Relevant bibliographies by topics / CICIDS2018 dataset

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'CICIDS2018 dataset'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "CICIDS2018 dataset"

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Deng, Miaolei, Chuanchuan Sun, Yupei Kan, Haihang Xu, Xin Zhou, and Shaojun Fan. "Network Intrusion Detection Based on Deep Belief Network Broad Equalization Learning System." Electronics 13, no. 15 (2024): 3014. http://dx.doi.org/10.3390/electronics13153014.

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Network intrusion detection systems are an important defense technology to guarantee information security and protect a network from attacks. In recent years, the broad learning system has attracted much attention and has been introduced into intrusion detection systems with some success. However, since the traditional broad learning system is a simple linear structure, when dealing with imbalanced datasets, it often ignores the feature learning of minority class samples, leading to a poorer recognition rate of minority class samples. Secondly, the high dimensionality and redundant features in
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Zhao, Jiaqi, Ming Xu, Yunzhi Chen, and Guoliang Xu. "A DNN Architecture Generation Method for DDoS Detection via Genetic Alogrithm." Future Internet 15, no. 4 (2023): 122. http://dx.doi.org/10.3390/fi15040122.

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Nowdays, DNNs (Deep Neural Networks) are widely used in the field of DDoS attack detection. However, designing a good DNN architecture relies on the designer’s experience and requires considerable work. In this paper, a GA (genetic algorithm) is used to automatically generate the DNN architecture for DDoS detection to minimize human intervention in the design process. Furthermore, given the complexity of contemporary networks and the diversity of DDoS attacks, the objective of this paper is to generate a DNN model that boasts superior performance, real-time capability, and generalization abili
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Gandhar, Abhishek, Prakhar Priyadarshi, Shashi Gandhar, S. B. Kumar, Arvind Rehalia, and Mohit Tiwari. "An Effective Deep Learning Model Design for Cyber Intrusion Prevention System." Indian Journal Of Science And Technology 18, no. 10 (2025): 811–15. https://doi.org/10.17485/ijst/v18i10.318.

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Objectives: The increasing frequency of cyber threats necessitates the advancement of Intrusion Prevention Systems (IPS). However, existing IPS models suffer from high false positive rates, inefficiencies in real-time detection, and suboptimal accuracy levels. Methods: This study presents a CNN-LSTM hybrid model optimized for real-time cyber intrusion detection. The CICIDS2018 dataset was utilized for training, incorporating feature selection, hyper-parameter tuning, and dropout-based regularization to improve efficiency and prevent over-fitting. Findings: The proposed system achieved an F1-sc
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Yogi, Aryan. "Hybrid Intrusion Detection System (IDS) Using Machine Learning and Deep Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47975.

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Abstract - This work offers a Hybrid Intrusion Detection System (HIDS) that combines traditional machine learning and deep learning methods for efficient and scalable network attack identification. The system makes use of Principal Component Analysis (PCA) for reducing dimensionality and then utilizes a hybrid CNN-LSTM architecture for feature learning as well as classification. An ensemble method is also utilized to combine Random Forest with the CNN-LSTM to add robustness as well as generalization. The CICIDS2018 dataset, comprising modern real-world network traffic situations, is employed f
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Irfa’issurur, Muhammad, and Bony Parulian Josaphat. "Machine Learning for Cybersecurity: Web Attack Detection (Brute Force, XSS, SQL Injection)." InPrime: Indonesian Journal of Pure and Applied Mathematics 7, no. 1 (2025): 1–15. https://doi.org/10.15408/inprime.v7i1.41025.

