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Статті в журналах з теми "CIC-DDoS2019 dataset"

Автор: Grafiati

Опубліковано: 7 червня 2025

Оновлено: 2 серпня 2025

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1

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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Анотація:

<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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2

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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Анотація:

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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3

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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Анотація:

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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4

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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Анотація:

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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5

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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Анотація:

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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6

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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Анотація:

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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7

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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8

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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Анотація:

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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9

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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Анотація:

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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10

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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11

Zegarra Rodríguez, Demóstenes, Ogobuchi Daniel Okey, Siti Sarah Maidin, Ekikere Umoren Udo, and João Henrique Kleinschmidt. "Attentive transformer deep learning algorithm for intrusion detection on IoT systems using automatic Xplainable feature selection." PLOS ONE 18, no. 10 (2023): e0286652. http://dx.doi.org/10.1371/journal.pone.0286652.

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Анотація:

Recent years have witnessed an in-depth proliferation of the Internet of Things (IoT) and Industrial Internet of Things (IIoT) systems linked to Industry 4.0 technology. The increasing rate of IoT device usage is associated with rising security risks resulting from malicious network flows during data exchange between the connected devices. Various security threats have shown high adverse effects on the availability, functionality, and usability of the devices among which denial of service (DoS) and distributed denial of service (DDoS), which attempt to exhaust the capacity of the IoT network (

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12

Aldhyani, Theyazn H. H., and Hasan Alkahtani. "Cyber Security for Detecting Distributed Denial of Service Attacks in Agriculture 4.0: Deep Learning Model." Mathematics 11, no. 1 (2023): 233. http://dx.doi.org/10.3390/math11010233.

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Анотація:

Attackers are increasingly targeting Internet of Things (IoT) networks, which connect industrial devices to the Internet. To construct network intrusion detection systems (NIDSs), which can secure Agriculture 4.0 networks, powerful deep learning (DL) models have recently been deployed. An effective and adaptable intrusion detection system may be implemented by using the architectures of long short-term memory (LSTM) and convolutional neural network combined with long short-term memory (CNN–LSTM) for detecting DDoS attacks. The CIC-DDoS2019 dataset was used to design a proposal for detecting di

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13

Ahmed, Ahmed, Noor D. AL AL-Shakarchy, and Mais Saad Safoq. "Early DDoS Attack Detection Using Lightweight Deep Neural Network." Fusion: Practice and Applications 19, no. 2 (2025): 392–401. https://doi.org/10.54216/fpa.190228.

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In the digital age, e-commerce platforms are critical components of the global economy, facilitating seamless transactions and interactions between businesses and consumers. The digital infrastructure of these institutions is frequently attacked, either to hack or disrupt online services, leading to significant financial losses and damage to reputation. The most famous of these attacks are DDoS attacks, which lead to an increase in the volume of traffic to the platform's website beyond the capacity of the servers, thus causing the platform to respond slowly and crash and customers to be unable

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14

Kanber, Bassam M., Naglaa F. Noaman, Amr M. H. Saeed, and Mansoor Malas. "DDoS Attacks Detection in the Application Layer Using Three Level Machine Learning Classification Architecture." International Journal of Computer Network and Information Security 14, no. 3 (2022): 33–46. http://dx.doi.org/10.5815/ijcnis.2022.03.03.

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Анотація:

Distributed Denial of Service (DDoS) is an ever-changing type of attack in cybersecurity, especially with the growing demand for cloud and web services raising a never-ending challenge in the lucrative business. DDoS attacks disrupt users' access to the targeted online services leading to significant business loss. This article presents a three-level architecture for detecting DDoS attacks at the application layer. The first level is responsible for selecting the best features of the samples and classifying the traffic into either benign or malicious, then the second level consists of a hard v

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15

Soim, Sopian, Sholihin Sholihin, and Cahyo Bayu Subianto. "Optimizing Performance Random Forest Algorithm Using Correlation-Based Feature Selection (CFS) Method to Improve Distributed Denial of Service (DDoS) Attack Detection Accuracy." Indonesian Journal of Artificial Intelligence and Data Mining 7, no. 2 (2024): 220. http://dx.doi.org/10.24014/ijaidm.v7i2.24783.

