Using bert to extract topic-independent sentiment features for social media bot detection

M Heidari, JH Jones - 2020 11th IEEE annual ubiquitous …, 2020 - ieeexplore.ieee.org
Millions of online posts about different topics and products are shared on popular social
media platforms. One use of this content is to provide crowd-sourced information about a …

Deep contextualized word embedding for text-based online user profiling to detect social bots on twitter

M Heidari, JH Jones, O Uzuner - … International Conference on …, 2020 - ieeexplore.ieee.org
Social media platforms can expose influential trends in many aspects of everyday life.
However, the trends they represent can be contaminated by disinformation. Social bots are …

Semantic convolutional neural network model for safe business investment by using bert

M Heidari, S Rafatirad - 2020 Seventh International Conference …, 2020 - ieeexplore.ieee.org
The real estate market creates one of the significant business domains for investors, but a
wise investment in real estate is more important for low-income people who have just one …

Using transfer learning approach to implement convolutional neural network model to recommend airline tickets by using online reviews

M Heidari, S Rafatirad - 2020 15th International Workshop on …, 2020 - ieeexplore.ieee.org
Social Media provides an opportunity for people to share their idea about different aspects of
life. Traveling is one of the essential aspects of life. In this paper, we use Bidirectional …

How to Defend and Secure Deep Learning Models Against Adversarial Attacks in Computer Vision: A Systematic Review

L Dhamija, U Bansal - New Generation Computing, 2024 - Springer
Deep learning plays a significant role in developing a robust and constructive framework for
tackling complex learning tasks. Consequently, it is widely utilized in many security-critical …

Stochastic-hmds: Adversarial-resilient hardware malware detectors via undervolting

MS Islam, I Alouani… - 2023 60th ACM/IEEE …, 2023 - ieeexplore.ieee.org
Machine learning-based hardware malware detectors (HMDs) offer a potential game
changing advantage in defending systems against malware. However, HMDs suffer from …

Online user profiling to detect social bots on twitter

M Heidari, JH Jones Jr, O Uzuner - arXiv preprint arXiv:2203.05966, 2022 - arxiv.org
Social media platforms can expose influential trends in many aspects of everyday life.
However, the movements they represent can be contaminated by disinformation. Social bots …

Transformers: A Security Perspective

BS Latibari, N Nazari, MA Chowdhury, KI Gubbi… - IEEE …, 2024 - ieeexplore.ieee.org
The Transformers architecture has recently emerged as a revolutionary paradigm in the field
of deep learning, particularly excelling in Natural Language Processing (NLP) and …

Vpp: Privacy preserving machine learning via undervolting

MS Islam, B Omidi, I Alouani… - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
Machine Learning (ML) systems are susceptible to membership inference attacks (MIAs),
which leak private information from the training data. Specifically, MIAs are able to infer …

A brain-inspired approach for malware detection using sub-semantic hardware features

M Parsa, KN Khasawneh, I Alouani - … of the Great Lakes Symposium on …, 2023 - dl.acm.org
Despite significant efforts to enhance the resilience of computer systems against malware
attacks, the abundance of exploitable vulnerabilities remains a significant challenge. While …