Malware Analysis: A Machine Learning Approach

Presented at DeepSec 2017 „Science First!“, Unknown date/time (Unknown duration).

Threats are a growing problem for people and organizations across the globe.With millions of malicious programs in the wild it has become hard to detect zero-day attacks and polymorphic viruses.This is why the need for machine learning-based detection arises ... A good understanding of malware analysis and Machine learning models is vital to ensure taking wise decisions and building a secure environment by being capable of correctly identifying and mitigating such potential threats

Presenters:

• Chiheb Chebbi - TEK-UP University
  Chiheb Chebbi is an InfoSec enthusiast and Security Researcher with experience in various aspects of Information Security, focusing on investigation of advanced cyber attacks and researching cyber espionage and APT attacks.His core interest lies in "Web Applications security" and "Industrial Control Systems". 2016 he was included in the Alibaba Security Research Center Hall Of Fame. He gave talks at the 4th Annual BSides Tampa IT Security Conference 2017 Florida USA, Black Hat Europe London 2016, NASA Space Apps Challenge 2015 and 2016, Global Windows Azure Boot camp 2014: Revolutionizing Education using cloud Computing, International Institute of technologies Sfax 2014: Introduction to Cloud Computing, Research Center in Informatics, Multimedia and Digital Data Processing of Sfax 2014: the future of Software industry

Links:

• https://deepsec.net/archive/2017.deepsec.net/speaker.html#PSLOT319

Similar Presentations:

• BSidesLV 2017 / The New Cat and Mouse Game: Attacking and Defending Machine Learning Based Software
• CactusCon 11 (2023) / Machine Learning for Security Analysts
• DEF CON 32 (2024) / Master Class: Hands-On Machine Learning to Enhance Malware Analysis, Classification, and Detection Workshop