Free Registration: https://www.eventbrite.com/e/android-mobile-malware-detection-models-a-schematic-view-tickets-428133417577 Synopsis: In today’s era, smartphones have become ubiquitous because of their 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 download various apps according to their needs. However, with an increase in their popularity, there has been an analogous increase in malware attacks targeting smartphones. If a smartphone gets compromised by any malware, it may cause many serious threats, such as financial loss, system damage, data loss, and privacy leakage. Detecting such malware is the key requirement in mobile communications. This talk presents different models developed at our lab to detect Android smartphone malware. The talk first presents an in-depth analysis of how smartphone malware has evolved over the past few years, their ways of infection, threats posed by them, and a comprehensive review of the related works in the field of malware detection. The talk also introduces a static approach that analyzes permission pairs in Android phones. It next discusses a dynamic network traffic-based approach for Android malware detection to analyze the run-time behavior of malicious Android apps. Finally, the talk will present a hybrid model that combines K-Medoids and KNN algorithms on hybrid feature vectors to detect Android malware. Speaker(s): Dr Peddoju, Vishnu S. Pendyala Virtual: https://events.vtools.ieee.org/m/325448

Free Registration: https://www.eventbrite.com/e/exploring-the-math-in-support-vector-machines-tickets-425130124647 Synopsis: “SVMs are a rare example of a methodology where geometric intuition, elegant mathematics, theoretical guarantees, and practical algorithms meet” – Bennet and Campbell Support Vector Machines (SVMs) are used for supervised machine learning and have been successful in many applications including those like image classification that favor deep learning. SVM owes its power to the intriguing math involved in its fabrication. This talk will introduce SVM and cover some of that math. Topics covered will include constrained and unconstrained optimization, convexity, the general notion of a function space, minmax equilibrium, duality, Cover theorem, Kernels, and Mercer theorem. Speaker(s): Dr Pendyala, Vishnu S. Pendyala Virtual: https://events.vtools.ieee.org/m/324950