Log in

Relevant bibliographies by topics / CICIDS2018 dataset / Journal articles

To see the other types of publications on this topic, follow the link: CICIDS2018 dataset.

Journal articles on the topic 'CICIDS2018 dataset'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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

Select a source type:

Consult the top 50 journal articles for your research on the topic 'CICIDS2018 dataset.'

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

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

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

3

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

Abstract:

Abstract <strong>Objectives:</strong> 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. <strong>Methods:</strong> 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

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

11

Rosline, Gnanam Jeba, and Pushpa Rani. "Intrusion detection based on generative adversarial network with random forest for cloud networks." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 2491–98. https://doi.org/10.11591/ijece.v15i2.pp2491-2498.

Full text

Abstract:

The development of cloud computing enables individuals and organizations to access a wide range of online programs and services. Because of its nature, numerous users can access and distribute cloud infrastructure. In cloud computing several security threats change the data and operations. A network's ability to detect malicious activity and possible threats is greatly aided by intrusion detection. To solve these issues, intrusion detection based on generative adversarial network with random forest (GAN-RF) for cloud networks is introduced. The function of the generative adversarial networks (

APA, Harvard, Vancouver, ISO, and other styles

12

Kaushik, Sunil, Akashdeep Bhardwaj, Abdullah Alomari, Salil Bharany, Amjad Alsirhani, and Mohammed Mujib Alshahrani. "Efficient, Lightweight Cyber Intrusion Detection System for IoT Ecosystems Using MI2G Algorithm." Computers 11, no. 10 (2022): 142. http://dx.doi.org/10.3390/computers11100142.

Full text

Abstract:

The increase in internet connectivity has led to an increased usage of the Internet of Things (IoT) and devices on the internet. These IoT devices are becoming the backbone of Industry 4.0. The dependence on IoT devices has made them vulnerable to cyber-attacks. IoT devices are often deployed in harsh conditions, challenged with less computational costs, and starved with energy. All these limitations make it tough to deploy accurate intrusion detection systems (IDSs) in IoT devices and make the critical IoT ecosystem more susceptible to cyber-attacks. A new lightweight IDS and a novel feature

APA, Harvard, Vancouver, ISO, and other styles

13

Prihantono, Yuri, and Kalamullah Ramli. "Model-Based Feature Selection for Developing Network Attack Detection and Alerting System." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 6, no. 2 (2022): 322–29. http://dx.doi.org/10.29207/resti.v6i2.3989.

Full text

Abstract:

The use of the Intrusion Detection Systems (IDS) still has unresolved problems, namely the lack of accuracy in attack detection, resulting in false-positive problems and many false alarms. Machine learning is one way that is often utilized to overcome challenges that arise during the implementation of IDS.. We present a system that uses a machine learning approach to detect network attacks and send attack alerts in this study. The CSE-CICIDS2018 Dataset and Model-Based Feature Selection technique are used to assess the performance of eight classifier algorithms in identifying network attacks i

APA, Harvard, Vancouver, ISO, and other styles

14

Alahmed, Shahad, Qutaiba Alasad, Maytham M. Hammood, Jiann-Shiun Yuan, and Mohammed Alawad. "Mitigation of Black-Box Attacks on Intrusion Detection Systems-Based ML." Computers 11, no. 7 (2022): 115. http://dx.doi.org/10.3390/computers11070115.

Full text

Abstract:

Intrusion detection systems (IDS) are a very vital part of network security, as they can be used to protect the network from illegal intrusions and communications. To detect malicious network traffic, several IDS based on machine learning (ML) methods have been developed in the literature. Machine learning models, on the other hand, have recently been proved to be effective, since they are vulnerable to adversarial perturbations, which allows the opponent to crash the system while performing network queries. This motivated us to present a defensive model that uses adversarial training based on

APA, Harvard, Vancouver, ISO, and other styles

15

Aldallal, Ammar. "Toward Efficient Intrusion Detection System Using Hybrid Deep Learning Approach." Symmetry 14, no. 9 (2022): 1916. http://dx.doi.org/10.3390/sym14091916.

