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Relevant bibliographies by topics / CIC-DDoS2019 dataset

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Academic literature on the topic 'CIC-DDoS2019 dataset'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "CIC-DDoS2019 dataset"

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Deris Stiawan, Deris Stiawan, Dimas Wahyudi Deris Stiawan, Tri Wanda Septian Dimas Wahyudi, Mohd Yazid Idris Tri Wanda Septian, and Rahmat Budiarto Mohd Yazid Idris. "The Development of an Internet of Things (IoT) Network Traffic Dataset with Simulated Attack Data." 網際網路技術學刊 24, no. 2 (2023): 345–56. http://dx.doi.org/10.53106/160792642023032402013.

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<p>Due to the complexity and multifaceted nature of Internet of Things (IoT) networks/systems, researchers in the field of IoT network security complain about the rareness of real life-based datasets and the limitation of heterogeneous of communication protocols used in the datasets. There are a number of datasets publicly available such as DARPA, Twente, ISCX2012, ADFA, CIC-IDS2017, CSE-CIC-IDS2018, CIC-DDOS2019, MQTT-IoT-IDS-2020, and UNSW-NB15 that have been used by researchers to evaluate performance of the Intrusion Detection Systems (IDSs), nevertheless, the datasets creation are n

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Zaki, Rana M., and Inam S. Naser. "Hybrid Classifier for Detecting Zero-Day Attacks on IoT Networks." Mesopotamian Journal of CyberSecurity 4, no. 3 (2024): 59–74. http://dx.doi.org/10.58496/mjcs/2024/016.

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Recently, Internet of Things (IoT) networks have been exposed to many electronic attacks, giving rise to concerns about the security of these networks, where their weaknesses and gaps can be exploited to access or steal data. These networks are threatened by several cyberattacks, one of which is the zero-day distributed denial-of-service (DDoS) attack, which is considered one of the dangerous attacks targeting network security. As such, it is necessary to find smart solutions to address such attacks swiftly. To address these attacks, this research proposed a hybrid IDS to detect cyber-attacks

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Ma, Ruikui, Xuebin Chen, and Ran Zhai. "A DDoS Attack Detection Method Based on Natural Selection of Features and Models." Electronics 12, no. 4 (2023): 1059. http://dx.doi.org/10.3390/electronics12041059.

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Distributed Denial of Service (DDoS) is still one of the main threats to network security today. Attackers are able to run DDoS in simple steps and with high efficiency to slow down or block users’ access to services. In this paper, we propose a framework based on feature and model selection (FAMS), which is used for detecting DDoS attacks with the aim of identifying the features and models with a high generalization capability, high prediction accuracy, and short prediction time. The FAMS framework is divided into four main phases. The first phase is data pre-processing, including operations

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Ahmad, Iftikhar, Muhammad Imran, Abdul Qayyum, Muhammad Sher Ramzan, and Madini O. Alassafi. "An Optimized Hybrid Deep Intrusion Detection Model (HD-IDM) for Enhancing Network Security." Mathematics 11, no. 21 (2023): 4501. http://dx.doi.org/10.3390/math11214501.

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Detecting cyber intrusions in network traffic is a tough task for cybersecurity. Current methods struggle with the complexity of understanding patterns in network data. To solve this, we present the Hybrid Deep Learning Intrusion Detection Model (HD-IDM), a new way that combines GRU and LSTM classifiers. GRU is good at catching quick patterns, while LSTM handles long-term ones. HD-IDM blends these models using weighted averaging, boosting accuracy, especially with complex patterns. We tested HD-IDM on four datasets: CSE-CIC-IDS2017, CSE-CIC-IDS2018, NSL KDD, and CIC-DDoS2019. The HD-IDM classi

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D’hooge, Laurens, Miel Verkerken, Tim Wauters, Filip De Turck, and Bruno Volckaert. "Investigating Generalized Performance of Data-Constrained Supervised Machine Learning Models on Novel, Related Samples in Intrusion Detection." Sensors 23, no. 4 (2023): 1846. http://dx.doi.org/10.3390/s23041846.

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Recently proposed methods in intrusion detection are iterating on machine learning methods as a potential solution. These novel methods are validated on one or more datasets from a sparse collection of academic intrusion detection datasets. Their recognition as improvements to the state-of-the-art is largely dependent on whether they can demonstrate a reliable increase in classification metrics compared to similar works validated on the same datasets. Whether these increases are meaningful outside of the training/testing datasets is rarely asked and never investigated. This work aims to demons

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Ferrag, Mohamed Amine, Lei Shu, Hamouda Djallel, and Kim-Kwang Raymond Choo. "Deep Learning-Based Intrusion Detection for Distributed Denial of Service Attack in Agriculture 4.0." Electronics 10, no. 11 (2021): 1257. http://dx.doi.org/10.3390/electronics10111257.