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Security is a top priority in system development, as web portals serve as critical entry points that are frequently targeted by cyber-attacks. Common attack methods include SQL Injection, Cross-Site Scripting (XSS), and Brute Force. The application of machine learning in cybersecurity is growing due to its effectiveness in detecting such threats. This study employs supervised machine learning with six algorithms: K-Nearest Neighbors (KNN), Random Forest, Naïve Bayes, AdaBoost, LightGBM, and XGBoost. The research utilizes the CICIDS2017 and CSE-CICIDS2018 datasets, which contain network traffic
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Abhishek, Gandhar, Priyadarshi Prakhar, Gandhar Shashi, B. Kumar S, Rehalia Arvind, and Tiwari Mohit. "An Effective Deep Learning Model Design for Cyber Intrusion Prevention System." Indian Journal of Science and Technology 18, no. 10 (2025): 811–15. https://doi.org/10.17485/IJST/v18i10.318.

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Abstract <strong>Objectives:</strong>&nbsp;The increasing frequency of cyber threats necessitates the advancement of Intrusion Prevention Systems (IPS). However, existing IPS models suffer from high false positive rates, inefficiencies in real-time detection, and suboptimal accuracy levels.&nbsp;<strong>Methods:</strong>&nbsp;This study presents a CNN-LSTM hybrid model optimized for real-time cyber intrusion detection. The CICIDS2018 dataset was utilized for training, incorporating feature selection, hyper-parameter tuning, and dropout-based regularization to improve efficiency and prevent ove
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Xiao, Yao, Chunying Kang, Hongchen Yu, Tao Fan, and Haofang Zhang. "Anomalous Network Traffic Detection Method Based on an Elevated Harris Hawks Optimization Method and Gated Recurrent Unit Classifier." Sensors 22, no. 19 (2022): 7548. http://dx.doi.org/10.3390/s22197548.

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In recent years, network traffic contains a lot of feature information. If there are too many redundant features, the computational cost of the algorithm will be greatly increased. This paper proposes an anomalous network traffic detection method based on Elevated Harris Hawks optimization. This method is easier to identify redundant features in anomalous network traffic, reduces computational overhead, and improves the performance of anomalous traffic detection methods. By enhancing the random jump distance function, escape energy function, and designing a unique fitness function, there is a
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Shivakanth, Gandla. "A Performance Analysis of ML-Based Intrusion Detection Systems in Cloud Environments." International Journal of Electrical and Electronic Engineering & Telecommunications 14, no. 4 (2025): 243–52. https://doi.org/10.18178/ijeetc.14.4.243-252.

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Intrusion Detection Systems (IDS) are important for protecting cloud environments against emerging cyber threats. This paper introduces AI-SCAN (artificial intelligence-driven scalable convolutional network for anomaly detection in cloud networks), a deep learning IDS that utilizes a Convolutional Neural Network (CNN) architecture to achieve better threat detection with better scalability, flexibility, and low false positives. The proposed system overcomes key challenges of dataset bias, external validation, and class imbalance to provide robust performance in dynamic cloud networks. To reduce
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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
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Zhang, Kunsan, Renguang Zheng, Chaopeng Li, et al. "SE-DWNet: An Advanced ResNet-Based Model for Intrusion Detection with Symmetric Data Distribution." Symmetry 17, no. 4 (2025): 526. https://doi.org/10.3390/sym17040526.

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With the rapid increase in cyber-attacks, intrusion detection systems (IDS) have become essential for network security. However, traditional IDS methods often struggle with class imbalance, leading to asymmetric data distributions that adversely affect detection performance and model generalization. To address this issue and enhance detection accuracy, this paper proposes SE-DWNet, a residual network model incorporating an attention mechanism and one-dimensional depthwise separable convolution, trained on a symmetrically preprocessed dataset using SMOTETomek sampling. First, the feature distri
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Dissertations / Theses on the topic "CICIDS2018 dataset"

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Gustavsson, Vilhelm. "Machine Learning for a Network-based Intrusion Detection System : An application using Zeek and the CICIDS2017 dataset." Thesis, KTH, Hälsoinformatik och logistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-253273.