Full text

Abstract:

The increased adoption of cloud computing resources produces major loopholes in cloud computing for cybersecurity attacks. An intrusion detection system (IDS) is one of the vital defenses against threats and attacks to cloud computing. Current IDSs encounter two challenges, namely, low accuracy and a high false alarm rate. Due to these challenges, additional efforts are required by network experts to respond to abnormal traffic alerts. To improve IDS efficiency in detecting abnormal network traffic, this work develops an IDS using a recurrent neural network based on gated recurrent units (GRUs

APA, Harvard, Vancouver, ISO, and other styles

16

Rosline, Gnanam Jeba, and Pushpa Rani. "Intrusion detection based on generative adversarial network with random forest for cloud networks." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 2491. https://doi.org/10.11591/ijece.v15i2.pp2491-2498.

Full text

Abstract:

The development of cloud computing enables individuals and organizations to access a wide range of online programs and services. Because of its nature, numerous users can access and distribute cloud infrastructure. In cloud computing several security threats change the data and operations. A network's ability to detect malicious activity and possible threats is greatly aided by intrusion detection. To solve these issues, intrusion detection based on generative adversarial network with random forest (GAN-RF) for cloud networks is introduced. The function of the generative adversarial networks (

APA, Harvard, Vancouver, ISO, and other styles

17

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.

Full text

Abstract:

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 (

APA, Harvard, Vancouver, ISO, and other styles

18

Fransiska, Hera, and Amalyanda Azhari. "Penerapan Transformer Based Deep Learning Untuk Deteksi Dini Serangan Siber Pada Infrastruktur Kritis Berbasis IoT." RIGGS: Journal of Artificial Intelligence and Digital Business 4, no. 2 (2025): 3818–25. https://doi.org/10.31004/riggs.v4i2.1118.

Full text

Abstract:

Perkembangan pesat Internet of Things (IoT) mendorong transformasi digital pada infrastruktur kritis seperti energi, transportasi, dan layanan publik. Namun, integrasi IoT juga meningkatkan risiko serangan siber akibat banyaknya celah keamanan di perangkat dan jaringan yang saling terhubung. Deteksi dini serangan siber menjadi sangat penting untuk menjaga stabilitas dan keberlanjutan operasional infrastruktur tersebut. Penelitian ini mengevaluasi efektivitas arsitektur Transformer Based Deep Learning dalam mendeteksi serangan siber secara proaktif di lingkungan IoT. Metode yang digunakan adala

APA, Harvard, Vancouver, ISO, and other styles

19

Qu, YanZe, HaiLong Ma, and YiMing Jiang. "CRND: An Unsupervised Learning Method to Detect Network Anomaly." Security and Communication Networks 2022 (October 28, 2022): 1–9. http://dx.doi.org/10.1155/2022/9509417.

Full text

Abstract:

Network anomaly detection system (NADS) is one of the most important methods to maintain network system security. At present, network anomaly detection models based on deep learning have become a research hotspot in the area because of their advantage in processing high-dimensional data and excellent performance on detecting anomaly. However, most of the related research studies are based on supervised learning, which has strict requirements for dataset such as labels with high accuracy. However, there are some difficulties in obtaining a large amount of data with complete label message, thus

APA, Harvard, Vancouver, ISO, and other styles

20

Alosimy, Hanadi, Jawaher AlZaidi, Samah H. Alajmani, and Ben Soh. "An Algorithm for Detecting Brute Force Attacks on FTP and SSH Services Utilizing Deep Learning with Probabilistic Neural Networks (PNN)." International Journal of Recent Technology and Engineering (IJRTE) 13, no. 6 (2025): 1–9. https://doi.org/10.35940/ijrte.e8187.13060325.