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Анотація:

In the ever-evolving digital era, Distributed Denial of Service (DDoS) attacks have become a major threat to the security of networks and online services, making it important to develop effective strategies to detect and overcome such attacks.This research aims to improve the performance of Random Forest algorithm in dealing with DDoS attacks by using Correlation-Based Feature Selection method. This method can identify and select the most relevant features from the dataset used, in this case the CIC-DDoS2019 dataset, with respect to accuracy, precision, recall, and F1-score as evaluation metri

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16

Chartuni, Andrés, and José Márquez. "Multi-Classifier of DDoS Attacks in Computer Networks Built on Neural Networks." Applied Sciences 11, no. 22 (2021): 10609. http://dx.doi.org/10.3390/app112210609.

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Анотація:

The great commitment in different areas of computer science for the study of computer networks used to fulfill specific and major business tasks has generated a need for their maintenance and optimal operability. Distributed denial of service (DDoS) is a frequent threat to computer networks because of its disruption to the services they cause. This disruption results in the instability and/or inoperability of the network. There are different classes of DDoS attacks, each with a different mode of operation, so detecting them has become a difficult task for network monitoring and control systems

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17

Babić, Ivan, Aleksandar Miljković, Milan Čabarkapa, et al. "Triple Modular Redundancy Optimization for Threshold Determination in Intrusion Detection Systems." Symmetry 13, no. 4 (2021): 557. http://dx.doi.org/10.3390/sym13040557.

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Анотація:

This paper presents a novel approach for an Intrusion Detection System (IDS) based on one kind of asymmetric optimization which use any three already well-known IDS algorithms and Triple Modular Redundancy (TMR) algorithm together. Namely, a variable threshold which indicates an attack on an observed and protected network is determined by using all three values obtained with three known IDS algorithms i.e., on previously recorded data by making a decision by majority. For these algorithms authors used algorithm of k-nearest neighbors, cumulative sum algorithm, and algorithm of exponentially we

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18

Yuliswar, Teddy, Ikhwana Elfitri, and Onno W purbo. "Optimization of Intrusion Detection System with Machine Learning for Detecting Distributed Attacks on Server." INOVTEK Polbeng - Seri Informatika 10, no. 1 (2025): 367–76. https://doi.org/10.35314/vem9da98.

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Анотація:

This study develops an intrusion detection system optimized with machine learning techniques for efficient and effective detection of Distributed Denial-of-Service (DDoS) attacks. Using the Decision Tree algorithm, the system is designed to maximise accuracy in the identification and classification of DDoS attacks. The CIC-DDoS2019 dataset, which consists of various comprehensive simulated attack scenarios, is used as the basis for training and validation, providing the model with robust capabilities in recognizing DDoS attacks with high accuracy. This IDS successfully achieved a 100% detectio

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19

Ma, Ruikui, Qiuqian Wang, Xiangxi Bu, and Xuebin Chen. "Real-Time Detection of DDoS Attacks Based on Random Forest in SDN." Applied Sciences 13, no. 13 (2023): 7872. http://dx.doi.org/10.3390/app13137872.

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Анотація:

With the development of the Internet of Things, a huge number of devices are connected to the network, network traffic is exhibiting massive and low latency characteristics. At the same time, it is becoming cheaper and cheaper to launch DDoS attacks, and the attack traffic is becoming larger and larger. Software-defined networking SDN is proposed as a new network architecture. However, the controller as the core of SDN is vulnerable to DDoS attacks and causes a single point of failure in the network. This paper combines the ideas of distributed and edge computing, firstly, a DDoS attack detect

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20

Dasari, Kishore Babu, and Nagaraju Devarakonda. "Detection of TCP-Based DDoS Attacks with SVM Classification with Different Kernel Functions Using Common Uncorrelated Feature Subsets." International Journal of Safety and Security Engineering 12, no. 2 (2022): 239–49. http://dx.doi.org/10.18280/ijsse.120213.

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Анотація:

Distributed Denial of Service (DDoS) is a server-side infrastructure type security attack that aims to prevent legitimate users from accessing server system resources. Huge financial losses, reputation damage and data theft are some of the serious circumstances of DDoS attacks. Available DDoS attack detection methods reduce the severity of the attack's consequences, but they require more data computation, which is more expensive. This research proposed two feature selection methods in order to reduce the data computation for TCP-based DDoS attack detection with Support Vector Machine (SVM) cla

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21

Ali, Basheer Husham, Nasri Sulaiman, Syed Abdul Rahman Al-Haddad, Rodziah Atan, Siti Lailatul Mohd Hassan, and Mokhalad Alghrairi. "Identification of Distributed Denial of Services Anomalies by Using Combination of Entropy and Sequential Probabilities Ratio Test Methods." Sensors 21, no. 19 (2021): 6453. http://dx.doi.org/10.3390/s21196453.

