Mendeley Data

Swab, Michelle

doi:10.5596/c16-022

Cited by 5 publications

(2 citation statements)

References 2 publications

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“…Ahuja et al [24] used various DL algorithms to detect the DDOS attacks: CNN, RNN, LSTM, CNN-LSTM, support vector classifier-self organizing map (SVC-SOM), and stacked autoencoder-multi layer perceptron (SAE-MLP). The team used the dataset provided by leading India Project Mentor [25], which consists of 22 features. Two different optimizers were used: stochastic gradient descent (SGD) for the first 10 epochs and Adam for the next 150 epochs.…”

Section: Ddos Attacksmentioning

confidence: 99%

Intelligent Techniques for Detecting Network Attacks: Review and Research Directions

Aljabri

Aljameel

Mohammad

et al. 2021

Sensors

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The significant growth in the use of the Internet and the rapid development of network technologies are associated with an increased risk of network attacks. Network attacks refer to all types of unauthorized access to a network including any attempts to damage and disrupt the network, often leading to serious consequences. Network attack detection is an active area of research in the community of cybersecurity. In the literature, there are various descriptions of network attack detection systems involving various intelligent-based techniques including machine learning (ML) and deep learning (DL) models. However, although such techniques have proved useful within specific domains, no technique has proved useful in mitigating all kinds of network attacks. This is because some intelligent-based approaches lack essential capabilities that render them reliable systems that are able to confront different types of network attacks. This was the main motivation behind this research, which evaluates contemporary intelligent-based research directions to address the gap that still exists in the field. The main components of any intelligent-based system are the training datasets, the algorithms, and the evaluation metrics; these were the main benchmark criteria used to assess the intelligent-based systems included in this research article. This research provides a rich source of references for scholars seeking to determine their scope of research in this field. Furthermore, although the paper does present a set of suggestions about future inductive directions, it leaves the reader free to derive additional insights about how to develop intelligent-based systems to counter current and future network attacks.

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Section: Ddos Attacksmentioning

confidence: 99%

Intelligent Techniques for Detecting Network Attacks: Review and Research Directions

Aljabri

Aljameel

Mohammad

et al. 2021

Sensors

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“…Section 3 gives information about the dataset and the image annotation process. Section 4 describes the ways to access data via Mendeley Data Repository [2] or LOD cloud [3]. Section 5 presents a CNN (convolutional neural network) application for demonstrating the ease of use provided by the Hermos dataset.…”

Section: Introductionmentioning

confidence: 99%

Hermos: An annotated image dataset for visual detection of grape leaf diseases

Özacar

Öztürk

Savaş³

2022

Journal of Information Science

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Powdery mildew, dead arm and vineyard downy mildew diseases are frequently seen in the vineyards in the Gediz River Basin, West Anatolia of Turkey. These diseases can be detected early using artificial intelligence (AI)–based systems that can contribute to crop yields and also reduce the labour of the farmer and the amount of pesticides used. This article presents a dataset – namely, Hermos – for use in such AI-based systems. Hermos contains four classes of grape leaf images: leaves with powdery mildew, leaves with dead arm, leaves with downy mildew and healthy leaves. We have currently 492 images and 13,913 labels in the dataset. We have published Hermos in the Linked Open Data (LOD) cloud in order to make it easier for consumers to access, process and manipulate the data.

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