Full text

Abstract:

Brute force attacks remain one of the most prevalent and effective methods cybercriminals use to gain unauthorized access to networks and systems. These attacks involve systematically attempting various password or key combinations until the correct one is identified, often targeting critical services such as FTP (File Transfer Protocol) and SSH (Secure Shell). The consequences of these attacks can be severe, including data breaches, financial losses, and reputational damage. Intrusion Detection Systems (IDS) play a crucial role in mitigating these threats by monitoring network traffic and ide

APA, Harvard, Vancouver, ISO, and other styles

21

Jawaher, AlZaidi. "An Algorithm for Detecting Brute Force Attacks on FTP and SSH Services Utilizing Deep Learning with Probabilistic Neural Networks (PNN)." International Journal of Recent Technology and Engineering (IJRTE) 13, no. 6 (2025): 1–9. https://doi.org/10.35940/ijrte.E8187.13060325.

Full text

Abstract:

<strong>Abstract: </strong>Brute force attacks remain one of the most prevalent and effective methods cybercriminals use to gain unauthorized access to networks and systems. These attacks involve systematically attempting various password or key combinations until the correct one is identified, often targeting critical services such as FTP (File Transfer Protocol) and SSH (Secure Shell). The consequences of these attacks can be severe, including data breaches, financial losses, and reputational damage. Intrusion Detection Systems (IDS) play a crucial role in mitigating these threats by monitor

APA, Harvard, Vancouver, ISO, and other styles

22

Adekunle, Temitope Samson, Toheeb Adetoyese Adeleke, Olakunle Sunday Afolabi, et al. "A Framework for Robust Attack Detection and Classification using Rap-Densenet." ParadigmPlus 4, no. 2 (2023): 1–17. http://dx.doi.org/10.55969/paradigmplus.v4n2a1.

Full text

Abstract:

Network attacks must be effectively identified and categorized to guarantee strong security. However, current techniques frequently have trouble correctly identifying and categorizing new attack patterns. This study presents a novel framework for reliable attack detection and classification that makes use of the complementary strengths of rap music analysis methods and DenseNet convolutional neural networks. This study employs feature extraction based on the Attention Pyramid Network (RAPNet) framework that has been proposed to extract features from the input data, and Pigeon in binary. Afterw

APA, Harvard, Vancouver, ISO, and other styles

23

Rajesh Bingu, Et al. "Performance Comparison Analysis of Classification Methodologies for Effective Detection of Intrusions." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 2860–79. http://dx.doi.org/10.17762/ijritcc.v11i9.9375.

Full text

Abstract:

Intrusion detection systems (IDS) are critical in many applications, including cloud environments. The intrusion poses a security threat and extracts privacy data and information from the cloud. The user has an Internet function that allows him to store personal information in the cloud environment. The cloud can be affected by various issues such as data loss, data breaches, lower security and lack of privacy due to some intruders. A single intrusion incident can result in data within computer and network systems being quickly stolen or deleted. Additionally, intrusions can cause damage to sy

APA, Harvard, Vancouver, ISO, and other styles

24

Gebremariam, Gebrekiros Gebreyesus, J. Panda, and S. Indu. "Localization and Detection of Multiple Attacks in Wireless Sensor Networks Using Artificial Neural Network." Wireless Communications and Mobile Computing 2023 (January 10, 2023): 1–29. http://dx.doi.org/10.1155/2023/2744706.

Full text

Abstract:

Security enhancement in wireless sensor networks (WSNs) is significant in different applications. The advancement of routing attack localization is a crucial security research scenario. Various routing attacks degrade the network performance by injecting malicious nodes into wireless sensor networks. Sybil attacks are the most prominent ones generating false nodes similar to the station node. This paper proposed detection and localization against multiple attacks using security localization based on an optimized multilayer perceptron artificial neural network (MLPANN). The proposed scheme has

APA, Harvard, Vancouver, ISO, and other styles

25

Awadh, Nouf, Hawazen Zaid, and Dr Samah Al-ajmani. "A Robust Framework for Detecting Brute-Force Attacks through Deep Learning Techniques." International Journal of Recent Technology and Engineering (IJRTE) 13, no. 5 (2025): 27–42. https://doi.org/10.35940/ijrte.e8182.13050125.

