Welcome Prof. Mamoun Alazab, IEEE Senior Member, Charles Darwin University, Australia to be keynote speaker!


Prof. Mamoun Alazab,Founder and Chair of IEEE Northern Territory Subsection, Charles Darwin University, Australia (click)

Research Area: cybersecurity and digital forensics, Computer science,machine learning,artificial intelligence

Title:Malware Analysis using Artificial Intelligence and Deep Learning

Abstract: Malicious software is one of the most serious threats to information security today. Malware analysis is a fastgrowing field demanding a great deal of attention because of remarkable progress in social networks, cloud and web technologies, ecommerce, mobile environments, smart grids, Internet of Things (IoT), etc. Due to this evolving cyber threat landscape, legacy solutions built on specified rule sets, such as signaturedriven security capabilities, cannot scale to fully meet the demand of advanced malware and other cybercrime detection and prevention. Artificial Intelligence (AI) and Deep Learning (DL) techniques have been successfully applied to many computer applications. These solutions often provide significant improvements as compared to more traditional machine learning methods and have resulted in new industry standards in highly cognitive tasks, ranging from natural language processing to self-driving cars. However, a relatively limited number of studies have applied these powerful techniques for malware analysis. The purpose of this presentation is to describe a recent trend in malware attacks, such as obfuscations,  zeroday exploits, botnet attacks against internet banking applications, the emergence of the darknet, malware-as-a-service.  Signature recognition and anomaly detection are the most common security detection techniques in use today. These techniques provide a strong defense. However, they fall short of detecting complicated or sophisticated attacks. I will also illustrate how new analytics using AI and DL can be used to uncover hidden patterns in malware attacks draw on real-world data.