Log in

Relevant bibliographies by topics / CSE-CIC-IDS2018

Contents

Journal articles
Book chapters
Conference papers

Academic literature on the topic 'CSE-CIC-IDS2018'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'CSE-CIC-IDS2018.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "CSE-CIC-IDS2018"

1

NIKITENKO, Andrii, and Yevhen SOBOL. "AN EFFECTIVE MODEL FOR DETECTING NETWORK INTRUSIONS USING MACHINE LEARNING METHODS." cientific papers of Donetsk National Technical University. Series: Informatics, Cybernetics and Computer Science 2, no. 39 (2024): 57–65. https://doi.org/10.31474/1996-1588-2024-2-39-57-65.

Full text

Abstract:

The article is devoted to the study of machine learning algorithms used to create network intrusion detection systems. As part of the study, we reviewed the most common machine learning algorithms and tested their effectiveness on two datasets: NSL-KDD and CSE-CIC-IDS2018. When working with the NSL-KDD dataset, the test dataset was used to evaluate the accuracy, while for the CSE-CIC-IDS2018 dataset, the data was divided into training (80%) and test (20%). Studies have shown that data preprocessing significantly affects the final accuracy of models and can improve the performance of even tradi

APA, Harvard, Vancouver, ISO, and other styles

2

Ghani, Ameer A., and Suad A. Alasadi. "A Deep Learning Algorithm to Cybersecurity: Enhancing Intrusion Detection with a Hybrid GRU and BiLSTM Model." Engineering, Technology & Applied Science Research 15, no. 3 (2025): 23605–12. https://doi.org/10.48084/etasr.10666.

Full text

Abstract:

Cyber security in networks and Internet of Things (IoT) environments is becoming complex with the evolution of sophisticated cyberattacks, and the existence of effective Intrusion Detection Systems (IDSs) is necessary. This work proposes a Network-based Intrusion Detection System (NIDS) for a hybrid Deep Learning (DL) model with Gated Recurrent Units (GRU) and Bidirectional Long Short-Term Memory (BiLSTM) to improve attack detection and classification. Pre-processing of datasets, feature selection with Pearson Correlation Coefficient (PCC), and training-testing with two benchmark datasets, CSE

APA, Harvard, Vancouver, ISO, and other styles

3

Kamal, Hesham, and Maggie Mashaly. "Combined Dataset System Based on a Hybrid PCA–Transformer Model for Effective Intrusion Detection Systems." AI 6, no. 8 (2025): 168. https://doi.org/10.3390/ai6080168.

Full text

Abstract:

With the growing number and diversity of network attacks, traditional security measures such as firewalls and data encryption are no longer sufficient to ensure robust network protection. As a result, intrusion detection systems (IDSs) have become a vital component in defending against evolving cyber threats. Although many modern IDS solutions employ machine learning techniques, they often suffer from low detection rates and depend heavily on manual feature engineering. Furthermore, most IDS models are designed to identify only a limited set of attack types, which restricts their effectiveness

APA, Harvard, Vancouver, ISO, and other styles

4

Al Hwaitat, Ahmad K., and Hussam N. Fakhouri. "Adaptive Cybersecurity Neural Networks: An Evolutionary Approach for Enhanced Attack Detection and Classification." Applied Sciences 14, no. 19 (2024): 9142. http://dx.doi.org/10.3390/app14199142.

Full text

Abstract:

The increasing sophistication and frequency of cyber threats necessitate the development of advanced techniques for detecting and mitigating attacks. This paper introduces a novel cybersecurity-focused Multi-Layer Perceptron (MLP) trainer that utilizes evolutionary computation methods, specifically tailored to improve the training process of neural networks in the cybersecurity domain. The proposed trainer dynamically optimizes the MLP’s weights and biases, enhancing its accuracy and robustness in defending against various attack vectors. To evaluate its effectiveness, the trainer was tested o

APA, Harvard, Vancouver, ISO, and other styles

5

Abedzadeh, Najmeh, and Matthew Jacobs. "A Reinforcement Learning Framework with Oversampling and Undersampling Algorithms for Intrusion Detection System." Applied Sciences 13, no. 20 (2023): 11275. http://dx.doi.org/10.3390/app132011275.

