Deceiving supervised machine learning models via adversarial data poisoning attacks: a case study with USB keyboards
Due to its plug-and-play functionality and wide device support, the universal serial bus
(USB) protocol has become one of the most widely used protocols. However, this …
(USB) protocol has become one of the most widely used protocols. However, this …
Madvex: Instrumentation-Based Adversarial Attacks on Machine Learning Malware Detection
WebAssembly (Wasm) is a low-level binary format for web applications, which has found
widespread adoption due to its improved performance and compatibility with existing …
widespread adoption due to its improved performance and compatibility with existing …
Robust detection model for portable execution malware
W Zheng, K Omote - ICC 2021-IEEE International Conference …, 2021 - ieeexplore.ieee.org
With recent technological developments, it has become natural for personal computers and
Internet of Things (IoT) devices, such as smartphones and tablets, to remain constantly …
Internet of Things (IoT) devices, such as smartphones and tablets, to remain constantly …
[PDF][PDF] Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach
M Conti - academia.edu
The widespread adoption of smartphones dramatically increases the risk of attacks and the
spread of mobile malware, especially on the Android platform. Machine learning-based …
spread of mobile malware, especially on the Android platform. Machine learning-based …
Can machine learning model with static features be fooled: an adversarial machine learning approach
The widespread adoption of smartphones dramatically increases the risk of attacks and the
spread of mobile malware, especially on the Android platform. Machine learning-based …
spread of mobile malware, especially on the Android platform. Machine learning-based …
Dynamically detecting usb attacks in hardware: Poster
Malicious USB devices can disguise themselves as benign devices (eg, keyboard, mouse,
etc.) to insert malicious commands on end devices. Advanced software-based detection …
etc.) to insert malicious commands on end devices. Advanced software-based detection …
Evaluating Realistic Adversarial Attacks against Machine Learning Models for Windows PE Malware Detection
During the last decade, the cybersecurity literature has conferred a high-level role to
machine learning as a powerful security paradigm to recognise malicious software in …
machine learning as a powerful security paradigm to recognise malicious software in …
[PDF][PDF] Make your IoT environments robust against adversarial machine learning malware threats: a code-cave approach
H Haddadpajouh, A Dehghantanha - sdiotsec.github.io
As the integration of Internet of Things devices continues to increase, the security challenges
associated with autonomous, self-executing Internet of Things devices become increasingly …
associated with autonomous, self-executing Internet of Things devices become increasingly …
[HTML][HTML] Algorithmic and Implementation-Based Threats for the Security of Embedded Machine Learning Models
PA Moëllic, M Dumont, K Hector, C Hennebert… - … Secure Trustable Things, 2024 - Springer
The large-scale deployment of machine learning models in a wide variety of AI-based
systems raises major security concerns related to their integrity, confidentiality and …
systems raises major security concerns related to their integrity, confidentiality and …
Smart app attack: hacking deep learning models in android apps
On-device deep learning is rapidly gaining popularity in mobile applications. Compared to
offloading deep learning from smartphones to the cloud, on-device deep learning enables …
offloading deep learning from smartphones to the cloud, on-device deep learning enables …