activity
20172022
most citedA Secure and Robust Scheme for Sharing Confidential Information in IoT Systems

33 citations · 39 across the 6 of their papers we have counts for

collaborators

11 papers

cs.DC2022

A Taxonomy of Error Sources in HPC I/O Machine Learning Models

Mihailo Isakov, Mikaela Currier, Eliakin del Rosario +6

I/O efficiency is crucial to productivity in scientific computing, but the increasing complexity of the system and the applications makes it difficult for practitioners to understa…

cs.LG20201 cited

NeuroFabric: Identifying Ideal Topologies for Training A Priori Sparse Networks

Mihailo Isakov, Michel A. Kinsy

Long training times of deep neural networks are a bottleneck in machine learning research. The major impediment to fast training is the quadratic growth of both memory and compute…

cs.DC2019

Drndalo: Lightweight Control Flow Obfuscation Through Minimal Processor/Compiler Co-Design

Novak Boskov, Mihailo Isakov, Michel A. Kinsy

Binary analysis is traditionally used in the realm of malware detection. However, the same technique may be employed by an attacker to analyze the original binaries in order to rev…

cs.CR201933 cited

A Secure and Robust Scheme for Sharing Confidential Information in IoT Systems

Lake Bu, Mihailo Isakov, Michel A. Kinsy

In Internet of Things (IoT) systems with security demands, there is often a need to distribute sensitive information (such as encryption keys, digital signatures, or login credenti…

cs.CR2019

Survey of Attacks and Defenses on Edge-Deployed Neural Networks

Mihailo Isakov, Vijay Gadepally, Karen M. Gettings +1

Deep Neural Network (DNN) workloads are quickly moving from datacenters onto edge devices, for latency, privacy, or energy reasons. While datacenter networks can be protected using…

cs.CR2019

CodeTrolley: Hardware-Assisted Control Flow Obfuscation

Novak Boskov, Mihailo Isakov, Michel A. Kinsy

Many cybersecurity attacks rely on analyzing a binary executable to find exploitable sections of code. Code obfuscation is used to prevent attackers from reverse engineering these…