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Анотація:

One of the most dangerous kinds of attacks affecting computers is a distributed denial of services (DDoS) attack. The main goal of this attack is to bring the targeted machine down and make their services unavailable to legal users. This can be accomplished mainly by directing many machines to send a very large number of packets toward the specified machine to consume its resources and stop it from working. We implemented a method using Java based on entropy and sequential probabilities ratio test (ESPRT) methods to identify malicious flows and their switch interfaces that aid them in passing

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22

Zhu, Yuanjiao, Yanling Chen, Jingsong Li, et al. "The DDoS Attack Detection for Information Systems of High-Penetration Renewable Energy Grids Based on TCN-BiLSTM." Journal of Physics: Conference Series 3022, no. 1 (2025): 012006. https://doi.org/10.1088/1742-6596/3022/1/012006.

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Abstract In high-penetration renewable energy grids, while providing efficient and intelligent energy supply and consumption management, they also face significant cybersecurity threats, particularly Distributed Denial of Service (DDoS) attacks. Due to the high dependence of the grid’s information systems on communication technologies, these attacks pose substantial risks to the stability and reliability of the grid. To tackle this issue, this paper introduces a novel approach for detecting DDoS attacks, leveraging Temporal Convolutional Networks (TCN) in combination with Bidirectional Long Sh

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23

Navya, Vattikonda Anuj Kumar Gupta Achuthananda Reddy Polu Bhumeka Narra Dheeraj Varun Kumar Reddy Buddula and Hari Hara Sudheer Patchipulusu. "Machine Learning-Based Approaches for Detecting and Mitigating Distributed Denial of Service (DDoS) Attacks to Improved Cloud Security." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN MULTIDISCIPLINARY EDUCATION 3, no. 12 (2024): 1993–64. https://doi.org/10.5281/zenodo.15340752.

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Cloud environments encounter massive service disruptions together with security breaches and substantial financiallosses through Distributed Denial of Service (DDoS) attacks. Detecting and mitigating DDoS assaults is the focus of this research,which examines the efficacy of ML models, particularly the CNN-LSTM model and the ID3 decision tree method. The CICDDoS2019dataset was used for both training and evaluation, employing a train-test data split of 80:20. The hybrid CNN-LSTMmodel achieved superior performance than the ID3 decision tree method when subjected to comparison because it integrate

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24

Kaliyaperumal, Karthikeyan, Raja Sarath Kumar Boddu, Sai Kiran Oruganti, Guidsa Tesema Kebesa, Mohsen Aghaeiboorkheili, and Rajendran Bhojan. "An Efficient Technique for Identifying Distributed Denial of‎Service Active Assaults Using Deep Neural Networks Based on the ‎Adaptive System Intelligence Paradigm." International Journal of Basic and Applied Sciences 14, no. 2 (2025): 577–90. https://doi.org/10.14419/dwfxsc41.

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A collection of interconnected devices that exchange data online is known as the Internet of Things. The IoT environment's diverse components make the distributed denial-of-service attack a security risk. One of the most important tasks in creating a smarter environment for ‎end users is detecting DDoS attacks in the Internet of Things. A new version of the optimized Elman recurrent neural network (ERNN) is ‎proposed to detect DDoS active attacks in Internet of Things scenarios. The proposed detection approach optimizes the weight and bias of ‎ERNN (ABCO-ERNN) using a novel adaptive bacterial

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Peng, Silin, Yu Han, Ruonan Li, Lichen Liu, Jie Liu, and Zhaoquan Gu. "ROSE-BOX: A Lightweight and Efficient Intrusion Detection Framework for Resource-Constrained IIoT Environments." Applied Sciences 15, no. 12 (2025): 6448. https://doi.org/10.3390/app15126448.

