BEGIN:VCALENDAR
VERSION:2.0
PRODID:Data::ICal 0.24
BEGIN:VEVENT
DESCRIPTION:   'Title: Sold Out - Master Class: Hands-On Machine Learning t
 o Enhance\n   Malware Analysis\, Classification\, and Detection\n   When: 
 Saturday\, Aug 10\, 14:00 - 17:59 PDT\n   Where: Springhill Suites/Sands -
  [1]Map\n\n   Description:\n\n   Malware continues to increase in prevalen
 ce and sophistication.\n   VirusTotal reported a daily submission of 2M+ m
 alware samples. Of\n   those 2 million malware daily submissions\, over 1 
 million were unique\n   malware samples. Successfully exploiting networks 
 and systems has\n   become a highly profitable operation for malicious thr
 eat actors.\n   Traditional detection mechanisms including antivirus softw
 are fail to\n   adequately detect new and varied malware. Artificial Intel
 ligence\n   provides advanced capabilities that can enhance cybersecurity.
  The\n   purpose of this workshop is to provide an immersive\, hands on pr
 ojects\n   that teach security analysts how to train Machine Learning mode
 ls to\n   detect thousands and thousands of unique malware samples. This\n
    workshop delivers a new framework that uses Machine Learning models to\
 n   analyze malware\, produce uniform datasets for additional analysis\, a
 nd\n   classify malicious samples into malware families. Additionally\, th
 is\n   research presents a new Ensemble Classification Facility we develop
 ed\n   that leverages several Machine Learning models to enhance malware\n
    classification. To our knowledge\, this is the first research that\n   
 utilizes Machine Learning to provide enhanced classification of an\n   ent
 ire 200+ gigabyte-malware family corpus consisting of 80K+ unique\n   malw
 are samples and 70+ unique malware families. New\, labeled datasets\n   ar
 e released to aid in future classification of malware. It is time we\n   l
 everage the capabilities of Artificial Intelligence and Machine\n   Learni
 ng to enhance detection and classification of malware. Topics\n   taught t
 hrough hands-on projects include Machine Learning\, Natural\n   Language P
 rocessing\, and Deep Learning models. This workshop provides\n   a pathway
  to incorporate Artificial Intelligence into the automated\n   malware ana
 lysis domain.\n\n   SpeakerBio:  Solomon Sonya\, Computer Science Graduate
  Student at\n   Purdue University\n\n   Solomon Sonya (@0xSolomonSonya) is
  a Computer Science Graduate Student\n   at Purdue University. He earned h
 is undergraduate degree in Computer\n   Science and Master’s Degrees in 
 Computer Science\, Information\n   Systems Engineering\, and Operational S
 trategy. Solomon routinely\n   develops new cybersecurity tools and presen
 ts research\, leads\n   workshops\, and delivers keynote addresses at cybe
 r security\n   conferences around the world. Prior to attending Purdue\, S
 olomon was\n   the Director of Cyber Operations Training. Prior to that po
 sition\,\n   Solomon was a Distinguished Computer Science Instructor at th
 e United\n   States Air Force Academy\, Research Scholar at the University
  of\n   Southern California\, Los Angeles\, and an Adjunct Faculty Instruc
 tor\n   with the Advanced Course in Engineering Cyberspace Security (ACE) 
 at\n   the Air Force Research Lab in Rome\, NY.\n\n   '\n\n   1. #Springhi
 ll_Full\n\n\n
DTEND:20240811T005900Z
DTSTART:20240810T210000Z
LOCATION:WS - Springhill Suites/Sands
SUMMARY:Sold Out - Master Class: Hands-On Machine Learning to Enhance Malwa
 re Analysis\, Classification\, and Detection
END:VEVENT
END:VCALENDAR