Predicting Malware Attributes from Cybersecurity Texts
Text analytics is a useful tool for studying malware behavior and tracking emerging threats. The task of automated malware attribute identification based on cybersecurity texts is very challenging due to a large number of malware attribute labels and a small number of training instances. In this paper, we propose a novel feature learning method to leverage diverse knowledge sources such as small amount of human annotations, unlabeled text and specifications about malware attribute labels. Our evaluation has demonstrated the effectiveness of our method over the state-of-the-art malware attribute prediction systems.
Code (0)
등록된 구현이 없습니다.
Tasks
AttributeSimilar Papers 제목 키워드 기반
MalwareTextDB: A Database for Annotated Malware Articles
Cybersecurity risks and malware threats are becoming increasingly dangerous and common. Despite the severity of the problem, there has been few NLP efforts focused on tackling cybersecurity. In this paper, we discuss the…
ArticlesUMBC at SemEval-2018 Task 8: Understanding Text about Malware
We describe the systems developed by the UMBC team for 2018 SemEval Task 8, SecureNLP (Semantic Extraction from CybersecUrity REports using Natural Language Processing). We participated in three of the sub-tasks: (1) cla…
AttributeExploring the Limits of Transfer Learning with Unified Model in the Cybersecurity Domain
With the increase in cybersecurity vulnerabilities of software systems, the ways to exploit them are also increasing. Besides these, malware threats, irregular network interactions, and discussions about exploits in publ…
ArticlesTransfer LearningFeatureAnalytics: An approach to derive relevant attributes for analyzing Android Malware
Ever increasing number of Android malware, has always been a concern for cybersecurity professionals. Even though plenty of anti-malware solutions exist, a rational and pragmatic approach for the same is rare and has to …
Attributefeature selectionMalware AnalysisLarge Language Model (LLM) for Software Security: Code Analysis, Malware Analysis, Reverse Engineering
Large Language Models (LLMs) have recently emerged as powerful tools in cybersecurity, offering advanced capabilities in malware detection, generation, and real-time monitoring. Numerous studies have explored their appli…
Language ModelingLanguage ModellingLarge Language ModelMalware Analysis+1