Full text

Abstract:

Intrusion detection systems (IDSs) play a pivotal role in safeguarding networks and systems against malicious activities. However, the challenge of imbalanced datasets significantly impacts IDS research, skewing learning models towards the majority class and diminishing accuracy for the minority class. This study introduces the Reinforcement Learning (RL) Framework with Oversampling and Undersampling Algorithm (RLFOUA) to address imbalanced datasets. RLFOUA combines RL with diverse resampling algorithms, creating an adaptive learning environment. It integrates the novel True False Rate Synthet

APA, Harvard, Vancouver, ISO, and other styles

6

Hassan, Sardar KH, and Muhammadamin A. Daneshwar. "Anomaly-based Network Intrusion Detection System using Deep Intelligent Technique." Polytechnic Journal 12, no. 2 (2023): 100–113. http://dx.doi.org/10.25156/ptj.v12n2y2022.pp100-113.

Full text

Abstract:

Background and objectives: Computer systems and network infrastructures are still exposed to many security risks and cyber-attack vulnerabilities despite advancements of information security. Traditional signature-based intrusion detection systems and security solutions by matching rule-based mechanism and prior knowledge are insufficient of fully protecting computer networks against novel attacks. For this purpose, Anomaly-based Network Intrusion Detection System (A-NIDS) as cyber security tool is considered for identifying and detecting anomalous behavior in the flow-based network traffic al

APA, Harvard, Vancouver, ISO, and other styles

7

Li, Jing, Wei Zong, Yang-Wai Chow, and Willy Susilo. "Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs." Future Internet 17, no. 5 (2025): 216. https://doi.org/10.3390/fi17050216.

Full text

Abstract:

Network Intrusion Detection Systems (NIDS) often suffer from severe class imbalance, where minority attack types are underrepresented, leading to degraded detection performance. To address this challenge, we propose a novel augmentation framework that integrates Soft Nearest Neighbor Loss (SNNL) into Generative Adversarial Networks (GANs), including WGAN, CWGAN, and WGAN-GP. Unlike traditional oversampling methods (e.g., SMOTE, ADASYN), our approach improves feature-space alignment between real and synthetic samples, enhancing classifier generalization on rare classes. Experiments on NSL-KDD,

APA, Harvard, Vancouver, ISO, and other styles

8

D’hooge, Laurens, Miel Verkerken, Tim Wauters, Filip De Turck, and Bruno Volckaert. "Characterizing the Impact of Data-Damaged Models on Generalization Strength in Intrusion Detection." Journal of Cybersecurity and Privacy 3, no. 2 (2023): 118–44. http://dx.doi.org/10.3390/jcp3020008.

Full text

Abstract:

Generalization is a longstanding assumption in articles concerning network intrusion detection through machine learning. Novel techniques are frequently proposed and validated based on the improvement they attain when classifying one or more of the existing datasets. The necessary follow-up question of whether this increased performance in classification is meaningful outside of the dataset(s) is almost never investigated. This lacuna is in part due to the sparse dataset landscape in network intrusion detection and the complexity of creating new data. The introduction of two recent datasets, n

APA, Harvard, Vancouver, ISO, and other styles

9

Niu, Yingchun, Chengdong Chen, Xuehua Zhang, Xiaoguang Zhou, and Hongjie Liu. "Application of a New Feature Generation Algorithm in Intrusion Detection System." Wireless Communications and Mobile Computing 2022 (January 31, 2022): 1–17. http://dx.doi.org/10.1155/2022/3794579.

Full text

Abstract:

The intrusion detection system is designed to discover the abnormal behavior of the network system, but it has the problems of low detection accuracy, inability to perform fine detection, and huge time cost. Therefore, it is necessary to design a fast and accurate intrusion detection system. Therefore, this paper proposes a multigranularity feature generation + XGBOOST method to improve the intrusion detection system. First, we propose a multigranularity feature generation algorithm, which converts all features into discrete features with different numbers of categories. Different numbers of c

APA, Harvard, Vancouver, ISO, and other styles

10

Fedorova, V. S., and V. V. Strigunov. "Solving One Problem of Network Traffic Anomaly Detection Using a Convolutional Neural Network." Вестник ТОГУ, no. 2(73) (June 24, 2024): 71–82. http://dx.doi.org/10.38161/1996-3440-2024-2-71-82.

