Impact of Convolutional Neural Network Input Parameters on Classification Performance

Maitra, Sanjit; Ojha, Rahul Kumar; Ghosh, Kuntal

doi:10.1109/i2ct42659.2018.9058213

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…The impact of these parameters on the accuracy of classification and the time required to train the network is assessed. [36]. In this application scenario, non-essential experimental parameters should be excluded based on the model design rules and actual experimental results.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Anatomy Identification of Bamboo Stems with The Convolutional Neural Networks (CNN) Method

Rustandi,

Sony Hartono Wijaya,

Mushthofa

et al. 2024

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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It is important to note that some species of bamboo are protected and considered endangered. However, distinguishing between traded and protected bamboo species or differentiating between bamboo species for various purposes remains a challenge. This requires specialized skills to identify the type of bamboo, and currently, the process can only be carried out in the forest for bamboo that is still in clump form by experienced researchers or officers. However, a study has been conducted to develop an easier and faster method of identifying bamboo species. The study aims to create an automatic identification system for bamboo stems based on their anatomical structure (ASINABU). The bamboo identification algorithm was developed using macroscopic images of cross-sectioned bamboo stems and the research method used was the convolutional neural network (CNN). CNN was designed to identify bamboo species with images taken using a cellphone camera equipped with a lens. The final product is an Android automatic identification application that can detect bamboo species with an accuracy of 99.9%.

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Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Anatomy Identification of Bamboo Stems with The Convolutional Neural Networks (CNN) Method

Rustandi,

Sony Hartono Wijaya,

Mushthofa

et al. 2024

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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“…Network security refers to the different mechanisms and techniques to prevent unauthorized access to digital assets in a network environment. Its main objective is to establish a set of practices that comply with the CIA triad, which stands for confidentiality, integrity, and availability and is the foundation of any security program in an organization [10][11][12]. This paper is organized as follows: Section 3 describes the dataset, Section 4 presents related work, Section 5 explains the research methodology, Section 6 covers the experimental results and analysis, and Section 7 concludes with future work recommendations.…”

Section: Network Securitymentioning

confidence: 99%

Deep Learning-based DDoS Detection in Network Traffic Data

Hussein Hadi

2024

Int. j. electr. comput. eng. syst. (Online)

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In today's society, the cloud is essential for communication since it allows access to important information anytime and anywhere. However, cloud services also attract hackers who want to exploit online details. This has caused significant changes in the cyber-attack landscape. Distributed Denial of Service (DDoS) is the most common attack. Traditional tools like firewalls and encryption can mitigate these risks, but new models are needed to cope with the changing nature of cyber-attacks. Detecting DDoS attacks is particularly challenging since network traffic data is complex and often contains unnecessary features. To address this, a new approach is proposed using Denoising AutoEncoder (DAE) and a Convolutional Neural Network (CNN) for feature selection and classification. The NSL-KDD dataset is used to evaluate the performance of this new model with three main steps: Data Pre- processing, Hyper-parameter Optimization, and Classification. Our method performed better in all four metrics, such as Accuracy, Recall, Precision, and F1-score, with rates of 97.7, 98.1, 97.7, and 97.8, respectively. The multiclass classification detection rate for DOS was 100%. Similarly, the detection rates for Probe, R2L, and U2R were 98%, 95%, and 80%, respectively. Python version 3.6 with Keras 2.2.4 and TensorFlow Engine was used in this paper.

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“…The number of convolution kernels and learning rate directly affect classification results in a CNN-based model [27,28] . We performed experiments on several convolution kernels with different learning rates to obtain a better result.…”

Section: Classificationmentioning

confidence: 99%

Network Intrusion Detection with 1D Convolutional Neural Networks

Hooshmand

Huchaiah

2022

digit. tech. res. and appl.

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Computer network assets expose to various cyber threats in today’s digital era. Network Anomaly Detection Systems (NADS) play a vital role in protecting digital assets in the purview of network security. Intrusion detection systems data are imbalanced and high dimensioned, affecting models’ performance in classifying malicious traffic. This paper uses a denoising autoencoder (DAE) for feature selection to reduce data dimension. To balance the data, the authors use a combined approach of oversampling technique, adaptive synthetic (ADASYN) and a cluster-based under-sampling method using a clustering algorithm, Kmeans. Then, a one-dimensional convolutional neural network (1D-CNN) is used to perform classification. The performance of the proposed model is evaluated on UNSW-NB15 and NSL-KDD datasets. The experimental results show that the model produces a detection rate of 98.79% and 97.23% on UNSW-NB15 for binary classification and multiclass classification, respectively. In the evaluation using NSL-KDD, the model yields a detection rate of 98.52% for binary type classification and 98.16% for multiclass type classification.

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Impact of Convolutional Neural Network Input Parameters on Classification Performance

Cited by 5 publications

References 10 publications

Anatomy Identification of Bamboo Stems with The Convolutional Neural Networks (CNN) Method

Anatomy Identification of Bamboo Stems with The Convolutional Neural Networks (CNN) Method

Deep Learning-based DDoS Detection in Network Traffic Data

Network Intrusion Detection with 1D Convolutional Neural Networks

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