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The rapid advancement of the Industrial Internet of Things (IIoT) has transformed industrial automation, enabling real-time monitoring and intelligent decision making. However, increased connectivity exposes IIoT systems to sophisticated cyber threats, which may pose significant security risks, especially in resource-constrained IIoT environments where computational efficiency is critical. Existing intrusion detection solutions often suffer from high computational overhead and inadequate adaptability, rendering them impractical for real-time deployment in IIoT environments. To address these ch

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Fang, Menghao, Yixiang Wang, Liangbin Yang, et al. "Reinventing Web Security: An Enhanced Cycle-Consistent Generative Adversarial Network Approach to Intrusion Detection." Electronics 13, no. 9 (2024): 1711. http://dx.doi.org/10.3390/electronics13091711.

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Анотація:

Web3.0, as the link between the physical and digital domains, faces increasing security threats due to its inherent complexity and openness. Traditional intrusion detection systems (IDSs) encounter formidable challenges in grappling with the multidimensional and nonlinear traffic data characteristic of the Web3.0 environment. Such challenges include insufficient samples of attack data, inadequate feature extraction, and resultant inaccuracies in model classification. Moreover, the scarcity of certain traffic data available for analysis by IDSs impedes the system’s capacity to document instance

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Anukriti Naithani, Shailendra Narayan Singh. "Quantum-Enhanced Ddos Detection in 5G Cloud Networks using Bottleneck Attention Mechanism." Communications on Applied Nonlinear Analysis 32, no. 9s (2025): 1409–26. https://doi.org/10.52783/cana.v32.4166.

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Анотація:

The rapid expansion of 5G networks and cloud computing has heightened the risk of “Distributed Denial of Service (DDoS)201D attacks, which can severely compromise service availability and network performance. Traditional machine-learning techniques have shown limitations in accurately detecting these evolving attacks under high-traffic conditions, especially in 5G environments. This research proposes an advanced DDoS detection framework utilizing a “Quantum Convolutional Neural Network (QCNN)” combined with a Bottleneck Attention Mechanism. The QCNN extracts spatial and temporal features from

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Berríos, Sebastián, Sebastián Garcia, Pamela Hermosilla, and Héctor Allende-Cid. "A Machine-Learning-Based Approach for the Detection and Mitigation of Distributed Denial-of-Service Attacks in Internet of Things Environments." Applied Sciences 15, no. 11 (2025): 6012. https://doi.org/10.3390/app15116012.

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Анотація:

The widespread adoption of Internet of Things (IoT) devices has significantly increased the exposure of cloud-based architectures to cybersecurity risks, particularly Distributed Denial-of-Service (DDoS) attacks. Traditional detection methods often fail to efficiently identify and mitigate these threats in dynamic IoT/Cloud environments. This study proposes a machine-learning-based framework to enhance DDoS attack detection and mitigation, employing Random Forest, XGBoost, and Long Short-Term Memory (LSTM) models. Two well-established datasets, CIC-DDoS2019 and N-BaIoT, were used to train and

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Alshdadi, Abdulrahman A., Abdulwahab Ali Almazroi, Nasir Ayub, et al. "Federated Deep Learning for Scalable and Privacy-Preserving Distributed Denial-of-Service Attack Detection in Internet of Things Networks." Future Internet 17, no. 2 (2025): 88. https://doi.org/10.3390/fi17020088.

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Анотація:

Industry-wide IoT networks have altered operations and increased vulnerabilities, notably DDoS attacks. IoT systems are decentralised. Therefore, these attacks flood networks with malicious traffic, creating interruptions, financial losses, and availability issues. We need scalable, privacy-preserving, and resource-efficient IoT intrusion detection algorithms to solve this essential problem. This paper presents a Federated-Learning (FL) framework using ResVGG-SwinNet, a hybrid deep-learning architecture, for multi-label DDoS attack detection. ResNet improves feature extraction, VGGNet optimise

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Xu, Wen, Julian Jang-Jaccard, Tong Liu, Fariza Sabrina, and Jin Kwak. "Improved Bidirectional GAN-Based Approach for Network Intrusion Detection Using One-Class Classifier." Computers 11, no. 6 (2022): 85. http://dx.doi.org/10.3390/computers11060085.

