[HTML][HTML] Machine Learning in Information and Communications Technology: A Survey

E Dritsas, M Trigka - Information, 2024 - mdpi.com
The rapid growth of data and the increasing complexity of modern networks have driven the
demand for intelligent solutions in the information and communications technology (ICT) …

XMD: An expansive Hardware-telemetry based Mobile Malware Detector for Endpoint Detection

H Kumar, B Chakraborty, S Sharma… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Hardware-based Malware Detectors (HMDs) have shown promise in detecting malicious
workloads. However, the current HMDs focus solely on the CPU core of a System-on-Chip …

Systematic Literature Review of EM-SCA Attacks on Encryption

MR Zunaidi, A Sayakkara, M Scanlon - arXiv preprint arXiv:2402.10030, 2024 - arxiv.org
Cryptography is vital for data security, but cryptographic algorithms can still be vulnerable to
side-channel attacks (SCAs), physical assaults exploiting power consumption and EM …

A Power-Aware Method for IoT Networks with Mobile Stations and Dynamic Power Management Strategy

AMS Saleh - Engineering, Technology & Applied Science Research, 2023 - etasr.com
Abstract The Internet of Things (IoT) plays a critical role in the digitalization of numerous
industries, enabling increased automation, connectivity, and data collection in areas such as …

Towards improving the trustworthiness of hardware based malware detector using online uncertainty estimation

H Kumar, N Chawla… - 2021 58th ACM/IEEE …, 2021 - ieeexplore.ieee.org
Hardware-based Malware Detectors (HMDs) using Machine Learning (ML) models have
shown promise in detecting malicious workloads. However, the conventional black-box …

XMD: An expansive hardware-telemetry based mobile malware detector to enhance endpoint detection

H Kumar, B Chakraborty, S Sharma… - arXiv preprint arXiv …, 2022 - arxiv.org
Hardware-based Malware Detectors (HMDs) have shown promise in detecting malicious
workloads. However, the current HMDs focus solely on the CPU core of a System-on-Chip …

Towards Robust Real-Time Hardware-based Mobile Malware Detection using Multiple Instance Learning Formulation

H Kumar, S Sharma, B Chakraborty… - arXiv preprint arXiv …, 2024 - arxiv.org
This study introduces RT-HMD, a Hardware-based Malware Detector (HMD) for mobile
devices, that refines malware representation in segmented time-series through a Multiple …

[PDF][PDF] A Hybrid Model for Reliability Aware and Energy-Efficiency in Multicore Systems.

S Nour, SA Salem, SM Habashy - Computers, Materials & …, 2022 - researchgate.net
Recently, Multicore systems use Dynamic Voltage/Frequency Scaling (DV/FS) technology to
allow the cores to operate with various voltage and/or frequencies than other cores to save …

Efficient Cyber Security Framework for IoT Using Machine Learning Algorithms

U Sangwan, RES Chhillar - Journal of Electrical Systems, 2024 - search.proquest.com
Through the network's infrastructure, the loT can impart perception, recognition, and remote
control to inanimate objects. Due to loT's characteristics, it's possible to integrate the real …

[PDF][PDF] A methodology to support online monitoring and control of complex processes in the foundry industry using machine learning

SB Alvi - 2023 - duepublico2.uni-due.de
Small and medium foundry industries strive to produce defect free parts and are planning to
use the technological advancement to incorporate automation in their production processes …