no code implementations • 4 Feb 2024 • Brian Etter, James Lee Hu, Mohammedreza Ebrahimi, Weifeng Li, Xin Li, Hsinchun Chen
Adversarial Malware Generation (AMG), the gen- eration of adversarial malware variants to strengthen Deep Learning (DL)-based malware detectors has emerged as a crucial tool in the development of proactive cyberdefense.
no code implementations • 27 Dec 2023 • Benjamin M. Ampel, Chi-Heng Yang, James Hu, Hsinchun Chen
To assist IS research in understanding how to operationalize LLMs, we propose a Text Analytics for Information Systems Research (TAISR) framework.
no code implementations • 25 Oct 2022 • James Lee Hu, MohammadReza Ebrahimi, Weifeng Li, Xin Li, Hsinchun Chen
This provides an opportunity for the defenders (i. e., malware detectors) to detect the adversarial variants by utilizing more than one view of a malware file (e. g., source code view in addition to the binary view).
1 code implementation • 5 May 2022 • MohammadReza Ebrahimi, Yidong Chai, Hao Helen Zhang, Hsinchun Chen
This incentivizes developing domain adaptation methods that leverage the knowledge in known domains (source) and adapt to new domains (target) with a different probability distribution.
no code implementations • 22 Apr 2022 • Xin Li, Hsinchun Chen, Zan Huang, Hua Su, Jesse D. Martinez
In this paper, we propose a comprehensive framework for constructing and analyzing large-scale gene functional networks based on the gene/protein interactions extracted from biomedical literature repositories using text mining tools.
no code implementations • 22 Apr 2022 • Xin Li, Hsinchun Chen, Jiexun Li, Zhu Zhang
Predicting gene functions is a challenge for biologists in the post genomic era.
no code implementations • 8 Jan 2022 • Ning Zhang, MohammadReza Ebrahimi, Weifeng Li, Hsinchun Chen
In this study, we propose a novel framework for automated breaking of dark web CAPTCHA to facilitate dark web data collection.
no code implementations • 3 Dec 2021 • James Lee Hu, MohammadReza Ebrahimi, Hsinchun Chen
Given that most malware detectors enforce a query limit, this could result in generating non-realistic adversarial examples that are likely to be detected in practice due to lack of stealth.
no code implementations • 11 Nov 2021 • Yizhi Liu, Fang Yu Lin, MohammadReza Ebrahimi, Weifeng Li, Hsinchun Chen
While Information Extraction (IE) techniques can be used to extract the PII automatically, Deep Learning (DL)-based IE models alleviate the need for feature engineering and further improve the efficiency.
1 code implementation • 14 Dec 2020 • MohammadReza Ebrahimi, Ning Zhang, James Hu, Muhammad Taqi Raza, Hsinchun Chen
Recently, deep learning-based static anti-malware detectors have achieved success in identifying unseen attacks without requiring feature engineering and dynamic analysis.
no code implementations • 7 Oct 2020 • Sagar Samtani, Hongyi Zhu, Balaji Padmanabhan, Yidong Chai, Hsinchun Chen
Related to this broader goal, this paper makes five timely contributions.
no code implementations • 27 Sep 2013 • Ahmed Abbasi, Zhu Zhang, Hsinchun Chen
Existing fake website detection systems are unable to effectively detect fake websites.
no code implementations • 27 Sep 2013 • Tianjun Fu, Ahmed Abbasi, Daniel Zeng, Hsinchun Chen
Despite the prevalence of sentiment-related content on the Web, there has been limited work on focused crawlers capable of effectively collecting such content.
no code implementations • 27 Sep 2013 • Ahmed Abbasi, Hsinchun Chen
The ability to automatically detect fraudulent escrow websites is important in order to alleviate online auction fraud.