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Malware Analysis

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/FutureComputing4AI/Reverse-Engineering-Function-Search

공식 코드

Is Function Similarity Over-Engineered? Building a Benchmark

https://github.com/GiuseppeLaurenza/I_F_Identifier

공식 코드

Malware triage for early identification of Advanced Persistent Threat activities

https://github.com/adelsamir01/cyberllminstruct

공식 코드

CyberLLMInstruct: A New Dataset for Analysing Safety of Fine-Tuned LLMs Using Cyber Security Data

https://github.com/allenye66/Malware-and-Ensemble-Learning-Research

공식 코드

On Ensemble Learning

https://github.com/avast/avast-ctu-cape-dataset

공식 코드

Avast-CTU Public CAPE Dataset

https://github.com/br0kej/kyn

공식 코드

Know Your Neighborhood: General and Zero-Shot Capable Binary Function Search Powered by Call Graphlets

https://github.com/cwbae10-purdue/cti-eacl24

공식 코드

Threat Behavior Textual Search by Attention Graph Isomorphism

https://github.com/cyberhunters/Malware-Detection-Using-Machine-Learning

공식 코드

Malware Classification using Deep Learning based Feature Extraction and Wrapper based Feature Selection Technique

https://github.com/dtrizna/nebula

공식 코드

Nebula: Self-Attention for Dynamic Malware Analysis

https://github.com/dtrizna/quo.vadis

공식 코드

Quo Vadis: Hybrid Machine Learning Meta-Model based on Contextual and Behavioral Malware Representations

https://github.com/e0hyl/finer-explain

공식 코드

FINER: Enhancing State-of-the-art Classifiers with Feature Attribution to Facilitate Security Analysis

https://github.com/futurecomputing4ai/ember2024

공식 코드

EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers

https://github.com/futurecomputing4ai/vtpipeline-rs

공식 코드

EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers

https://github.com/google/magika

공식 코드

Magika: AI-Powered Content-Type Detection

https://github.com/guelfoweb/peframe

공식 코드

Integration of Static and Dynamic Analysis for Malware Family Classification with Composite Neural Network

https://github.com/hiyouga/llama-efficient-tuning

공식 코드

Large Language Models for Cyber Security: A Systematic Literature Review

https://github.com/jennieett/decompilebench

공식 코드

DecompileBench: A Comprehensive Benchmark for Evaluating Decompilers in Real-World Scenarios

https://github.com/joddiy/DynamicMalwareAnalysis

공식 코드

Dynamic Malware Analysis with Feature Engineering and Feature Learning

https://github.com/johnnyzn/MalRNN

공식 코드

Binary Black-box Evasion Attacks Against Deep Learning-based Static Malware Detectors with Adversarial Byte-Level Language Model

https://github.com/nlp-code-analysis/cross-arch-instr-model

공식 코드

A Cross-Architecture Instruction Embedding Model for Natural Language Processing-Inspired Binary Code Analysis

https://github.com/saltytrain2/DIRTY

공식 코드

Revisiting Deep Learning for Variable Type Recovery

https://github.com/Leo-Mun/API-Call-Embedding

Dynamic Malware Analysis with Feature Engineering and Feature Learning

https://github.com/NeuromorphicComputationResearchProgram/KiloGrams

KiloGrams: Very Large N-Grams for Malware Classification

https://github.com/UMBC-DREAM-Lab/montemutacon

Evading Malware Classifiers via Monte Carlo Mutant Feature Discovery

https://github.com/WilliamBriguglio/Malware-Classifier-Interpreter

Interpreting Machine Learning Malware Detectors Which Leverage N-gram Analysis

https://github.com/facebookresearch/SAFEtorch

SAFE: Self-Attentive Function Embeddings for Binary Similarity

https://github.com/gadiluna/SAFE

SAFE: Self-Attentive Function Embeddings for Binary Similarity

https://github.com/iboutsikas/montemutacon

Evading Malware Classifiers via Monte Carlo Mutant Feature Discovery

https://github.com/lucamassarelli/yarasafe

SAFE: Self-Attentive Function Embeddings for Binary Similarity

https://github.com/skyguy19/tdamalwaredetection

Reliable Malware Analysis and Detection using Topology Data Analysis

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.