scholarly journals Classifying Co-Resident Computer Programs Using Information Revealed by Resource Contention

2021 ◽  
Author(s):  
Tor Langehaug ◽  
Brett Borghetti ◽  
Scott Graham
Keyword(s):  
Computer Architectures ◽  
Learning Approach ◽  
Program Execution ◽  
Design Considerations ◽  
Modern Computer ◽  
High Classification Accuracy ◽  
Machine Learning Approach ◽  
Program Technique ◽  
Weight Sensor ◽  
System Operating

Modern computer architectures are complex, containing numerous components that can unintentionally reveal system operating properties. Defensive security professionals seek to minimize this kind of exposure while adversaries can leverage the data to attain an advantage. This paper presents a novel covert interrogator program technique using light-weight sensor programs to target integer, floating point, and memory units within a computer's architecture to collect data which can be used to match a running program to a known set of programs with up to 100\% accuracy under simultaneous multithreading conditions. This technique is applicable to a broad spectrum of architectural components, does not rely on specific vulnerabilities, nor requires elevated privileges. Furthermore, this research demonstrates the technique in a system with operating system containers intended to provide isolation guarantees which limit a user's ability to observe the activity of other users. In essence, this research exploits observable noise that is present whenever a program executes on a modern computer. This paper presents interrogator program design considerations, a machine learning approach to identify models with high classification accuracy, and measures the effectiveness of the approach under a variety of program execution scenarios.

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use

A Machine Learning Approach to Jet-Surface Interaction Noise Modeling

2020 ◽  
Author(s):  
Clifford A. Brown ◽  
Jonny Dowdall ◽  
Brian Whiteaker ◽  
Lauren McIntyre
Keyword(s):  
Machine Learning ◽  
Surface Interaction ◽  
Learning Approach ◽  
Noise Modeling ◽  
Machine Learning Approach

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

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