Papers with Code 아카이브

Malware Classification

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 4
평가표: 3
보존된 평가 기록: 35

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

논문

이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.

Malware Classification

논문

Deep Learning-Driven Malware Classification with API Call Sequence Analysis and Concept Drift Handling

RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization

Learning Temporal Invariance in Android Malware Detectors

Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems

Multimodal Techniques for Malware Classification

Integrating Explainable AI for Effective Malware Detection in Encrypted Network Traffic

Predicting Vulnerability to Malware Using Machine Learning Models: A Study on Microsoft Windows Machines

MalCL: Leveraging GAN-Based Generative Replay to Combat Catastrophic Forgetting in Malware Classification

Malware Classification using a Hybrid Hidden Markov Model-Convolutional Neural Network

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies

Image-Based Malware Classification Using QR and Aztec Codes

Applications of Positive Unlabeled (PU) and Negative Unlabeled (NU) Learning in Cybersecurity

Explainable Malware Detection through Integrated Graph Reduction and Learning Techniques

Living off the Analyst: Harvesting Features from Yara Rules for Malware Detection

XAI and Android Malware Models

On the Cost of Model-Serving Frameworks: An Experimental Evaluation

SCORE: Syntactic Code Representations for Static Script Malware Detection

Unmasking the Shadows: Pinpoint the Implementations of Anti-Dynamic Analysis Techniques in Malware Using LLM

Metamorphic Malware Evolution: The Potential and Peril of Large Language Models

Assessing the Impact of Packing on Machine Learning-Based Malware Detection and Classification Systems

Classification under strategic adversary manipulation using pessimistic bilevel optimisation

A Novel Reinforcement Learning Model for Post-Incident Malware Investigations

Deep Learning Based XIoT Malware Analysis: A Comprehensive Survey, Taxonomy, and Research Challenges

A Novel Approach to Malicious Code Detection Using CNN-BiLSTM and Feature Fusion

MASKDROID: Robust Android Malware Detection with Masked Graph Representations

Decoding Android Malware with a Fraction of Features: An Attention-Enhanced MLP-SVM Approach

Accelerating Malware Classification: A Vision Transformer Solution

Packet Inspection Transformer: A Self-Supervised Journey to Unseen Malware Detection with Few Samples

A Visualized Malware Detection Framework with CNN and Conditional GAN

MalMixer: Few-Shot Malware Classification with Retrieval-Augmented Semi-Supervised Learning

Towards Novel Malicious Packet Recognition: A Few-Shot Learning Approach

Revisiting Static Feature-Based Android Malware Detection

Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges

DetectBERT: Towards Full App-Level Representation Learning to Detect Android Malware

Android Malware Detection Based on RGB Images and Multi-feature Fusion

Improving Adversarial Robustness in Android Malware Detection by Reducing the Impact of Spurious Correlations