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Found 16 papers, 2 papers with code
Light up that Droid! On the Effectiveness of Static Analysis Features against App Obfuscation for Android Malware Detection
Therefore, it needs to be determined to what extent the use of a specific obfuscation strategy or tool poses a risk for the validity of ML malware detectors for Android based on static analysis features.
Neuroevolutionary algorithms driven by neuron coverage metrics for semi-supervised classification
We assess the impact of these functions on semi-supervised problems with a varying amount of labeled instances.
Neural Improvement Heuristics for Graph Combinatorial Optimization Problems
Conducted experiments demonstrate that the proposed model can recommend neighborhood operations that outperform conventional versions for the Preference Ranking Problem with a performance in the 99th percentile.
Towards a Fair Comparison and Realistic Evaluation Framework of Android Malware Detectors based on Static Analysis and Machine Learning
As in other cybersecurity areas, machine learning (ML) techniques have emerged as a promising solution to detect Android malware.
Neural Combinatorial Optimization: a New Player in the Field
Neural Combinatorial Optimization attempts to learn good heuristics for solving a set of problems using Neural Network models and Reinforcement Learning.
Redefining Neural Architecture Search of Heterogeneous Multi-Network Models by Characterizing Variation Operators and Model Components
In this paper, we investigate the effect of different variation operators in a complex domain, that of multi-network heterogeneous neural models.
On the Exploitation of Neuroevolutionary Information: Analyzing the Past for a More Efficient Future
Neuroevolutionary algorithms, automatic searches of neural network structures by means of evolutionary techniques, are computationally costly procedures.
Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process
As such, we discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods.
Evolving Gaussian Process kernels from elementary mathematical expressions
Gaussian Process is a state-of-the-art technique for regression and classification that heavily relies on a kernel function.
Sentiment analysis with genetically evolved Gaussian kernels
Our results show that the algorithm can outperform Gaussian Processes with traditional kernels for some of the sentiment analysis tasks considered.
Towards automatic construction of multi-network models for heterogeneous multi-task learning
Multi-task learning, as it is understood nowadays, consists of using one single model to carry out several similar tasks.
Expanding variational autoencoders for learning and exploiting latent representations in search distributions
In the past, evolutionary algorithms (EAs) that use probabilistic modeling of the best solutions incorporated latent or hidden vari- ables to the models as a more accurate way to represent the search distributions.
Towards a more efficient representation of imputation operators in TPOT
We evaluate the method to introduce imputation methods as part of TPOT.
Evolving imputation strategies for missing data in classification problems with TPOT
Missing data has a ubiquitous presence in real-life applications of machine learning techniques.
Computing factorized approximations of Pareto-fronts using mNM-landscapes and Boltzmann distributions
NM-landscapes have been recently introduced as a class of tunable rugged models.
MOEA/D-GM: Using probabilistic graphical models in MOEA/D for solving combinatorial optimization problems
Evolutionary algorithms based on modeling the statistical dependencies (interactions) between the variables have been proposed to solve a wide range of complex problems.