Full text

Abstract:

A considerable concern arises with the precise identification of brute-force threats within a networked environment. It emphasizes the need for new methods, as existing ones often lead to many false alarms, as well as delays in real-time threat detection. To tackle these issues, this study proposes a novel intrusion detection framework that utilizes deep learning models for more accurate and efficient detection of brute-force attacks. The framework’s structure includes data collection and preprocessing components performed at the outset of the study using the CSE-CICIDS2018 dataset. The design

APA, Harvard, Vancouver, ISO, and other styles

26

Riyadi, Andri Agung, Fachri Amsury, Irwansyah Saputra, Tiska Pattiasina, and Jupriyanto Jupriyanto. "COMPARATIVE ANALYSIS OF THE K-NEAREST NEIGHBOR ALGORITHM ON VARIOUS INTRUSION DETECTION DATASETS." Jurnal Riset Informatika 4, no. 1 (2022): 127–32. http://dx.doi.org/10.34288/jri.v4i1.341.

Full text

Abstract:

Security in computer networks can be vulnerable, this is because we have weaknesses in making security policies, weak computer system configurations, or software bugs. Intrusion detection is a mechanism for securing computer networks by detecting, preventing, and blocking illegal attempts to access confidential information. The IDS mechanism is designed to protect the system and reduce the impact of damage from any attack on a computer network for violating computer security policies including availability, confidentiality, and integrity. Data mining techniques have been used to obtain useful

APA, Harvard, Vancouver, ISO, and other styles

27

Fiona Lawrence. "Enhancing Intrusion Detection Systems with Ensemble Models and Hybrid Feature Selection Techniques." Journal of Information Systems Engineering and Management 10, no. 23s (2025): 937–54. https://doi.org/10.52783/jisem.v10i23s.3816.

Full text

Abstract:

Detection Systems (IDS) play a critical role in safeguarding networks against cyber attacks. However, selecting the most effective machine learning model for intrusion detection is challenging due to varying dataset characteristics. This research investigates the performance of multiple machine learning models, including SVM (Linear, Poly, RBF, and Sigmoid), LightGBM, XGBoost, and CatBoost, across two widely used datasets: CICIDS2017 and NF-UNSW-NB15. The primary problem is the inconsistency in model performance across different datasets, affecting the reliability of IDS solutions. To address

APA, Harvard, Vancouver, ISO, and other styles

28

Mohammed, Widad K., Mohammed A. Taha, and Saleh M. Mohammed. "A Novel Hybrid Fusion Model for Intrusion Detection Systems Using Benchmark Checklist Comparisons." Mesopotamian Journal of CyberSecurity 4, no. 3 (2024): 216–32. https://doi.org/10.58496/mjcs/2024/024.

Full text

Abstract:

Due to the quick development of network technology, assaults have become more sophisticated and dangerous. Numerous strategies have been put out to target different types of attacks and conduct trials using various approaches. In order to maintain network integrity and ensure network security, intrusion detection systems, or IDSs, are necessary. In this work, we investigate the effects of several feature extraction methods on IDS performance. We analyze the performance of various feature extraction techniques on two well-known intrusion detection datasets, NSL-KDD and CICIDS2017. Two datasets

APA, Harvard, Vancouver, ISO, and other styles

29

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

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

30

Pangsuban, Preecha, Prachyanun Nilsook, and Panita Wannapiroon. "A Real-time Risk Assessment for Information System with CICIDS2017 Dataset Using Machine Learning." International Journal of Machine Learning and Computing 10, no. 3 (2020): 465–70. http://dx.doi.org/10.18178/ijmlc.2020.10.3.958.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Hofer-Schmitz, Katharina, Ulrike Kleb, and Branka Stojanović. "The Influences of Feature Sets on the Detection of Advanced Persistent Threats." Electronics 10, no. 6 (2021): 704. http://dx.doi.org/10.3390/electronics10060704.

