K
Konstantinos G Liakos
Researcher at University of Thessaly
Publications - 11
Citations - 1368
Konstantinos G Liakos is an academic researcher from University of Thessaly. The author has contributed to research in topics: Hardware Trojan & Netlist. The author has an hindex of 4, co-authored 9 publications receiving 598 citations.
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Journal ArticleDOI
Machine Learning in Agriculture: A Review.
TL;DR: A comprehensive review of research dedicated to applications of machine learning in agricultural production systems is presented, demonstrating how agriculture will benefit from machine learning technologies.
Proceedings ArticleDOI
Machine Learning for Hardware Trojan Detection: A Review
Konstantinos G Liakos,Georgios Georgakilas,Serafeim Moustakidis,Patrik Karlsson,Fotis Plessas +4 more
TL;DR: A comprehensive review of research dedicated to applications based on Machine Learning for the detection of HTs in ICs is presented, categorized in reverse-engineering development for the imaging phase, real-time detection, and classification approaches.
Journal ArticleDOI
Conventional and machine learning approaches as countermeasures against hardware trojan attacks
Konstantinos G Liakos,Georgios Georgakilas,Serafeim Moustakidis,Nicolas Sklavos,Fotis Plessas +4 more
TL;DR: A comprehensive review of research dedicated to countermeasures against HTs embedded into ICs is presented, grouped in four main categories; (a) conventional HT detection approaches, (b) machine learning for HT countermeasures, (c) design for security and (d) runtime monitor.
Journal ArticleDOI
Multi-branch Convolutional Neural Network for Identification of Small Non-coding RNA genomic loci
Georgios Georgakilas,Andrea Grioni,Konstantinos G Liakos,Eliska Chalupova,Fotis Plessas,Panagiotis Alexiou +5 more
TL;DR: It is demonstrated that MuStARD is a generic method that can be trained on different classes of human small RNA genomic loci, without need for domain specific knowledge, due to the automated feature and background selection processes built into the model.
Proceedings ArticleDOI
Hardware Trojan Classification at Gate-level Netlists based on Area and Power Machine Learning Analysis
TL;DR: In this paper, the authors proposed a HT classification method, named hArdware Trojan Learning AnalysiS (ATLAS), that identifies HT-infected circuits using a Gradient Boosting (GB) model on data from the gate-level netlist (GLN) phase.