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Анотація:

Existing generative adversarial networks (GANs), primarily used for creating fake image samples from natural images, demand a strong dependence (i.e., the training strategy of the generators and the discriminators require to be in sync) for the generators to produce as realistic fake samples that can “fool” the discriminators. We argue that this strong dependency required for GAN training on images does not necessarily work for GAN models for network intrusion detection tasks. This is because the network intrusion inputs have a simpler feature structure such as relatively low-dimension, discre

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Mills, Godfrey A., Daniel K. Acquah, and Robert A. Sowah. "Network Intrusion Detection and Prevention System Using Hybrid Machine Learning with Supervised Ensemble Stacking Model." Journal of Computer Networks and Communications 2024, no. 1 (2024). http://dx.doi.org/10.1155/2024/5775671.

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Анотація:

Network intrusion detection systems play a critical role in protecting a variety of services ranging from economic through social to commerce. However, the growing level and sophistication of malicious attacks launched on networks in the current technological landscape have necessitated the need for advanced and robust detection mechanisms to mitigate against security breaches of confidentiality, integrity, and denial‐of‐service. In this paper, we present a hybrid intrusion detection system that combines supervised and unsupervised learning models through an ensemble stacking model to increase

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32

İnce, Uğur, and Gülşah Karaduman. "Classification of Distributed Denial of Service Attacks Using Machine Learning Methods." NATURENGS MTU Journal of Engineering and Natural Sciences Malatya Turgut Ozal University, May 1, 2024. http://dx.doi.org/10.46572/naturengs.1450965.

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Анотація:

With the digitalized world, the uninterrupted provision of services over the internet, especially in hospitals, banking, energy, etc. systems is of great importance. There are many attack methods to disrupt or disable these services. Denial of service attacks, which are one of these methods, are more complex and difficult to detect; Organizing such attacks becomes very easy and cost-effective thanks to many tools. Attackers can perform DDoS attacks on target systems with very little knowledge and skills, and they can render target systems inoperable, sometimes for a short time or for days. Thi

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33

"COMPARATIVE ANALYSIS OF MACHINE LEARNING METHODS FOR NETWORK TRAFFIC BINARY CLASSIFICATION." Infokommunikacionnye tehnologii, March 28, 2025, 20–26. https://doi.org/10.18469/ikt.2024.22.2.03.

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This research offers a a comparative analysis of different machine learning methods used to solve the problem of dividing network traffic into two separate categories. The study considered such algorithms as logistic regression, support vector machine, decision tree, random forest, gradient boosting, single-layer and multilayer perceptrons. Experiments were performed on the CIC-DDoS2019 dataset, which contains more than 75 characteristics of normal and abnormal traffic. Metrics of accuracy, recall and F-measure were used to evaluate the models. This made it possible to get a comprehensive unde

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Wang, Bangli, Yuxuan Jiang, You Liao, and Zhen Li. "DDoS‐MSCT: A DDoS Attack Detection Method Based on Multiscale Convolution and Transformer." IET Information Security 2024, no. 1 (2024). http://dx.doi.org/10.1049/2024/1056705.

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Distributed denial‐of‐service (DDoS) attacks pose a significant threat to network security due to their widespread impact and detrimental consequences. Currently, deep learning methods are widely applied in DDoS anomaly traffic detection. However, they often lack the ability to collectively model both local and global traffic features, which presents challenges in improving performance. In order to provide an effective method for detecting abnormal traffic, this paper proposes a novel network architecture called DDoS‐MSCT, which combines a multiscale convolutional neural network and transforme

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35

Hicham, Yzzogh, and Benaboud Hafssa. "Enhancing SDN security with a feature-based approach using multiple k-means, Word2Vec, and neural network." March 5, 2025. 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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Alslman, Yasmeen, Ashwaq Khalil, Remah Younisse, Eman AlNagi, Jaafer Saraireh, and Rawan Ghnemat. "DDoS Attacks Detection Approach based on Ensemble Model using Spark." Jordanian Journal of Computers and Information Technology, 2024, 1. http://dx.doi.org/10.5455/jjcit.71-1694806966.

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We live in an era when time is a precious resource. Thus, dealing with the vast amount of data collected from different resources for various purposes requires creating systems that can process the data in a reasonable time to make it worthwhile. Accessing big data in machine learning and artificial intelligence models creates efficient, robust models. In this work, we present a method to create a multi-class classification model using Apache-spark. The model is built and trained with the CIC-DDOS2019 dataset to build a DDoS Attack detection model. Ensemble modeling was used to improve the acc

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37

Roopak, Monika, Simon Parkinson, Gui Yun Tian, Yachao Ran, Saad Khan, and Balasubramaniyan Chandrasekaran. "An unsupervised approach for the detection of zero‐day distributed denial of service attacks in Internet of Things networks." IET Networks, October 8, 2024. http://dx.doi.org/10.1049/ntw2.12134.

