Relevant bibliographies by topics / CSE-CIC-IDS-2018

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Academic literature on the topic 'CSE-CIC-IDS-2018'

Author: Grafiati
Published: 5 June 2025
Last updated: 8 July 2025

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Journal articles on the topic "CSE-CIC-IDS-2018"

1

Chimphlee, Witcha, and Siriporn Chimphlee. "Hyperparameters optimization XGBoost for network intrusion detection using CSE-CIC-IDS 2018 dataset." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 817. http://dx.doi.org/10.11591/ijai.v13.i1.pp817-826.

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<p>With the introduction of high-speed internet access, the demand for security and dependable networks has grown. In recent years, network attacks have gotten more complex and intense, making security a vital component of organizational information systems. Network intrusion detection systems (NIDS) have become an essential detection technology to protect data integrity and system availability against such attacks. NIDS is one of the most well-known areas of machine learning software in the security field, with machine learni ng algorithms constantly being developed to improve performance. This research focuses on detecting abnormalities in societal infiltration using the hyperparameters optimization XGBoost (HO-XGB) algorithm with the Communications Security Establishment-The Canadian Institute for Cybersecurity-Intrusion Detection System2018 (CSE-CIC-IDS2018) dataset to get the best potential results. When compared to typical machine learning methods published in the literature, HO-XGB outperforms them. The study shows that XGBoost outperforms other detection algorithms. We refined the HO-XGB model's hyperparameters, which included learning_rate, subsample, max_leaves, max_depth, gamma, colsample_bytree, min_child_weight, n_estimators, max_depth, and reg_alpha. The experimental findings reveal that HO-XGB1 outperforms multiple parameter settings for intrusion detection, effectively optimizing XGBoost's hyperparameters.</p>
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Chimphlee, Witcha, and Siriporn Chimphlee. "Hyperparameters optimization XGBoost for network intrusion detection using CSE-CIC-IDS 2018 dataset." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 817–26. https://doi.org/10.11591/ijai.v13.i1.pp817-826.

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With the introduction of high-speed internet access, the demand for security and dependable networks has grown. In recent years, network attacks have gotten more complex and intense, making security a vital component of organizational information systems. Network intrusion detection systems (NIDS) have become an essential detection technology to protect data integrity and system availability against such attacks. NIDS is one of the most well-known areas of machine learning software in the security field, with machine learni ng algorithms constantly being developed to improve performance. This research focuses on detecting abnormalities in societal infiltration using the hyperparameters optimization XGBoost (HO-XGB) algorithm with the Communications Security Establishment-The Canadian Institute for Cybersecurity-Intrusion Detection System2018 (CSE-CICIDS2018) dataset to get the best potential results. When compared to typical machine learning methods published in the literature, HO-XGB outperforms them. The study shows that XGBoost outperforms other detection algorithms. We refined the HO-XGB model's hyperparameters, which included learning_rate, subsample, max_leaves, max_depth, gamma, colsample_bytree, min_child_weight, n_estimators, max_depth, and reg_alpha. The experimental findings reveal that HO-XGB1 outperforms multiple parameter settings for intrusion detection, effectively optimizing XGBoost's hyperparameters.
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Songma, Surasit, Theera Sathuphan, and Thanakorn Pamutha. "Optimizing Intrusion Detection Systems in Three Phases on the CSE-CIC-IDS-2018 Dataset." Computers 12, no. 12 (2023): 245. http://dx.doi.org/10.3390/computers12120245.

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This article examines intrusion detection systems in depth using the CSE-CIC-IDS-2018 dataset. The investigation is divided into three stages: to begin, data cleaning, exploratory data analysis, and data normalization procedures (min-max and Z-score) are used to prepare data for use with various classifiers; second, in order to improve processing speed and reduce model complexity, a combination of principal component analysis (PCA) and random forest (RF) is used to reduce non-significant features by comparing them to the full dataset; finally, machine learning methods (XGBoost, CART, DT, KNN, MLP, RF, LR, and Bayes) are applied to specific features and preprocessing procedures, with the XGBoost, DT, and RF models outperforming the others in terms of both ROC values and CPU runtime. The evaluation concludes with the discovery of an optimal set, which includes PCA and RF feature selection.
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Shyaa, Methaq A., Zurinahni Zainol, Rosni Abdullah, Mohammed Anbar, Laith Alzubaidi, and José Santamaría. "Enhanced Intrusion Detection with Data Stream Classification and Concept Drift Guided by the Incremental Learning Genetic Programming Combiner." Sensors 23, no. 7 (2023): 3736. http://dx.doi.org/10.3390/s23073736.