Full text

Abstract:

Detecting network attacks by anomaly search method is to identify behaviors that deviate from established baseline parameters, signaling potential security incidents. In this paper, the authors consider the application of convolutional neural network for network traffic anomaly detection. As part of the study, a convolutional neural network has been developed, trained on the dataset CICIDS2017 dataset and quality assessment has been carried out. Based on the developed neural network, a prototype for anomaly detection in network traffic has been built. Testing and quality assessment of the prot

APA, Harvard, Vancouver, ISO, and other styles

More sources

Book chapters on the topic "CSE-CIC-IDS2018"

1

Elhanashi, Abdussalam, Kaouther Gasmi, Andrea Begni, Pierpaolo Dini, Qinghe Zheng, and Sergio Saponara. "Machine Learning Techniques for Anomaly-Based Detection System on CSE-CIC-IDS2018 Dataset." In Lecture Notes in Electrical Engineering. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30333-3_17.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Celik, Yuksel, Erdal Basaran, and Sanjay Goel. "Deep Learning Methods for Intrusion Detection Systems on the CSE-CIC-IDS2018 Dataset: A Review." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-89363-6_3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Alozie, Chisom Elizabeth. "Performance metrics analysis: Evaluating machine learning models in the detection of cloud-based DDoS." In Deep Science Publishing. Deep Science Publishing, 2025. https://doi.org/10.70593/978-93-49307-78-0_4.

Full text

Abstract:

Tables 10 and 11 show the performance metric result of this experiment. The metrics used to evaluate the machine learning models are accuracy, precision, recall, f1-score, and computation time on new datasets and open-source datasets as shown in Tables 10 and 11. An 80:20 split of the overall dataset was used for the Model building where 80% was used for training and 20% was used for validation and testing. The objective of the evaluation is to assess the effectiveness of DDoS datasets in terms of their ability to detect DDoS attacks in the cloud system. The results demonstrate that the new da

APA, Harvard, Vancouver, ISO, and other styles

4

Gasu, Daniel K., Winfred Yaokumah, and Justice Kwame Appati. "Supervised Machine Learning Methods for Cyber Threat Detection Using Genetic Algorithm." In Advances in Linguistics and Communication Studies. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-7702-1.ch002.

Full text

Abstract:

Security threats continue to pose enormous challenges to network and applications security, particularly with the emerging IoT technologies and cloud computing services. Current intrusion and threat detection schemes still experience low detection rates and high rates of false alarms. In this study, an optimal set of features were extracted from CSE-CIC-IDS2018 using genetic algorithm. Machine learning algorithms, including random forest, support vector machines, logistic regression, gradient boosting, and naïve bayes were employed for classification and the results compared. Evaluation of the

APA, Harvard, Vancouver, ISO, and other styles

5

Alozie, Chisom Elizabeth. "Feature selection and machine learning model optimization for DDoS detection." In Deep Science Publishing. Deep Science Publishing, 2025. https://doi.org/10.70593/978-93-49307-78-0_3.

Full text

Abstract:

In this chapter, the Implementation design is divided into two sections, the system setup design, and the machine learning flow process design. Both are explained in detail in the section. The dataset used for the experiment was obtained from the open-source database CSE-CIC-IDS2018 (‘IDS 2018’ 2022) and the generated data set. The open-source dataset was used to train a large data set that contained 300967 instances of benign and DDoS datasets while the generated data set contained 28, 972 instances of benign and DDoS datasets. The dataset contained many fields in which “32 out of 80” feature

APA, Harvard, Vancouver, ISO, and other styles

6

Sharath, T., and A. Muthukumaravel. "Deep Learning-Powered Intrusion Detection Systems Networks Using LSTM." In Advances in Computer and Electrical Engineering. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-3739-4.ch006.

Full text

Abstract:

Novel methods for intrusion detection systems (IDS) are essential to safeguard network environments against cyber-attacks effectively. Traditional intrusion detection systems struggle to handle modern cyber threats due to their complex patterns. This study suggests implementing an intrusion detection system that utilizes long short-term memory (LSTM) networks to tackle this issue. Identifying network traffic patterns with temporal correlations poses a challenge for intrusion detection systems (IDS) due to limitations in current models. When dealing with sequential data like flow timings, packe

APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "CSE-CIC-IDS2018"

