3 research outputs found
Devising Malware Characterstics using Transformers
With the increasing number of cybersecurity threats, it becomes more
difficult for researchers to skim through the security reports for malware
analysis. There is a need to be able to extract highly relevant sentences
without having to read through the entire malware reports. In this paper, we
are finding relevant malware behavior mentions from Advanced Persistent Threat
Reports. This main contribution is an opening attempt to Transformer the
approach for malware behavior analysis.Comment: 5 pages, 3 figures, 3 table
Identifying Offensive Posts and Targeted Offense from Twitter
In this paper we present our approach and the system description for Sub-task
A and Sub Task B of SemEval 2019 Task 6: Identifying and Categorizing Offensive
Language in Social Media. Sub-task A involves identifying if a given tweet is
offensive or not, and Sub Task B involves detecting if an offensive tweet is
targeted towards someone (group or an individual). Our models for Sub-task A is
based on an ensemble of Convolutional Neural Network, Bidirectional LSTM with
attention, and Bidirectional LSTM + Bidirectional GRU, whereas for Sub-task B,
we rely on a set of heuristics derived from the training data and manual
observation. We provide detailed analysis of the results obtained using the
trained models. Our team ranked 5th out of 103 participants in Sub-task A,
achieving a macro F1 score of 0.807, and ranked 8th out of 75 participants in
Sub Task B achieving a macro F1 of 0.695
Suggestion Mining from Online Reviews using ULMFiT
In this paper we present our approach and the system description for Sub Task
A of SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums.
Given a sentence, the task asks to predict whether the sentence consists of a
suggestion or not. Our model is based on Universal Language Model Fine-tuning
for Text Classification. We apply various pre-processing techniques before
training the language and the classification model. We further provide detailed
analysis of the results obtained using the trained model. Our team ranked 10th
out of 34 participants, achieving an F1 score of 0.7011. We publicly share our
implementation at https://github.com/isarth/SemEval9_MIDA