Cyberattacks and the use of malware are more and more omnipresent nowadays. Targets are as varied as states or publicly traded companies. Malware analysis has become a very important activity in the management of computer security    incidents. Organizations are often faced with suspicious files captured through   their antiviral and security monitoring systems, or during forensics analysis. Most solutions funnel out suspicious files through multiple tactics correlating    static and dynamic techniques in order to detect malware. However, these mechanisms have many practical limitations giving rise to a new research track. The aim of this paper is to tackle the use of machine learning algorithms to analyse malware and expose how data science is used to detect malware. Training systems to find attacks allows to develop better protection tools, capable of detecting unprecedented campaigns. This study reveals that many    models can be employed to evaluate their detectability. Our demonstration results illustrate the possibility to analyze malware leveraging several machine learning (ML) algorithms comparing them.

• Cybersecurity
• MalwareAnalysis

We introduce CaRP, a novel security approach using hard AI problems, merging Captcha and graphical passwords. CaRP combats various attacks, including online guessing, relay, and shoulder-surfing. It also addresses image hotspot issues in graphical passwords. Additionally, we employ Rabin’s Cryptosystem for robust password authentication in real-time systems.

• AI-based Security
• Authentication
• Cybersecurity