1

Ibrahimi, Khalil, Mohammed Jouhari, and Zineb Jakout. "Enhancing Intrusion Detection Systems Using Machine Learning Classifiers on the CSE-CIC-IDS2018 Dataset." In 2024 11th International Conference on Wireless Networks and Mobile Communications (WINCOM). IEEE, 2024. http://dx.doi.org/10.1109/wincom62286.2024.10655131.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Yarram, Srimaan. "An Optimized Deep Learning Approach for Intrusion Detection: AE-DBN Hybrid Model with Dingo Feature Selection on CSE-CIC-IDS2018." In 2025 International Conference on Computing for Sustainability and Intelligent Future (COMP-SIF). IEEE, 2025. https://doi.org/10.1109/comp-sif65618.2025.10969957.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Saidane, Samia, Francesco Telch, Kussai Shahin, and Fabrizio Granelli. "Optimizing Intrusion Detection System Performance through Synergistic Hyperparameter Tuning and Advanced Data Processing." In 11th International Conference on Computer Science, Engineering and Information Technology. Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.141411.

Full text

Abstract:

Intrusion detection is vital for securing computer networks against malicious activities. Traditional methods struggle to detect complex patterns and anomalies in network traffic effectively. To address this issue, we propose a system combining deep learning, data balancing (K-means + SMOTE), high-dimensional reduction (PCA and FCBF), and hyperparameter optimization (Extra Trees and BO-TPE) to enhance intrusion detection performance. By training on extensive datasets like CIC IDS 2018 and CIC IDS 2017, our models demonstrate robust performance and generalization. Notably, the ensemble model "V

APA, Harvard, Vancouver, ISO, and other styles

4

Borisenko, B. B., S. D. Erokhin, I. D. Martishin, and A. S. Fadeev. "How the CSE-CIC-IDS2018 Dataset is Related to the MITRE Matrix." In 2022 Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO). IEEE, 2022. http://dx.doi.org/10.1109/synchroinfo55067.2022.9840920.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Khan, Minhaj, and Mohd Haroon. "Artificial Neural Network-based Intrusion Detection in Cloud Computing using CSE-CIC-IDS2018 Datasets." In 2023 3rd Asian Conference on Innovation in Technology (ASIANCON). IEEE, 2023. http://dx.doi.org/10.1109/asiancon58793.2023.10269948.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Ayachi, Yassine, Youssef Mellah, Jamal Berrich, and Toumi Bouchentouf. "Increasing the Performance of an IDS using ANN model on the realistic cyber dataset CSE-CIC-IDS2018." In 2020 International Symposium on Advanced Electrical and Communication Technologies (ISAECT). IEEE, 2020. http://dx.doi.org/10.1109/isaect50560.2020.9523662.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Smith, Morgan L., and Tor A. Kwembe. "Application of Machine Learning Classifiers Interfacing Google Colab and Sklearn to Intrusion Detection CSE-CIC-IDS2018 Dataset." In 2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE). IEEE, 2023. http://dx.doi.org/10.1109/csce60160.2023.00311.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Kumar, Abhishek, and Dilkeshwar Pandey. "Enhancing intrusion detection with ML and deep learning: A survey of CICIDS 2017 and CSE-CIC-IDS2018 datasets." In 1ST INTERNATIONAL CONFERENCE ON RECENT ADVANCEMENTS IN COMPUTING TECHNOLOGIES & ENGINEERING. AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0222131.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Neira, Anderson Bergamini, Alex Medeiro, and Michele Nogueira. "Identificação Antecipada de Botnets por Aprendizagem de Máquina." In Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/sbrc.2020.12333.

Full text

Abstract:

O envio de spam, o roubo de dados pessoais e o ataque de negação de serviço são exemplos de ações resultantes da exploração de vulnerabilidades em dispositivos inseguros conectados à Internet. A constante evolução dos ataques, o aumento na quantidade de dispositivos vulneráveis devido à Internet das Coisas (IoT) e os elevados custos com os danos causados reforçam a necessidade de antecipar a ação de redes de dispositivos infectados (bots) geradoras de ataques. Neste contexto, os algoritmos de aprendizagem de máquina são relevantes para identificar essas redes, pois oferecem adaptação e tratame

APA, Harvard, Vancouver, ISO, and other styles

10

Kanimozhi, V., and T. Prem Jacob. "Artificial Intelligence based Network Intrusion Detection with Hyper-Parameter Optimization Tuning on the Realistic Cyber Dataset CSE-CIC-IDS2018 using Cloud Computing." In 2019 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2019. http://dx.doi.org/10.1109/iccsp.2019.8698029.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!