A comprehensive survey on machine learning approaches for malware detection in IoT-based enterprise information system

A Gaurav, BB Gupta, PK Panigrahi - Enterprise Information …, 2023 - Taylor & Francis
ABSTRACT The Internet of Things (IoT) is a relatively new technology that has piqued
academics' and business information systems' attention in recent years. The Internet of …

[HTML][HTML] The rise of machine learning for detection and classification of malware: Research developments, trends and challenges

D Gibert, C Mateu, J Planes - Journal of Network and Computer …, 2020 - Elsevier
The struggle between security analysts and malware developers is a never-ending battle
with the complexity of malware changing as quickly as innovation grows. Current state-of-the …

The role of machine learning in cybersecurity

G Apruzzese, P Laskov, E Montes de Oca… - … Threats: Research and …, 2023 - dl.acm.org
Machine Learning (ML) represents a pivotal technology for current and future information
systems, and many domains already leverage the capabilities of ML. However, deployment …

Dos and don'ts of machine learning in computer security

D Arp, E Quiring, F Pendlebury, A Warnecke… - 31st USENIX Security …, 2022 - usenix.org
With the growing processing power of computing systems and the increasing availability of
massive datasets, machine learning algorithms have led to major breakthroughs in many …

“real attackers don't compute gradients”: bridging the gap between adversarial ml research and practice

G Apruzzese, HS Anderson, S Dambra… - … IEEE conference on …, 2023 - ieeexplore.ieee.org
Recent years have seen a proliferation of research on adversarial machine learning.
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …

A review of android malware detection approaches based on machine learning

K Liu, S Xu, G Xu, M Zhang, D Sun, H Liu - IEEE access, 2020 - ieeexplore.ieee.org
Android applications are develo** rapidly across the mobile ecosystem, but Android
malware is also emerging in an endless stream. Many researchers have studied the …

Wild patterns: Ten years after the rise of adversarial machine learning

B Biggio, F Roli - Proceedings of the 2018 ACM SIGSAC Conference on …, 2018 - dl.acm.org
Deep neural networks and machine-learning algorithms are pervasively used in several
applications, ranging from computer vision to computer security. In most of these …

Multimodal dual-embedding networks for malware open-set recognition

J Guo, H Wang, Y Xu, W Xu, Y Zhan… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Malware open-set recognition (MOSR) is an emerging research domain that aims at jointly
classifying malware samples from known families and detecting the ones from novel …

Unique identification of 50,000+ virtual reality users from head & hand motion data

V Nair, W Guo, J Mattern, R Wang, JF O'Brien… - 32nd USENIX Security …, 2023 - usenix.org
With the recent explosive growth of interest and investment in virtual reality (VR) and the so-
called" metaverse," public attention has rightly shifted toward the unique security and privacy …

Machine learning in cybersecurity: a comprehensive survey

D Dasgupta, Z Akhtar, S Sen - The Journal of Defense …, 2022 - journals.sagepub.com
Today's world is highly network interconnected owing to the pervasiveness of small personal
devices (eg, smartphones) as well as large computing devices or services (eg, cloud …