MAB-Malware: A Reinforcement Learning Framework for Attacking Static Malware Classifiers

3 code implementations • 6 Mar 2020 • Wei Song, Xuezixiang Li, Sadia Afroz, Deepali Garg, Dmitry Kuznetsov, Heng Yin

However, it is well-known that machine learning models are vulnerable to adversarial examples (AEs).

Cryptography and Security

A Benchmark Study of Machine Learning Models for Online Fake News Detection

1 code implementation • 12 May 2019 • Junaed Younus Khan, Md. Tawkat Islam Khondaker, Sadia Afroz, Gias Uddin, Anindya Iqbal

In this research, we conducted a benchmark study to assess the performance of different applicable machine learning approaches on three different datasets where we accumulated the largest and most diversified one.

BIG-bench Machine Learning Fake News Detection

Towards Automatic Discovery of Cybercrime Supply Chains

no code implementations • 2 Dec 2018 • Rasika Bhalerao, Maxwell Aliapoulios, Ilia Shumailov, Sadia Afroz, Damon McCoy

Our analysis of the automatically generated supply chains demonstrates underlying connections between products and services within these forums.

Identifying Products in Online Cybercrime Marketplaces: A Dataset for Fine-grained Domain Adaptation

1 code implementation • EMNLP 2017 • Greg Durrett, Jonathan K. Kummerfeld, Taylor Berg-Kirkpatrick, Rebecca S. Portnoff, Sadia Afroz, Damon McCoy, Kirill Levchenko, Vern Paxson

One weakness of machine-learned NLP models is that they typically perform poorly on out-of-domain data.

Domain Adaptation named-entity-recognition +4