End-to-end transformer-based models in textual-based NLP
A Rahali, MA Akhloufi - AI, 2023 - mdpi.com
Transformer architectures are highly expressive because they use self-attention
mechanisms to encode long-range dependencies in the input sequences. In this paper, we …
mechanisms to encode long-range dependencies in the input sequences. In this paper, we …
DawnGNN: Documentation augmented windows malware detection using graph neural network
Abstract Application Program Interface (API) calls are widely used in dynamic Windows
malware analysis to characterize the run-time behavior of malware. Researchers have …
malware analysis to characterize the run-time behavior of malware. Researchers have …
Revolutionizing cyber threat detection with large language models
Natural Language Processing (NLP) domain is experiencing a revolution due to the
capabilities of Pre-trained Large Language Models (LLMs), fueled by ground-breaking …
capabilities of Pre-trained Large Language Models (LLMs), fueled by ground-breaking …
Malbertv2: Code aware bert-based model for malware identification
A Rahali, MA Akhloufi - Big Data and Cognitive Computing, 2023 - mdpi.com
To proactively mitigate malware threats, cybersecurity tools, such as anti-virus and anti-
malware software, as well as firewalls, require frequent updates and proactive …
malware software, as well as firewalls, require frequent updates and proactive …
Revolutionizing cyber threat detection with large language models: A privacy-preserving bert-based lightweight model for iot/iiot devices
The field of Natural Language Processing (NLP) is currently undergoing a revolutionary
transformation driven by the power of pre-trained Large Language Models (LLMs) based on …
transformation driven by the power of pre-trained Large Language Models (LLMs) based on …
Triplet-trained graph transformer with control flow graph for few-shot malware classification
The exponential proliferation of malware requires robust detection mechanisms for the
security of global enterprises and national infrastructures. Conventional malware …
security of global enterprises and national infrastructures. Conventional malware …
Prompt engineering-assisted malware dynamic analysis using gpt-4
P Yan, S Tan, M Wang, J Huang - arXiv preprint arXiv:2312.08317, 2023 - arxiv.org
Dynamic analysis methods effectively identify shelled, wrapped, or obfuscated malware,
thereby preventing them from invading computers. As a significant representation of …
thereby preventing them from invading computers. As a significant representation of …
Semalbert: Semantic-based malware detection with bidirectional encoder representations from transformers
J Liu, Y Zhao, Y Feng, Y Hu, X Ma - Journal of Information Security and …, 2024 - Elsevier
Abstract Machine learning models are widely used for identifying malicious software.
However, existing models suffer from issues such as imprecise polysemous representations …
However, existing models suffer from issues such as imprecise polysemous representations …
A review of advancements and applications of pre-trained language models in cybersecurity
Z Liu - 2024 12th International Symposium on Digital …, 2024 - ieeexplore.ieee.org
In this paper, we delve into the transformative role of pre-trained language models (PLMs) in
cybersecurity, offering a comprehensive examination of their deployment across a wide …
cybersecurity, offering a comprehensive examination of their deployment across a wide …
[HTML][HTML] Unmasking Large Language Models By Means of OpenAI GPT-4 and Google AI: A Deep Instruction-Based Analysis
Abstract Large Language Models (LLMs) have become a hot topic in AI due to their ability to
mimic human conversation. This study compares the open artificial intelligence generative …
mimic human conversation. This study compares the open artificial intelligence generative …