Full text

Abstract:

This paper investigates the influences of different statistical network traffic feature sets on detecting advanced persistent threats. The selection of suitable features for detecting targeted cyber attacks is crucial to achieving high performance and to address limited computational and storage costs. The evaluation was performed on a semi-synthetic dataset, which combined the CICIDS2017 dataset and the Contagio malware dataset. The CICIDS2017 dataset is a benchmark dataset in the intrusion detection field and the Contagio malware dataset contains real advanced persistent threat (APT) attack

APA, Harvard, Vancouver, ISO, and other styles

32

Zhao, Yifan, Zhanhui Hu, and Rongjun Liu. "TBGD: Deep Learning Methods on Network Intrusion Detection Using CICIDS2017 Dataset." Journal of Physics: Conference Series 2670, no. 1 (2023): 012025. http://dx.doi.org/10.1088/1742-6596/2670/1/012025.

Full text

Abstract:

Abstract With the development of science and technology, more and more personal information is uploaded to the Internet, which poses a serious threat to our personal and property security. As machine learning and deep learning techniques continue to develop, they become increasingly powerful at extracting data and improving the accuracy of classifying malicious traffic. This paper proposes an intrusion detection model based on Transformer, BiGRU, and DNN, referred to as the TBGD model. The Multi-Head Attention mechanism and Feedforward Neural Network in Transformer help capture global relation

APA, Harvard, Vancouver, ISO, and other styles

33

Han, Hyojoon, Hyukho Kim, and Yangwoo Kim. "An Efficient Hyperparameter Control Method for a Network Intrusion Detection System Based on Proximal Policy Optimization." Symmetry 14, no. 1 (2022): 161. http://dx.doi.org/10.3390/sym14010161.

Full text

Abstract:

The complexity of network intrusion detection systems (IDSs) is increasing due to the continuous increases in network traffic, various attacks and the ever-changing network environment. In addition, network traffic is asymmetric with few attack data, but the attack data are so complex that it is difficult to detect one. Many studies on improving intrusion detection performance using feature engineering have been conducted. These studies work well in the dataset environment; however, it is challenging to cope with a changing network environment. This paper proposes an intrusion detection hyperp

APA, Harvard, Vancouver, ISO, and other styles

34

Han, Daoqi, Honghui Li, Xueliang Fu, and Shuncheng Zhou. "Traffic Feature Selection and Distributed Denial of Service Attack Detection in Software-Defined Networks Based on Machine Learning." Sensors 24, no. 13 (2024): 4344. http://dx.doi.org/10.3390/s24134344.

Full text

Abstract:

As 5G technology becomes more widespread, the significant improvement in network speed and connection density has introduced more challenges to network security. In particular, distributed denial of service (DDoS) attacks have become more frequent and complex in software-defined network (SDN) environments. The complexity and diversity of 5G networks result in a great deal of unnecessary features, which may introduce noise into the detection process of an intrusion detection system (IDS) and reduce the generalization ability of the model. This paper aims to improve the performance of the IDS in

APA, Harvard, Vancouver, ISO, and other styles

35

Dhoot, A., A. N. Nazarov, and I. M. Voronkov. "Genetic programming support vector machine model for a wireless intrusion detection system." Russian Technological Journal 10, no. 6 (2022): 20–27. http://dx.doi.org/10.32362/2500-316x-2022-10-6-20-27.

