world. Vigna also leads the Shellphish Hacking Team, the longest-running team playing at DefCon's CTF.