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Cyber security is an emerging field in the IT-sector. As more devices are connected to the internet, the attack surface for hackers is steadily increasing. Network-based Intrusion Detection Systems (NIDS) can be used to detect malicious traffic in networks and Machine Learning is an up and coming approach for improving the detection rate. In this thesis the NIDS Zeek is used to extract features based on time and data size from network traffic. The features are then analyzed with Machine Learning in Scikit-Learn in order to detect malicious traffic. A 98.58% Bayesian detection rate was achieved for t
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Book chapters on the topic "CICIDS2018 dataset"

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Saleh, Abu Jafar Mohammad, and Nasim Adnan. "Denial-of-Service (DoS) Threat Detection Using Supervised Machine Learning Algorithms on CICIDS2018 Dataset." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2445-3_36.

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Shoukat, Duaa, Adnan Akhunzada, Muhammad Taimoor Khan, Ahmad Sami Al-Shamayleh, Mueen Uddin, and Hashem Alaidaros. "Bridging Innovation and Security: Advancing Cyber-Threat Detection in Sustainable Smart Infrastructure." In Proceedings in Technology Transfer. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-8588-9_11.

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Abstract The rapid evolution of Smart Infrastructure (SI) on a global scale has revolutionized our daily lives, empowering us with unprecedented connectivity and convenience. However, this evolution has also exposed smart devices to increasingly sophisticated cyber-threats, endangering the integrity of entire smart networks. In response to these challenges, this paper proposes a novel approach utilizing Deep Learning (DL) models for multi-class threat detection in SI environments. Specifically, we introduce the Cu-GRULSTM model, which leverages CUDA-enabled Gated Recurrent Units (GRU) and Long
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Reddy, Pochamreddy Mukesh, Lav Upadhyay, and Lanka Rakesh. "Performance Analysis of Machine Learning Classifiers on CICIDS2017 Dataset." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-8193-5_30.

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Rosay, Arnaud, Florent Carlier, and Pascal Leroux. "MLP4NIDS: An Efficient MLP-Based Network Intrusion Detection for CICIDS2017 Dataset." In Machine Learning for Networking. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45778-5_16.

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Lanvin, Maxime, Pierre-François Gimenez, Yufei Han, Frédéric Majorczyk, Ludovic Mé, and Éric Totel. "Errors in the CICIDS2017 Dataset and the Significant Differences in Detection Performances It Makes." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-31108-6_2.

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Phulre, Ajay Kumar, Manoj Verma, Jitendra Pratap Singh Mathur, and Sanat Jain. "Approach on Machine Learning Techniques for Anomaly-Based Web Intrusion Detection Systems: Using CICIDS2017 Dataset." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8135-9_6.

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Singh, Kuljeet, Amit Mahajan, and Vibhakar Mansotra. "Deep Learning Approach Based on ADASYN for Detection of Web Attacks in the CICIDS2017 Dataset." In Rising Threats in Expert Applications and Solutions. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1122-4_7.

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Azalmad, Mohamed, Rachid El Ayachi, and Mohamed Biniz. "Unveiling the Performance Insights: Benchmarking Anomaly-Based Intrusion Detection Systems Using Decision Tree Family Algorithms on the CICIDS2017 Dataset." In Business Intelligence. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37872-0_15.

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Do, Thi Thu Hien, Ba Truc Le, The Duy Phan, Thi Huong Lan Do, Do Hoang Hien, and Van-Hau Pham. "Intrusion Detection with Big Data Analysis in SDN-Enabled Networks." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220284.

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Although Software-defined networking (SDN) is a promising architecture that simplifies network management and control, it also faces security problems that may affect the whole network. Hence, protecting strategies, such as intrusion detection and prevention system (IDPS), are in need in the SDN context. The potential of machine learning-based solutions can become the motivation of cut-edge deep learning-based intrusion detection system that can leverage the centralized control and view of the controller to secure the underlying infrastructure. However, performing additional IDPS functions in
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Saranya, R., and S. Silvia Priscila. "Enhancing Network Security on a Hybrid LSTM-Gradient Boosting Framework for Intrusion Detection." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-4672-4.ch006.