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Concept drift (CD) in data streaming scenarios such as networking intrusion detection systems (IDS) refers to the change in the statistical distribution of the data over time. There are five principal variants related to CD: incremental, gradual, recurrent, sudden, and blip. Genetic programming combiner (GPC) classification is an effective core candidate for data stream classification for IDS. However, its basic structure relies on the usage of traditional static machine learning models that receive onetime training, limiting its ability to handle CD. To address this issue, we propose an extended variant of the GPC using three main components. First, we replace existing classifiers with alternatives: online sequential extreme learning machine (OSELM), feature adaptive OSELM (FA-OSELM), and knowledge preservation OSELM (KP-OSELM). Second, we add two new components to the GPC, specifically, a data balancing and a classifier update. Third, the coordination between the sub-models produces three novel variants of the GPC: GPC-KOS for KA-OSELM; GPC-FOS for FA-OSELM; and GPC-OS for OSELM. This article presents the first data stream-based classification framework that provides novel strategies for handling CD variants. The experimental results demonstrate that both GPC-KOS and GPC-FOS outperform the traditional GPC and other state-of-the-art methods, and the transfer learning and memory features contribute to the effective handling of most types of CD. Moreover, the application of our incremental variants on real-world datasets (KDD Cup ‘99, CICIDS-2017, CSE-CIC-IDS-2018, and ISCX ‘12) demonstrate improved performance (GPC-FOS in connection with CSE-CIC-IDS-2018 and CICIDS-2017; GPC-KOS in connection with ISCX2012 and KDD Cup ‘99), with maximum accuracy rates of 100% and 98% by GPC-KOS and GPC-FOS, respectively. Additionally, our GPC variants do not show superior performance in handling blip drift.
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International, Journal for Research In Science &. Advanced Technologies. "Cloud Computing Environment: An Effective New Intrusion Detection System." International Journal for Research In Science & Advanced Technologies 25, no. 05 (2025): 33–42. https://doi.org/10.5281/zenodo.15597744.

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Rife acceptance of Cloud Computing has made it bull’s eye for the hackers. Intrusion detection System (IDS) plays a vibrant role for it. Researchers have done marvelous works on the development of a competence IDS. But there are many challenges still exists with IDS. One of the biggest concerns is that the computational complexity and false alarms of the IDS escalates with the increase in the number of features or attributes of the dataset. Hence, the concept of Feature Selection (FS) contributes an all-important role for the buildout of an efficacious IDS. New FS algorithm is put forward which is the modified Firefly Algorithm in which Decision Tree (DT) classifier is used as the classification function. We have used the hybrid classifier which is the combination of neural network and DT. We have used CSE CIC IDS 2018 dataset and simulated dataset for performance assessment. Our examination pragmatic that the performance of proposed architecture is better than the state-of-the-art algorithms.
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Abuali, Khadija M., Liyth Nissirat, and Aida Al-Samawi. "Advancing Network Security with AI: SVM-Based Deep Learning for Intrusion Detection." Sensors 23, no. 21 (2023): 8959. http://dx.doi.org/10.3390/s23218959.

