Results 21 to 30 of about 3,896 (184)
MLP-Mixer-Autoencoder: A Lightweight Ensemble Architecture for Malware Classification
Information, 2023 Malware is becoming an effective support tool not only for professional hackers but also for amateur ones. Due to the support of free malware generators, anyone can easily create various types of malicious code.
Tuan Van Dao, Hiroshi Sato, Masao Kubo
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OF-AE: Oblique Forest AutoEncoders
, 2023 In the present work we propose an unsupervised ensemble method consisting of oblique trees that can address the task of auto-encoding, namely Oblique Forest AutoEncoders (briefly OF-AE). Our method is a natural extension of the eForest encoder introduced in [1].
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AE-Net: Novel Autoencoder-Based Deep Features for SQL Injection Attack Detection
IEEE Access, 2023 Structured Query Language (SQL) injection attacks represent a critical threat to database-driven applications and systems, exploiting vulnerabilities in input fields to inject malicious SQL code into database queries. This unauthorized access enables attackers to manipulate, retrieve, or even delete sensitive data.
Nisrean Thalji +4 more
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AE-CGAN Model based High Performance Network Intrusion Detection System
Applied Sciences, 2019 In this paper, a high-performance network intrusion detection system based on deep learning is proposed for situations in which there are significant imbalances between normal and abnormal traffic.
JooHwa Lee, KeeHyun Park
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Proceedings of the ACM Conference on Health, Inference, and Learning, 2020 Systematic review (SR) is an essential process to identify, evaluate, and summarize the findings of all relevant individual studies concerning health-related questions. However, conducting a SR is labor-intensive, as identifying relevant studies is a daunting process that entails multiple researchers screening thousands of articles for relevance.
Eric Wonhee Lee +3 more
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ViT-AE++: Improving Vision Transformer Autoencoder for Self-supervised Medical Image Representations
CoRR, 2023 Self-supervised learning has attracted increasing attention as it learns data-driven representation from data without annotations. Vision transformer-based autoencoder (ViT-AE) by He et al. (2021) is a recent self-supervised learning technique that employs a patch-masking strategy to learn a meaningful latent space. In this paper, we focus on improving
Prabhakar, Chinmay +5 more
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Sensors, 2023 The prediction of system degradation is very important as it serves as an important basis for the formulation of condition-based maintenance strategies.
Lin Huang, Xin Pan, Yajie Liu, Li Gong
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Holographic-(V)AE: an end-to-end SO(3)-Equivariant (Variational) Autoencoder in Fourier Space
Physical Review Research, 2022 Group-equivariant neural networks have emerged as a data-efficient approach to solve classification and regression tasks, while respecting the relevant symmetries of the data. However, little work has been done to extend this paradigm to the unsupervised and generative domains. Here, we present Holographic
Gian Marco Visani +3 more
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IEEE Access, 2019 Extreme learning machine is characterized by less training parameters, fast training speed, and strong generalization ability. It has been applied to obtain feature representations from the complex data in the tasks of data clustering or classification ...
Hongwei Ge +3 more
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Anomaly Detection for Agricultural Vehicles Using Autoencoders
Sensors, 2022 The safe in-field operation of autonomous agricultural vehicles requires detecting all objects that pose a risk of collision. Current vision-based algorithms for object detection and classification are unable to detect unknown classes of objects. In this
Esma Mujkic +4 more
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