https://bitbucket.org/ceadarireland/deeplearningattheshallowend
공식 코드Deep learning at the shallow end: Malware classification for non-domain experts
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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.
Deep learning at the shallow end: Malware classification for non-domain experts
Cross-Language Binary-Source Code Matching with Intermediate Representations
Statistical Estimation of Malware Detection Metrics in the Absence of Ground Truth
Improving Malware Detection Accuracy by Extracting Icon Information
Against All Odds: Winning the Defense Challenge in an Evasion Competition with Diversification
An Ensemble of Pre-trained Transformer Models For Imbalanced Multiclass Malware Classification
Dataset Optimization Strategies for MalwareTraffic Detection
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced Datasets
Malware Detection Using Frequency Domain-Based Image Visualization and Deep Learning
Sequential Embedding-based Attentive (SEA) classifier for malware classification
Classifying Sequences of Extreme Length with Constant Memory Applied to Malware Detection
DAEMON: Dataset-Agnostic Explainable Malware Classification Using Multi-Stage Feature Mining
Accelerating Malware Classification: A Vision Transformer Solution
MASKDROID: Robust Android Malware Detection with Masked Graph Representations
CyberLLMInstruct: A New Dataset for Analysing Safety of Fine-Tuned LLMs Using Cyber Security Data
pAElla: Edge-AI based Real-Time Malware Detection in Data Centers
Assemblage: Automatic Binary Dataset Construction for Machine Learning
Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy
UniASM: Binary Code Similarity Detection without Fine-tuning
Malware Classification Using Static Disassembly and Machine Learning
Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware Detection
A Framework for Enhancing Deep Neural Networks Against Adversarial Malware
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.