Effectiveness of Video-Classification in Android Malware Detection Through API-Streams and CNN-LSTM Autoencoders

D’Angelo, Gianni; Palmieri, Francesco; Robustelli, Antonio

doi:10.1007/978-981-16-9576-6_13

Cited by 6 publications

(2 citation statements)

References 35 publications

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“…Online incremental learning techniques are used to train models on continuously arriving data, where the model needs to be updated with each new sample. Some of the most popular online incremental learning techniques include [18]:…”

Section: Online Incremental Learningmentioning

confidence: 99%

Intrusion Detection based on Concept Drift Detection & Online Incremental Learning

Jemili,

Jouini,

KORBAA

2023

Preprint

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Intrusions are constantly evolving and changing, and to keep up with these changes, it is necessary to have models that detect these changes, also known as concept drifts, and offer the ability to update the model without starting the learning process from scratch. In our contribution, we have opted for a new approach to intrusion detection based on concept drift detection and online incremental learning, named DDM-ORF. Our approach is based on the Detection Drift Method (DDM) and Online Random Forest algorithm (ORF). The model has shown very good accuracy compared to traditional approaches and an ability to handle massive data, providing multi-class classification that allows for determining insights. The proposed system achieves very good classification results, along with good processing speed that meets real-world scenarios. Apache Spark Structured Streaming provides important functionalities for dealing with streaming data and enables the deployment of the proposed system DDM-ORF in real-world applications.

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Section: Online Incremental Learningmentioning

confidence: 99%

Intrusion Detection based on Concept Drift Detection & Online Incremental Learning

Jemili,

Jouini,

KORBAA

2023

Preprint

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“…The architecture of a convolutional neural network (CNN) plays a critical role in its ability to process and extract meaningful features from input data effectively. The architecture of a CNN refers to the arrangement and organization of its layers and their connectivity patterns [30].…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Dynamic Clustering Strategies Boosting Deep Learning in Olive Leaf Disease Diagnosis

Alsaeedi,

Al-juboori,

Al-Mahmood

et al. 2023

Sustainability

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Artificial intelligence has many applications in various industries, including agriculture. It can help overcome challenges by providing efficient solutions, especially in the early stages of development. When working with tree leaves to identify the type of disease, diseases often show up through changes in leaf color. Therefore, it is crucial to improve the color brightness before using them in intelligent agricultural systems. Color improvement should achieve a balance where no new colors appear, as this could interfere with accurate identification and diagnosis of the disease. This is considered one of the challenges in this field. This work proposes an effective model for olive disease diagnosis, consisting of five modules: image enhancement, feature extraction, clustering, and deep neural network. In image enhancement, noise reduction, balanced colors, and CLAHE are applied to LAB color space channels to improve image quality and visual stimulus. In feature extraction, raw images of olive leaves are processed through triple convolutional layers, max pooling operations, and flattening in the CNN convolutional phase. The classification process starts by dividing the data into clusters based on density, followed by the use of a deep neural network. The proposed model was tested on over 3200 olive leaf images and compared with two deep learning algorithms (VGG16 and Alexnet). The results of accuracy and loss rate show that the proposed model achieves (98%, 0.193), while VGG16 and Alexnet reach (96%, 0.432) and (95%, 1.74), respectively. The proposed model demonstrates a robust and effective approach for olive disease diagnosis that combines image enhancement techniques and deep learning-based classification to achieve accurate and reliable results.

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An Android Malware Multi-class Classification Explained Through Genetic Programming

D’Angelo,

Palmieri,

Robustelli

2024

Computational Science and Its Applications – ICCSA 2024 Workshops

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Effectiveness of Video-Classification in Android Malware Detection Through API-Streams and CNN-LSTM Autoencoders

Cited by 6 publications

References 35 publications

Intrusion Detection based on Concept Drift Detection & Online Incremental Learning

Intrusion Detection based on Concept Drift Detection & Online Incremental Learning

Dynamic Clustering Strategies Boosting Deep Learning in Olive Leaf Disease Diagnosis

An Android Malware Multi-class Classification Explained Through Genetic Programming

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Effectiveness of Video-Classification in Android Malware Detection Through API-Streams and CNN-LSTM Autoencoders

Cited by 6 publications

References 35 publications

Intrusion Detection based on Concept Drift Detection &amp; Online Incremental Learning

Intrusion Detection based on Concept Drift Detection &amp; Online Incremental Learning

Dynamic Clustering Strategies Boosting Deep Learning in Olive Leaf Disease Diagnosis

An Android Malware Multi-class Classification Explained Through Genetic Programming

Contact Info

Product

Resources

About

Intrusion Detection based on Concept Drift Detection & Online Incremental Learning

Intrusion Detection based on Concept Drift Detection & Online Incremental Learning