Flytxt_NTNU at SemEval-2018 Task 8: Identifying and Classifying Malware Text Using Conditional Random Fields and Naïve Bayes Classifiers

Utpal Kumar Sikdar, Biswanath Barik, Björn Gambäck


Abstract
Cybersecurity risks such as malware threaten the personal safety of users, but to identify malware text is a major challenge. The paper proposes a supervised learning approach to identifying malware sentences given a document (subTask1 of SemEval 2018, Task 8), as well as to classifying malware tokens in the sentences (subTask2). The approach achieved good results, ranking second of twelve participants for both subtasks, with F-scores of 57% for subTask1 and 28% for subTask2.
Anthology ID:
S18-1144
Volume:
Proceedings of The 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
890–893
Language:
URL:
https://www.aclweb.org/anthology/S18-1144
DOI:
10.18653/v1/S18-1144
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1144.pdf