BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:US/Pacific BEGIN:DAYLIGHT DTSTART:20220313T030000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:PDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20221106T010000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:PST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20221004T171833Z UID:1E5A70FA-93E7-4C34-840D-B235DACE6A3C DTSTART;TZID=US/Pacific:20221004T090000 DTEND;TZID=US/Pacific:20221004T100000 DESCRIPTION:Free Registration: https://www.eventbrite.com/e/android-mobile- malware-detection-models-a-schematic-view-tickets-428133417577\n\nSynopsis :\n\nIn today’s era\, smartphones have become ubiquitous because of thei r fascinating capabilities\, for instance\, sending and receiving emails\, online shopping\, mobile Internet browsing\, and location-based services\ , apart from regular calling and messaging features. Additionally\, a user -friendly app interface is present in most smartphones allowing users to d ownload various apps according to their needs. However\, with an increase in their popularity\, there has been an analogous increase in malware atta cks targeting smartphones. If a smartphone gets compromised by any malware \, it may cause many serious threats\, such as financial loss\, system dam age\, data loss\, and privacy leakage. Detecting such malware is the key r equirement in mobile communications. This talk presents different models d eveloped at our lab to detect Android smartphone malware. The talk first p resents an in-depth analysis of how smartphone malware has evolved over th e past few years\, their ways of infection\, threats posed by them\, and a comprehensive review of the related works in the field of malware detecti on. The talk also introduces a static approach that analyzes permission pa irs in Android phones. It next discusses a dynamic network traffic-based a pproach for Android malware detection to analyze the run-time behavior of malicious Android apps. Finally\, the talk will present a hybrid model tha t combines K-Medoids and KNN algorithms on hybrid feature vectors to detec t Android malware.\n\nSpeaker(s): Dr Peddoju\, Vishnu S. Pendyala\n\nVirtu al: https://events.vtools.ieee.org/m/325448 LOCATION:Virtual: https://events.vtools.ieee.org/m/325448 ORGANIZER:pendyala@ieee.org SEQUENCE:1 SUMMARY:IEEE Day Event 1: Android Mobile Malware Detection Models – A Sch ematic View URL;VALUE=URI:https://events.vtools.ieee.org/m/325448 X-ALT-DESC:Description: <br /><p>Free Registration: https://www.eventbrite. com/e/android-mobile-malware-detection-models-a-schematic-view-tickets-428 133417577</p>\n<p><em><strong>Synopsis:</strong></em></p>\n<p>In today&rsq uo\;s era\, smartphones have become ubiquitous because of their fascinatin g capabilities\, for instance\, sending and receiving emails\, online shop ping\, mobile Internet browsing\, and location-based services\, apart from regular calling and messaging features. Additionally\, a user-friendly ap p interface is present in most smartphones allowing users to download vari ous apps according to their needs. However\, with an increase in their pop ularity\, there has been an analogous increase in malware attacks targetin g smartphones. If a smartphone gets compromised by any malware\, it may ca use many serious threats\, such as financial loss\, system damage\, data l oss\, and privacy leakage. Detecting such malware is the key requirement i n mobile communications. This talk presents different models developed at our lab to detect Android smartphone malware. The talk first presents an i n-depth analysis of how smartphone malware has evolved over the past few y ears\, their ways of infection\, threats posed by them\, and a comprehensi ve review of the related works in the field of malware detection. The talk also introduces a static approach that analyzes permission pairs in Andro id phones. It next discusses a dynamic network traffic-based approach for Android malware detection to analyze the run-time behavior of malicious An droid apps. Finally\, the talk will present a hybrid model that combines K -Medoids and KNN algorithms on hybrid feature vectors to detect Android ma lware.</p> END:VEVENT END:VCALENDAR