https://bitbucket.org/ceadarireland/deeplearningattheshallowend
공식 코드Deep learning at the shallow end: Malware classification for non-domain experts
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Malware Detection is a significant part of endpoint security including workstations, servers, cloud instances, and mobile devices. Malware Detection is used to detect and identify malicious activities caused by malware. With the increase in the variety of malware activities on CMS based websites such as malicious malware redirects on WordPress site (Aka, WordPress Malware Redirect Hack) where the site redirects to spam, being the most widespread, the need for automatic detection and classifier amplifies as well. The signature-based Malware Detection system is commonly used for existing malware that has a signature but it is not suitable for unknown malware or zero-day malware
Source: The Threat of Adversarial Attacks on Machine Learning in Network Security - A Survey
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
Dataset Optimization Strategies for MalwareTraffic Detection
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
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
UniASM: Binary Code Similarity Detection without Fine-tuning
Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware Detection
A Framework for Enhancing Deep Neural Networks Against Adversarial Malware
PAD: Towards Principled Adversarial Malware Detection Against Evasion Attacks
RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion
Fast & Furious: Modelling Malware Detection as Evolving Data Streams
Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks
EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.