https://github.com/s2labres/autorobust
공식 코드How to Train your Antivirus: RL-based Hardening through the Problem-Space
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Malware Classification is the process of assigning a malware sample to a specific malware family. Malware within a family shares similar properties that can be used to create signatures for detection and classification. Signatures can be categorized as static or dynamic based on how they are extracted. A static signature can be based on a byte-code sequence, binary assembly instruction, or an imported Dynamic Link Library (DLL). Dynamic signatures can be based on file system activities, terminal commands, network communications, or function and system call sequences.
Source: Behavioral Malware Classification using Convolutional Recurrent Neural Networks
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
How to Train your Antivirus: RL-based Hardening through the Problem-Space
Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved Features
DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness
Adversarially Robust Learning with Optimal Transport Regularized Divergences
The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learning
Stealing and Evading Malware Classifiers and Antivirus at Low False Positive Conditions
Learning Security Classifiers with Verified Global Robustness Properties
High-resolution Image-based Malware Classification using Multiple Instance Learning
DetectBERT: Towards Full App-Level Representation Learning to Detect Android Malware
Dynamic data fusion using multi-input models for malware classification
Why an Android App is Classified as Malware? Towards Malware Classification Interpretation
Prompt Engineering-assisted Malware Dynamic Analysis Using GPT-4
Deep Learning for Android Malware Defenses: a Systematic Literature Review
Efficient Concept Drift Handling for Batch Android Malware Detection Models
Learning a Neural-network-based Representation for Open Set Recognition
Learning a Neural-network-based Representation for Open Set Recognition
Explanation-Guided Backdoor Poisoning Attacks Against Malware Classifiers
Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
Assemblage: Automatic Binary Dataset Construction for Machine Learning
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
Dynamic Malware Analysis with Feature Engineering and Feature Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.