BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT DTSTART:20240310T030000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:PDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20241103T010000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:PST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20240328T034045Z UID:B8AB22BD-89FD-4800-9020-4EF1EB7051CC DTSTART;TZID=America/Los_Angeles:20240327T160000 DTEND;TZID=America/Los_Angeles:20240327T173000 DESCRIPTION:Machine learning algorithms have been applied to address a vari ety of engineering and scientific problems. The Internet has historically been prone to failures and attacks that significantly degrade its performa nce. To detect such anomalies\, efforts are made to create datasets from d ata collected by initiatives such as RIPE\, Route Views\, and IODA. Creati ng these datasets involves processing large amounts of data using distribu ted computing techniques. The application of machine learning allows the d etection of anomalies such as outages\, worms\, ransomware\, and DDoS atta cks. Machine learning algorithms can not only automate anomaly detection b ut also enable the detection of patterns not visible to humans due to thei r complexity and the volume of the number of features and data points. We will describe the entire journey from obtaining raw data through the creat ion of datasets to the performance results of models capable of detecting anomalies. Furthermore\, essential design definitions of the Python code a nd models developed to carry out such scientific investigations will be ad dressed.\n\nSpeaker(s): Luiz Felipe Silva Oliveira\, \, \n\nBldg: Applied Science Bulding\, 10704\, Simon Fraser University\, Burnaby\, British Colu mbia\, Canada\, Virtual: https://events.vtools.ieee.org/m/413712 LOCATION:Bldg: Applied Science Bulding\, 10704\, Simon Fraser University\, Burnaby\, British Columbia\, Canada\, Virtual: https://events.vtools.ieee. org/m/413712 ORGANIZER:ljilja@cs.sfu.ca SEQUENCE:19 SUMMARY:Applying Machine Learning to Detect Internet Anomalies URL;VALUE=URI:https://events.vtools.ieee.org/m/413712 X-ALT-DESC:Description: <br /><p>Machine learning algorithms have been appl ied to address a variety of engineering and scientific problems. The Inter net has historically been prone to failures and attacks that significantly degrade its performance. To detect such anomalies\, efforts are made to c reate datasets from data collected by initiatives such as RIPE\, Route Vie ws\, and IODA. Creating these datasets involves processing large amounts o f data using distributed computing techniques. The application of machine learning allows the detection of anomalies such as outages\, worms\, ranso mware\, and DDoS attacks. Machine learning algorithms can not only automat e anomaly detection but also enable the detection of patterns not visible to humans due to their complexity and the volume of the number of features and data points. We will describe the entire journey from obtaining raw d ata through the creation of datasets to the performance results of models capable of detecting anomalies. Furthermore\, essential design definitions of the Python code and models developed to carry out such scientific inve stigations will be addressed.&nbsp\;</p> END:VEVENT END:VCALENDAR