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With the rapid growth of social media networks and internet accessibility, most businesses are becoming vulnerable to a wide range of threats and attacks. Thus, intrusion detection systems (IDSs) are considered one of the most essential components for securing organizational networks. They are the first line of defense against online threats and are responsible for quickly identifying potential network intrusions. Mainly, IDSs analyze the network traffic to detect any malicious activities in the network. Today, networks are expanding tremendously as the demand for network services is expanding. This expansion leads to diverse data types and complexities in the network, which may limit the applicability of the developed algorithms. Moreover, viruses and malicious attacks are changing in their quantity and quality. Therefore, recently, several security researchers have developed IDSs using several innovative techniques, including artificial intelligence methods. This work aims to propose a support vector machine (SVM)-based deep learning system that will classify the data extracted from servers to determine the intrusion incidents on social media. To implement deep learning-based IDSs for multiclass classification, the CSE-CIC-IDS 2018 dataset has been used for system evaluation. The CSE-CIC-IDS 2018 dataset was subjected to several preprocessing techniques to prepare it for the training phase. The proposed model has been implemented in 100,000 instances of a sample dataset. This study demonstrated that the accuracy, true-positive recall, precision, specificity, false-positive recall, and F-score of the proposed model were 100%, 100%, 100%, 100%, 0%, and 100%, respectively.
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Gutiérrez-Galeano, Leopoldo, Juan-José Domínguez-Jiménez, Jörg Schäfer, and Inmaculada Medina-Bulo. "LLM-Based Cyberattack Detection Using Network Flow Statistics." Applied Sciences 15, no. 12 (2025): 6529. https://doi.org/10.3390/app15126529.

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Cybersecurity is a growing area of research due to the constantly emerging new types of cyberthreats. Tools and techniques exist to keep systems secure against certain known types of cyberattacks, but are insufficient for others that have recently appeared. Therefore, research is needed to design new strategies to deal with new types of cyberattacks as they arise. Existing tools that harness artificial intelligence techniques mainly use artificial neural networks designed from scratch. In this paper, we present a novel approach for cyberattack detection using an encoder–decoder pre-trained Large Language Model (T5), fine-tuned to adapt its classification scheme for the detection of cyberattacks. Our system is anomaly-based and takes statistics of already finished network flows as input. This work makes significant contributions by introducing a novel methodology for adapting its original task from natural language processing to cybersecurity, achieved by transforming numerical network flow features into a unique abstract artificial language for the model input. We validated the robustness of our detection system across three datasets using undersampling. Our model achieved consistently high performance across all evaluated datasets. Specifically, for the CIC-IDS-2017 dataset, we obtained an accuracy, precision, recall, and F-score of more than 99.94%. For CSE-CIC-IDS-2018, these metrics exceeded 99.84%, and for BCCC-CIC-IDS-2017, they were all above 99.90%. These results collectively demonstrate superior performance for cyberattack detection, while maintaining highly competitive false-positive rates and false-negative rates. This efficacy is achieved by relying exclusively on real-world network flow statistics, without the need for synthetic data generation.
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R M, Balajee, and Jayanthi Kannan M K. "Intrusion Detection on AWS Cloud through Hybrid Deep Learning Algorithm." Electronics 12, no. 6 (2023): 1423. http://dx.doi.org/10.3390/electronics12061423.

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The network security and cloud environment have been playing vital roles in today’s era due to increased network data transmission, the cloud’s elasticity, pay as you go and global distributed resources. A recent survey for the cloud environment involving 300 organizations in North America with 500 or more employees who had spent a minimum of USD 1 million on cloud infrastructure, as per March 2022 statistics, stated that 79% of organizations experienced at least one cloud data breach. In the year 2022, the AWS cloud provider leads the market share with 34% and a USD 200 billion cloud market, proving important and producing the motivation to improve the detection of intrusion with respect to network security on the basis of the AWS cloud dataset. The chosen CSE-CIC-IDS-2018 dataset had network attack details based on the real time attack carried out on the AWS cloud infrastructure. The proposed method here is the hybrid deep learning based approach, which uses the raw data first to do the pre-processing and then for normalization. The normalized data have been feature extracted from seventy-six fields to seven bottlenecks using Principal Component Analysis (PCA); those seven extracted features of every packet have been categorized as two-way soft-clustered (attack and non-attack) using the Smart Monkey Optimized Fuzzy C-Means algorithm (SMO-FCM). The attack cluster data have been further provided as inputs for the deep learning based AutoEncoder algorithm, which provides the outputs as attack classifications. Finally, the accuracy of the results in intrusion detection using the proposed technique (PCA + SMO-FCM + AE) is achieved as 95% over the CSE-CIC-IDS-2018 dataset, which is the highest known for state-of-the-art protocols compared with 11 existing techniques.
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Dini, Pierpaolo, Abdussalam Elhanashi, Andrea Begni, Sergio Saponara, Qinghe Zheng, and Kaouther Gasmi. "Overview on Intrusion Detection Systems Design Exploiting Machine Learning for Networking Cybersecurity." Applied Sciences 13, no. 13 (2023): 7507. http://dx.doi.org/10.3390/app13137507.