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Smart Agriculture or Agricultural Internet of things, consists of integrating advanced technologies (e.g., NFV, SDN, 5G/6G, Blockchain, IoT, Fog, Edge, and AI) into existing farm operations to improve the quality and productivity of agricultural products. The convergence of Industry 4.0 and Intelligent Agriculture provides new opportunities for migration from factory agriculture to the future generation, known as Agriculture 4.0. However, since the deployment of thousands of IoT based devices is in an open field, there are many new threats in Agriculture 4.0. Security researchers are involved

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Xu, Hao, and Hequn Xian. "SCD: A Detection System for DDoS Attacks based on SAE-CNN Networks." Frontiers in Computing and Intelligent Systems 5, no. 3 (2023): 94–99. http://dx.doi.org/10.54097/fcis.v5i3.13865.

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The pervasive application of network technology has given rise to a numerous of network attacks, including Distributed Denial of Service (DDoS) attacks. DDoS attacks can lead to the collapse of network resources, making the target server unable to support legitimate users, which is a critical issue in cyberspace security. In complex real-world network environments, differentiating DDoS attack traffic from normal traffic is a challenging task, making it significant to effectively distinguish between attack types in order to resist DDoS attacks. However, traditional DDoS attack detection methods

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Williams, Brandon, and Lijun Qian. "Semi-Supervised Learning for Intrusion Detection in Large Computer Networks." Applied Sciences 15, no. 11 (2025): 5930. https://doi.org/10.3390/app15115930.

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In an increasingly interconnected world, securing large networks against cyber-threats has become paramount as cyberattacks become more rampant, difficult, and expensive to remedy. This research explores data-driven security by applying semi-supervised machine learning techniques for intrusion detection in large-scale network environments. Novel methods (including decision tree with entropy-based uncertainty sampling, logistic regression with self-training, and co-training with random forest) are proposed to perform intrusion detection with limited labeled data. These methods leverage both ava

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Yzzogh, Hicham, and Hafssa Benaboud. "Enhancing SDN security with a feature-based approach using multiple k-means, Word2Vec, and neural network." Bulletin of Electrical Engineering and Informatics 14, no. 2 (2025): 1456–67. https://doi.org/10.11591/eei.v14i2.8834.

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In the rapidly evolving landscape of network management, software-defined networking (SDN) stands out as a transformative technology. It revolutionizes network management by decoupling the control and data planes, enhancing both flexibility and operational efficiency. However, this separation introduces significant security challenges, such as data interception, manipulation, and unauthorized access. To address these issues, this paper investigates the application of advanced clustering and classification algorithms for anomaly detection and traffic analysis in SDN environments. We present a n

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Z, Syauqii Fayyadh Hilal, and Rushendra Rushendra. "The Efficiency of Machine Learning Techniques in Strengthening Defenses Against DDoS Attacks, Such as Random Forest, Logistic Regression, and Neural Networks." sinkron 9, no. 1 (2025): 520–30. https://doi.org/10.33395/sinkron.v9i1.14502.

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Distributed Denial of Service (DDoS) attacks are one of the most common cybersecurity concerns brought on by the quick development of digital technology. By flooding servers with too many requests, these assaults interfere with online services, highlighting the necessity of strong detection systems. Using the well-known CIC-DDoS2019 dataset, this study explores the use of machine learning algorithms—Random Forest (RF), Logistic Regression (LR), and Neural Networks (NN)—to improve DDoS assault detection. A comprehensive preprocessing procedure that comprised feature selection, normalization, an

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Book chapters on the topic "CIC-DDoS2019 dataset"

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Gondi, Lakshmeeswari, Swathi Sambangi, P. Kundana Priya, and S. Sharika Anjum. "A Machine Learning Approach for DDoS Attack Detection in CIC-DDoS2019 Dataset Using Multiple Linear Regression Algorithm." In Springer Proceedings in Mathematics & Statistics. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51167-7_38.

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Dasari, Kishore Babu, and Srinivas Mekala. "Proactive DDoS Attacks Detection on the Cloud Computing Environment Using Machine Learning Techniques." In Advances in Information Security, Privacy, and Ethics. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-9317-5.ch016.

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Distributed Denial of Service (DDoS) is a cyber-attack targeted on availability principle of information security by disrupts the services to the users. Cloud computing is very demand service in internet to provide computing resources. DDoS attack is one of the severe cyber-attack to disrupt the resource unavailable to the legitimate users. So DDoS attack detection is more essential in cloud computing environment to reduce the effect of circumstances of the attack. This Chapter proposed DDoS attack detection with network flow features instead of conventional researchers use network type featur

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