Mixture of expert artificial neural networks with ensemble training for reduction of various sources of false positives in CAD

2007 ◽  
Author(s):  
Kenji Suzuki ◽  
Lifeng He ◽  
Shweta Khankari ◽  
Liang Ge ◽  
Joel Verceles ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
False Positives ◽  
Artificial Neural

scholarly journals Multiclass decomposition and Artificial Neural Networks for intrusion detection and identification in Internet of Things environments

2021 ◽  
Author(s):  
Cristiano Antonio de Souza ◽  
João Vitor Cardoso ◽  
Carlos Becker Westphall
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Internet Of Things ◽  
False Positives ◽  
Machine Learning Techniques ◽  
Detection And Identification ◽  
Learning Techniques ◽  
Artificial Neural ◽  
Stage Analysis ◽  
The Internet Of Things

The Internet of Things (IoT) systems have limited resources, making it difficult to implement some security mechanisms. It is important to detect attacks against these environments and identify their type. However, existing multi-class detection approaches present difficulties related to false positives and detection of less common attacks. Thus, this work proposes an approach with a two-stage analysis architecture based on One-Vs-All (OVA) and Artificial Neural Networks (ANN) to detect and identify intrusions in fog and IoT computing environments. The results of experiments with the Bot-IoT dataset demonstrate that the approach achieved promising results and reduced the number of false positives compared to state-of-the-art approaches and machine learning techniques.

PROGRAM MATCHING THROUGH CODE ANALYSIS AND ARTIFICIAL NEURAL NETWORKS

2012 ◽  
Vol 22 (02) ◽  
pp. 225-241 ◽  
Author(s):  
TIAGO M. NASCIMENTO ◽  
DAVIDSON R. BOCCARDO ◽  
CHARLES B. PRADO ◽  
RAPHAEL C. S. MACHADO ◽  
LUIZ F. R. C. CARMO
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Software Engineering ◽  
False Positives ◽  
Program Code ◽  
Code Analysis ◽  
Novel Approach ◽  
Artificial Neural ◽  
High Level ◽  
Low Rate

Program matching refers to the mapping between equivalent codes written in different languages — including high-level and low-level languages. This equivalence is useful for some software engineering scenarios such as determining whether rewritten code is correct, which version of a program is being used, and whether a malware is present in the program. In the present work, we propose a novel approach to solve the executable code traceability by using program code analysis and artificial neural networks. From the program code analysis we obtained execution behavior properties of the codes, and from the artificial neural networks we judge about their correspondence. Our evaluation using real code examples shows an acceptable correspondence rate between 62% and 100% with the very low rate of 4% false positives.

FUTURE OF ARTIFICIAL NEURAL NETWORKS IN SPACE EXPLORATION AND SPACE ENGINEERING

2018 ◽  
pp. 6-11
Author(s):  
Kobiljon Kh. Zoidov ◽  
◽  
Svetlana V. Ponomareva ◽  
Daniel I. Serebryansky ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Space Exploration ◽  
Artificial Neural
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