Explore a comprehensive conference talk from Black Hat that delves into the complex world of malware detection and analysis. Learn about the challenges faced by corporations in defending against rapidly evolving malware threats and the potential consequences of security breaches. Discover innovative approaches to identifying new malware families by understanding old malware tricks. Gain insights into advanced techniques such as machine learning, big data analysis, and active learning for improving malware detection capabilities. Examine real-world examples of phishing, ransomware, and advertising-based attacks, and understand how to map malicious infrastructures. Discuss the future of cybersecurity and the importance of staying ahead in the ongoing battle against malware.
Understanding Old Malware Tricks to Find New Malware Families
Overview
Syllabus
Introduction
Who are we
Phishing
Ransomware
Normal Hunting
Common Networks
Network Size
Big Data
Machine Learning
Combining
Challenges
Muller Dynamic
Metadata
Other Changes
Basic Features
Flowbased
Bagbased
Examples
Action Recognition
Overview
Multiple Instance Learning Approach
HTML paper
Training Data
Positive Unlabeled Training
Random Product
Neural Networks
Classification Topology
Active Learning
Classification Module
Summary
Mark the relatives
Thread analyse
N stranger
Audience Changer
Source Source
Mamba
In summary
Advertising gone rogue
Traffic in the network
Second opinion
Popnet
Mapping the infrastructure
Host names
The finish
The algorithm
More campaigns
Conclusions
What got us here
Questions
Future of security
Taught by
Black Hat