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The Intrusion Detection System (IDS) is an effective tool utilized in cybersecurity systems to detect and identify intrusion attacks. With the increasing volume of data generation, the possibility of various forms of intrusion attacks also increases. Feature selection is crucial and often necessary to enhance performance. The structure of the dataset can impact the efficiency of the machine learning model. Furthermore, data imbalance can pose a problem, but sampling approaches can help mitigate it. This research aims to explore machine learning (ML) approaches for IDS, specifically focusing on datasets, machine algorithms, and metrics. Three datasets were utilized in this study: KDD 99, UNSW-NB15, and CSE-CIC-IDS 2018. Various machine learning algorithms were chosen and examined to assess IDS performance. The primary objective was to provide a taxonomy for interconnected intrusion detection systems and supervised machine learning algorithms. The selection of datasets is crucial to ensure the suitability of the model construction for IDS usage. The evaluation was conducted for both binary and multi-class classification to ensure the consistency of the selected ML algorithms for the given dataset. The experimental results demonstrated accuracy rates of 100% for binary classification and 99.4In conclusion, it can be stated that supervised machine learning algorithms exhibit high and promising classification performance based on the study of three popular datasets.
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Dini, Pierpaolo, Abdussalam Elhanashi, Andrea Begni, Sergio Saponara, Qinghe Zheng, and kaouther Gasmi. "Overview on Intrusion Detection Systems Design Exploiting Machine Learning for Networking Cybersecurity." Applied Sciences 13 (June 25, 2023): 13. https://doi.org/10.3390/app13137507.

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The Intrusion Detection System (IDS) is an effective tool utilized in cybersecurity systems to detect and identify intrusion attacks. With the increasing volume of data generation, the possibility of various forms of intrusion attacks also increases. Feature selection is crucial and often necessary to enhance performance. The structure of the dataset can impact the efficiency of the machine learning model. Furthermore, data imbalance can pose a problem, but sampling approaches can help mitigate it. This research aims to explore machine learning (ML) approaches for IDS, specifically focusing on datasets, machine algorithms, and metrics. Three datasets were utilized in this study: KDD 99, UNSW-NB15, and CSE-CIC-IDS 2018. Various machine learning algorithms were chosen and examined to assess IDS performance. The primary objective was to provide a taxonomy for interconnected intrusion detection systems and supervised machine learning algorithms. The selection of datasets is crucial to ensure the suitability of the model construction for IDS usage. The evaluation was conducted for both binary and multi-class classification to ensure the consistency of the selected ML algorithms for the given dataset. The experimental results demonstrated accuracy rates of 100% for binary classification and 99.4In conclusion, it can be stated that supervised machine learning algorithms exhibit high and promising classification performance based on the study of three popular datasets.
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Book chapters on the topic "CSE-CIC-IDS-2018"

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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.

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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” features were used for the open data set and “31 out of 84” was used for the newly generated dataset. A comparative study of supervised machine learning algorithms which will be used to predict the accuracy and clarity of how DDoS attacks are detected in the cloud will be presented and evaluated in terms of performance and accuracy.
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Conference papers on the topic "CSE-CIC-IDS-2018"

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Liu, Lisa, Gints Engelen, Timothy Lynar, Daryl Essam, and Wouter Joosen. "Error Prevalence in NIDS datasets: A Case Study on CIC-IDS-2017 and CSE-CIC-IDS-2018." In 2022 IEEE Conference on Communications and Network Security (CNS). IEEE, 2022. http://dx.doi.org/10.1109/cns56114.2022.9947235.

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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.

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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 "VGG19" consistently achieves remarkable accuracy (99.26% on CIC-IDS2017 and 99.22% on CSE-CIC-IDS2018), outperforming other models.
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Nisha, T. N., and Dhanya Pramod. "Sequential event-based detection of network attacks on CSE CIC IDS 2018 data set – Application of GSP and IPAM Algorithm." In 2022 International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS). IEEE, 2022. http://dx.doi.org/10.1109/ic3sis54991.2022.9885438.

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