Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

YouTube

Exploring Adversarial Examples in Malware Detection

IEEE via YouTube

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore adversarial examples in malware detection through this IEEE conference talk presented at the 2nd Deep Learning and Security Workshop. Delve into the application of convolutional neural networks (CNNs) in malware detection, examining their ability to learn malicious behavior from raw executable bytes. Investigate the robustness of these architectures against active attackers and potential new attack vectors. Analyze the effectiveness of existing evasion attacks on malware detectors, considering input semantics that prevent arbitrary changes to binaries. Examine architectural weaknesses that enable new attack strategies specific to malware classification. Evaluate the generalizability, effectiveness trade-offs, and transferability of various attack strategies, including single-step attacks. Gain insights into feature engineering, end-to-end learning, natural language processing, and image classification as they relate to malware detection. Explore success rates, append strategies, and file format considerations in adversarial attacks on malware classifiers.

Syllabus

Introduction
Feature Engineering
EndtoEnd Learning
Natural Language Processing
Example Architecture
Image Classification
Benign App
Success Rate
Append Strategy
Results
Why is this happening
Takeaway
Section Header
File Format
F GSM Attack
Summary
Questions

Taught by

IEEE Symposium on Security and Privacy

Reviews

Start your review of Exploring Adversarial Examples in Malware Detection

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.