https://github.com/yan-scnu/prompted_dynamic_detection
공식 코드Prompt Engineering-assisted Malware Dynamic Analysis Using GPT-4
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
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
Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
Dynamic Malware Analysis with Feature Engineering and Feature Learning
Black-Box Attacks against RNN based Malware Detection Algorithms
Evading Malware Classifiers via Monte Carlo Mutant Feature Discovery
Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
Interpreting Machine Learning Malware Detectors Which Leverage N-gram Analysis
Evaluating Explanation Methods for Deep Learning in Security
Evaluating Explanation Methods for Deep Learning in Security
Detecting DGA domains with recurrent neural networks and side information
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning
ALOHA: Auxiliary Loss Optimization for Hypothesis Augmentation
DeepXplore: Automated Whitebox Testing of Deep Learning Systems
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.