Full text

Abstract:

Objectives. The rapid penetration of wireless communication technologies into the activities of both humans and Internet of Things (IoT) devices along with their widespread use by information consumers represents an epochal phenomenon. However, this is accompanied by the growing intensity of successful information attacks, involving the use of bot attacks via IoT, which, along with network attacks, has reached a critical level. Under such circumstances, there is an increasing need for new technological approaches to developing intrusion detection systems based on the latest achievements of art

APA, Harvard, Vancouver, ISO, and other styles

36

Goryunov, Maxim Nikolaevich, Andrey Georgievich Matskevich, and Dmitry Aleksandrovich Rybolovlev. "Synthesis of a Machine Learning Model for Detecting Computer Attacks Based on the CICIDS2017 Dataset." Proceedings of the Institute for System Programming of the RAS 32, no. 5 (2020): 81–94. http://dx.doi.org/10.15514/ispras-2020-32(5)-6.

Full text

Abstract:

The paper deals with the construction and practical implementation of the model of computer attack detection based on machine learning methods. Among available public datasets one of the most relevant was chosen - CICIDS2017. For this dataset, the procedures of data preprocessing and sampling were developed in detail. In order to reduce computation time, the only class of computer attacks (brute force, XSS, SQL injection) was left in the training set. The procedure of feature space construction is described sequentially, which allowed to significantly reduce its dimensions - from 85 to 10 most

APA, Harvard, Vancouver, ISO, and other styles

37

Vasilica, Bogdan-Valentin, Florin-Daniel Anton, Radu Pietraru, Silvia-Oana Anton, and Beatrice-Nicoleta Chiriac. "Enhancing Security in Smart Robot Digital Twins Through Intrusion Detection Systems." Applied Sciences 15, no. 9 (2025): 4596. https://doi.org/10.3390/app15094596.

Full text

Abstract:

This paper investigates the integration of intrusion detection systems (IDSs) within Digital Twin (DT) architectures to enhance cybersecurity in industrial environments. Using the CICIDS2017, CIC Modbus, and 4SICS 2015 datasets, we evaluate the performance of Random Forest (RF) and Support Vector Machine (SVM) in detecting network intrusions. Results indicate that RF achieves an accuracy of 99.9% for CICIDS2017, with high precision, recall, and low false positives. In contrast, SVM exhibits an accuracy of 94.2% for the same dataset, struggling with high rates of false positives and moderate re

APA, Harvard, Vancouver, ISO, and other styles

38

Imran, Faisal Jamil, and Dohyeun Kim. "An Ensemble of a Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments." Sustainability 13, no. 18 (2021): 10057. http://dx.doi.org/10.3390/su131810057.

Full text

Abstract:

The connectivity of our surrounding objects to the internet plays a tremendous role in our daily lives. Many network applications have been developed in every domain of life, including business, healthcare, smart homes, and smart cities, to name a few. As these network applications provide a wide range of services for large user groups, the network intruders are prone to developing intrusion skills for attack and malicious compliance. Therefore, safeguarding network applications and things connected to the internet has always been a point of interest for researchers. Many studies propose solut

APA, Harvard, Vancouver, ISO, and other styles

39

Coronel Gaviro, Javier, and Akram Boukhamla. "CICIDS2017 Dataset: Performance Improvements and Validation as a Robust Intrusion Detection System Testbed." International Journal of Information and Computer Security 1, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijics.2021.10039325.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Boukhamla, Akram, and Javier Coronel Gaviro. "CICIDS2017 dataset: performance improvements and validation as a robust intrusion detection system testbed." International Journal of Information and Computer Security 16, no. 1/2 (2021): 20. http://dx.doi.org/10.1504/ijics.2021.117392.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Alsameraee, Amer Abulmajeed Abdulrahman, and Mahmood Khalel Ibrahem. "Toward Constructing a Balanced Intrusion Detection Dataset." Samarra Journal of Pure and Applied Science 2, no. 3 (2021): 132–42. http://dx.doi.org/10.54153/sjpas.2020.v2i3.86.