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The chapter presents a new hybrid intrusion detection framework that combines LSTM networks with gradient-boosting techniques. The approach presented in this study utilizes two prominent datasets, namely CICIDS2018 and CICIDS2017. The main objective is to improve the precision and reliability of intrusion detection in systems. This is achieved by capturing temporal dependencies in network traffic data and improving predictive performance by implementing boosting algorithms. The datasets are subjected to a thorough preprocessing process involving cleaning, normalization, and feature selection.
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Conference papers on the topic "CICIDS2018 dataset"

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Raza, Md Salim, Humaira Arif, Sai Mounika Errapotu, and Virgilio Gonzalez. "Comprehensive Analysis of MLP and Ensemble Learning Approaches for Intrusion Detection using CICIDS2017 Dataset." In 2024 IEEE Future Networks World Forum (FNWF). IEEE, 2024. https://doi.org/10.1109/fnwf63303.2024.11028740.

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Niang, Papa Malick. "ANALYSIS OF DATA SETS FOR THE STUDY OF COMPUTER NETWORK VULNERABILITIES." In Intelligent transport systems. Russian University of Transport, 2024. http://dx.doi.org/10.30932/9785002446094-2024-699-709.

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One of the main challenges is the lack of a real-world data set that reflects modern network traffic scenarios and contains common transactions as well as common and emerging attacks. This article aims to compare the KDD99, IoTID20, UNSW-NB15, CICIDS2017 datasets with each other and identify a dataset that meets the requirements.
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Engelen, Gints, Vera Rimmer, and Wouter Joosen. "Troubleshooting an Intrusion Detection Dataset: the CICIDS2017 Case Study." In 2021 IEEE Security and Privacy Workshops (SPW). IEEE, 2021. http://dx.doi.org/10.1109/spw53761.2021.00009.

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Silva Neto, Manuel Gonçalves da, and Danielo G. Gomes. "Network Intrusion Detection Systems Design: A Machine Learning Approach." In XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sbrc.2019.7413.

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With the increasing popularization of computer network-based technologies, security has become a daily concern, and intrusion detection systems (IDS) play an essential role in the supervision of computer networks. An employed approach to combat network intrusions is the development of intrusion detection systems via machine learning techniques. The intrusion detection performance of these systems depends highly on the quality of the IDS dataset used in their design and the decision making for the most suitable machine learning algorithm becomes a difficult task. The proposed paper focuses on e
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Elaeraj, Ouafae, and Cherkaoui Leghris. "The Evolution of Vector Machine Support in the Field of Intrusion Detection Systems." In 2nd International Conference on Machine Learning Techniques and Data Science (MLDS 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.111817.

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With the increase in Internet and local area network usage, malicious attacks and intrusions into computer systems are growing. The design and implementation of intrusion detection systems became extremely important to help maintain good network security. Support vector machines (SVM), a classic pattern recognition tool, has been widely used in intrusion detection. They make it possible to process very large data with great efficiency and are easy to use, and exhibit good prediction behavior. This paper presents a new SVM model enriched with a Gaussian kernel function based on the features of
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Hidalgo-Espinoza, Sergio, Kevin Chamorro-Cupuerán, and Oscar Chang-Tortolero. "Intrusion Detection in Computer Systems by using Artificial Neural Networks with Deep Learning Approaches." In 10th International Conference on Advances in Computing and Information Technology (ACITY 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101501.

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Intrusion detection into computer networks has become one of the most important issues in cybersecurity. Attackers keep on researching and coding to discover new vulnerabilities to penetrate information security system. In consequence computer systems must be daily upgraded using up-to-date techniques to keep hackers at bay. This paper focuses on the design and implementation of an intrusion detection system based on Deep Learning architectures. As a first step, a shallow network is trained with labelled log-in [into a computer network] data taken from the Dataset CICIDS2017. The internal beha
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