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AbstractThe authors introduce an unsupervised Intrusion Detection System designed to detect zero‐day distributed denial of service (DDoS) attacks in Internet of Things (IoT) networks. This system can identify anomalies without needing prior knowledge or training on attack information. Zero‐day attacks exploit previously unknown vulnerabilities, making them hard to detect with traditional deep learning and machine learning systems that require pre‐labelled data. Labelling data is also a time‐consuming task for security experts. Therefore, unsupervised methods are necessary to detect these new t

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38

Ferdous, Farhan Sadik, Tapu Biswas, and Akinul Islam Jony. "Enhancing Cybersecurity: Machine Learning Approaches for Predicting DDoS Attack." Malaysian Journal of Science and Advanced Technology, July 4, 2024, 249–55. http://dx.doi.org/10.56532/mjsat.v4i3.306.

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Dealing with network security has always been challenging, particularly with regard to the detection and prevention of Distributed Denial of Service (DDoS) attacks. Attacks like DDoS bring threats to the network by violating its availability to the probable people who are in need of using that particular server. It is a type of cyber-attack where a network is flooded with a huge amount of traffic, overwhelming the system, and making it unavailable. This type of attack focuses on making the service unavailable to rightful users, without breaching the security perimeter. In a DDoS attack, a mast

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39

Hossain, Md Alamgir. "Enhanced Ensemble-Based Distributed Denial-of-Service (DDoS) Attack Detection with Novel Feature Selection: A Robust Cybersecurity Approach." Artificial Intelligence Evolution, August 24, 2023, 165–86. http://dx.doi.org/10.37256/aie.4220233337.

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Анотація:

One of the major threats to computer networks and systems is distributed denial-of-service (DDoS) attacks. These attacks include saturating the targeted system with a large volume of traffic coming from several sources, which causes a service interruption. Detecting these attacks in real-time has become a critical task in cybersecurity. The existing method of DDoS attack detection suffers from the problem of high false positive rates. Additionally, the classifiers used in the existing methods may not be able to capture the complex patterns of the DDoS attack traffic, leading to low accuracy. I

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40

Guzman-Brand, Victor Alfonso, and Laura Gelvez-Garcia. "Identificación de ataques de denegación de servicio distribuido (DDoS) mediante la integración de algoritmos de aprendizaje automático y arquitecturas de redes neuronales artificiales." Revista Ingeniería, Matemáticas y Ciencias de la Información 12, no. 23 (2025). https://doi.org/10.21017/rimci.1116.

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Objective: To identify distributed denial of service (DDoS) attacks by integrating machine learning algorithms and artificial neural network architectures. Methodology: To structure the data analysis, the Knowledge Discovery Data (KDD) technique is used. This approach allows examining large volumes of information of various types, with the objective of identifying patterns, correlations and producing valuable information. As for the data set, the CIC-DDoS2019 dataset developed by the Canadian Cybersecurity Institute is used. Results: When training and evaluating the different algorithms, it wa

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Rashid Najam, Nora, and Razan Abdulhammed Abduljawad. "RF-RFE-SMOTE: A DoS And DDoS Attack Detection Framework." NTU Journal of Engineering and Technology 2, no. 2 (2023). http://dx.doi.org/10.56286/ntujet.v2i2.436.

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Denial of service and Distributed denial of service (Dos/DDos) attacks continue to be one of the most significant dangers in cybersecurity. Many efforts are being put into developing defenses against these types of attacks. The tools used by attackers to perform these types of attacks increase day-to-day. Thus, a countermeasure is necessary. For this reason, this thesis utilized one of the most recent datasets (CSE-CICIDS2018 and CIC-DDoS2019) containing most Dos/DDoS attacks. This study proposed a framework based on Machine Learning for detecting denial-of-service (DoS) and distributed denial

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Prasad, Arvind, and Shalini Chandra. "Machine learning to combat cyberattack: a survey of datasets and challenges." Journal of Defense Modeling and Simulation: Applications, Methodology, Technology, May 1, 2022, 154851292210948. http://dx.doi.org/10.1177/15485129221094881.