Full text

Abstract:

Several Intrusion Detection Systems (IDS) have been proposed in the current decade. Most datasets which associate with intrusion detection dataset suffer from an imbalance class problem. This problem limits the performance of classifier for minority classes. This paper has presented a novel class imbalance processing technology for large scale multiclass dataset, referred to as BMCD. Our algorithm is based on adapting the Synthetic Minority Over-Sampling Technique (SMOTE) with multiclass dataset to improve the detection rate of minority classes while ensuring efficiency. In this work we have b

APA, Harvard, Vancouver, ISO, and other styles

42

Xu, Congyuan, Donghui Li, Zihao Liu, Jun Yang, Qinfeng Shen, and Ningbing Tong. "Few-shot network intrusion detection method based on multi-domain fusion and cross-attention." PLOS One 20, no. 7 (2025): e0327161. https://doi.org/10.1371/journal.pone.0327161.

Full text

Abstract:

Deep learning methods have achieved remarkable progress in network intrusion detection. However, their performance often deteriorates significantly in real-world scenarios characterized by limited attack samples and substantial domain shifts. To address this challenge, we propose a novel few-shot intrusion detection method that integrates multi-domain feature fusion with a bidirectional cross-attention mechanism. Specifically, the method adopts a dual-branch feature extractor to jointly capture spatial and frequency domain characteristics of network traffic. The frequency domain features are o

APA, Harvard, Vancouver, ISO, and other styles

43

Farahmandnia, Feraidoon, and Serhat Özekes. "ENHANCED DDoS ATTACK DETECTION THROUGH HYBRID MACHINE LEARNING TECHNIQUES." İstanbul Ticaret Üniversitesi Teknoloji ve Uygulamalı Bilimler Dergisi 7, no. 2 (2025): 275–307. https://doi.org/10.56809/icujtas.1513881.

Full text

Abstract:

This research focuses on enhancing the detection mechanisms for Distributed Denial of Service (DDoS) attacks using advanced machine learning techniques. We explore two innovative approaches: a metaclassifier stacking model and a transfer learning model, utilizing the CICDDoS2019 and CICIDS2017 datasets for training and evaluation. The first approach integrates K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF) algorithms through a logistic regression metaclassifier. This ensemble method harnesses the strengths of each algorithm, leading to improved metrics such as

APA, Harvard, Vancouver, ISO, and other styles

44

Bibi, Aysha, Gabriel Avelino Sampedro, Ahmad Almadhor, Abdul Rehman Javed, and Tai-hoon Kim. "A Hypertuned Lightweight and Scalable LSTM Model for Hybrid Network Intrusion Detection." Technologies 11, no. 5 (2023): 121. http://dx.doi.org/10.3390/technologies11050121.

Full text

Abstract:

Given the increasing frequency of network attacks, there is an urgent need for more effective network security measures. While traditional approaches such as firewalls and data encryption have been implemented, there is still room for improvement in their effectiveness. To effectively address this concern, it is essential to integrate Artificial Intelligence (AI)-based solutions into historical methods. However, AI-driven approaches often encounter challenges, including lower detection rates and the complexity of feature engineering requirements. Finding solutions to overcome these hurdles is

APA, Harvard, Vancouver, ISO, and other styles

45

Balla, Asaad, Mohamed Hadi Habaebi, Elfatih A. A. Elsheikh, Md Rafiqul Islam, and F. M. Suliman. "The Effect of Dataset Imbalance on the Performance of SCADA Intrusion Detection Systems." Sensors 23, no. 2 (2023): 758. http://dx.doi.org/10.3390/s23020758.