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The ever-increasing number of multi-vector cyberattacks has become a concern for all levels of organizations. Attackers are infecting Internet-enabled devices and exploiting them to carry out attacks. These devices are unwittingly becoming part of carrying out cyberattacks. Many studies have proposed machine learning–based promising solutions to stamp out cyberattacks preemptively. We review the machine learning techniques and highlight some promising solutions in recent studies. This study provides the advantage of experimenting with the developed solutions on modern datasets. This survey aim

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Lalduhsaka, R., and Ajoy Kumar Khan. "Enhanced Intrusion Detection System Using a Two‐Staged Feature Selection Method." SECURITY AND PRIVACY 8, no. 3 (2025). https://doi.org/10.1002/spy2.70025.

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ABSTRACTIntrusion detection (ID) systems are essential tools for safeguarding networks against cyber‐attacks. With the increasing sophistication and frequency of these attacks, developing ID systems that are both accurate and efficient is crucial. However, high‐dimensional datasets can hinder their efficiency and increase computational costs. This paper proposes a novel two‐stage feature selection method (GIGA) to optimize and enhance ID systems by reducing dimensionality while also improving detection accuracy. The first stage employs Gini impurity (GI) to filter out features with less import

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44

Li, Guo, and MingHua Wang. "A Meta-learning Approach for Few-shot Network Intrusion Detection Using Depthwise Separable Convolution." Journal of ICT Standardization, March 10, 2025, 443–70. https://doi.org/10.13052/jicts2245-800x.1245.

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Анотація:

As cyberattacks become more frequent and sophisticated, network intrusion detection systems (IDS) play a critical role in safeguarding networks. However, traditional IDS models face challenges in detecting new, unseen attacks and typically require large volumes of labeled data for effective training. To address these issues, we propose a novel intrusion detection model based on meta-learning, integrating depthwise separable convolution (DSC). This model leverages few-shot learning to detect rare and emerging attack types with minimal labeled data. By using meta-learning, our model can rapidly

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Mahdi, Zaed S., Rana M. Zaki, and Laith Alzubaidi. "Advanced Hybrid Techniques for Cyberattack Detection and Defense in IoT Networks." SECURITY AND PRIVACY, October 2024. http://dx.doi.org/10.1002/spy2.471.

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ABSTRACTThe Internet of Things (IoT) represents a vast network of devices connected to the Internet, making it easier for users to connect to modern technology. However, the complexity of these networks and the large volume of data pose significant challenges in protecting them from persistent cyberattacks, such as distributed denial‐of‐service (DDoS) attacks and spoofing. It has become necessary to use intrusion detection systems and protect these networks. Existing intrusion detection systems for IoT networks face many problems and limitations, including high false alarm rates and delayed de

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Ayad, Aya G., Nehal A. Sakr, and Noha A. Hikal. "A hybrid approach for efficient feature selection in anomaly intrusion detection for IoT networks." Journal of Supercomputing, August 29, 2024. http://dx.doi.org/10.1007/s11227-024-06409-x.

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AbstractThe exponential growth of Internet of Things (IoT) devices underscores the need for robust security measures against cyber-attacks. Extensive research in the IoT security community has centered on effective traffic detection models, with a particular focus on anomaly intrusion detection systems (AIDS). This paper specifically addresses the preprocessing stage for IoT datasets and feature selection approaches to reduce the complexity of the data. The goal is to develop an efficient AIDS that strikes a balance between high accuracy and low detection time. To achieve this goal, we propose

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Prasath, J. S., V. Irine Shyja, P. Chandrakanth, Boddepalli Kiran Kumar, and Adam Raja Basha. "An optimal secure defense mechanism for DDoS attack in IoT network using feature optimization and intrusion detection system." Journal of Intelligent & Fuzzy Systems, January 19, 2024, 1–18. http://dx.doi.org/10.3233/jifs-235529.

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Now, the Cyber security is facing unprecedented difficulties as a result of the proliferation of smart devices in the Internet of Things (IoT) environment. The rapid growth in the number of Internet users over the past two decades has increased the need for cyber security. Users have provided new opportunities for attackers to do harm. Limited security budgets leave IoT devices vulnerable and easily hacked to launch distributed denial-of-service (DDoS) attacks, with disastrous results. Unfortunately, due to the unique nature of the Internet of Things environment, most security solutions and in

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