Full text

Abstract:

Integrating IoT devices in SCADA systems has provided efficient and improved data collection and transmission technologies. This enhancement comes with significant security challenges, exposing traditionally isolated systems to the public internet. Effective and highly reliable security devices, such as intrusion detection system (IDSs) and intrusion prevention systems (IPS), are critical. Countless studies used deep learning algorithms to design an efficient IDS; however, the fundamental issue of imbalanced datasets was not fully addressed. In our research, we examined the impact of data imba

APA, Harvard, Vancouver, ISO, and other styles

46

Maseer, Ziadoon Kamil, Robiah Yusof, Nazrulazhar Bahaman, Salama A. Mostafa, and Cik Feresa Mohd Foozy. "Benchmarking of Machine Learning for Anomaly Based Intrusion Detection Systems in the CICIDS2017 Dataset." IEEE Access 9 (2021): 22351–70. http://dx.doi.org/10.1109/access.2021.3056614.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Hindy, Hanan, Robert Atkinson, Christos Tachtatzis, Jean-Noël Colin, Ethan Bayne, and Xavier Bellekens. "Utilising Deep Learning Techniques for Effective Zero-Day Attack Detection." Electronics 9, no. 10 (2020): 1684. http://dx.doi.org/10.3390/electronics9101684.

Full text

Abstract:

Machine Learning (ML) and Deep Learning (DL) have been used for building Intrusion Detection Systems (IDS). The increase in both the number and sheer variety of new cyber-attacks poses a tremendous challenge for IDS solutions that rely on a database of historical attack signatures. Therefore, the industrial pull for robust IDSs that are capable of flagging zero-day attacks is growing. Current outlier-based zero-day detection research suffers from high false-negative rates, thus limiting their practical use and performance. This paper proposes an autoencoder implementation for detecting zero-da

APA, Harvard, Vancouver, ISO, and other styles

48

Ahmed, Meaad, Qutaiba Alasad, Jiann-Shiun Yuan, and Mohammed Alawad. "Re-Evaluating Deep Learning Attacks and Defenses in Cybersecurity Systems." Big Data and Cognitive Computing 8, no. 12 (2024): 191. https://doi.org/10.3390/bdcc8120191.

Full text

Abstract:

Cybersecurity attacks pose a significant threat to the security of network systems through intrusions and illegal communications. Measuring the vulnerability of cybersecurity is crucial for refining the overall system security to further mitigate potential security risks. Machine learning (ML)-based intrusion detection systems (IDSs) are mainly designed to detect malicious network traffic. Unfortunately, ML models have recently been demonstrated to be vulnerable to adversarial perturbation, and therefore enable potential attackers to crash the system during normal operation. Among different at

APA, Harvard, Vancouver, ISO, and other styles

49

Xu, Congyuan, Yong Zhan, Guanghui Chen, Zhiqiang Wang, Siqing Liu, and Weichen Hu. "Elevated few-shot network intrusion detection via self-attention mechanisms and iterative refinement." PLOS ONE 20, no. 1 (2025): e0317713. https://doi.org/10.1371/journal.pone.0317713.

Full text

Abstract:

The network intrusion detection system (NIDS) plays a critical role in maintaining network security. However, traditional NIDS relies on a large volume of samples for training, which exhibits insufficient adaptability in rapidly changing network environments and complex attack methods, especially when facing novel and rare attacks. As attack strategies evolve, there is often a lack of sufficient samples to train models, making it difficult for traditional methods to respond quickly and effectively to new threats. Although existing few-shot network intrusion detection systems have begun to addr

APA, Harvard, Vancouver, ISO, and other styles

50

Salman, Wisam Ali Hussein, and Chan Huah Yong. "Overview of the CICIoT2023 Dataset for Internet of Things Intrusion Detection Systems." Mesopotamian Journal of Big Data 2025 (June 10, 2025): 50–60. https://doi.org/10.58496/mjbd/2025/004.

Full text

Abstract:

The rapid expansion of the use of the Internet of Things (IoT) has encouraged many attackers to exploit the vulnerabilities in these networks to violate data privacy or disrupt service; they are easy targets due to the diversity of devices within the network, which has led to the loss of unified security standards. intrusion detection system (IDS) play a pivotal role in securing IoT networks by monitoring inbound and outbound traffic to these networks and issuing a security alarm when there is an attack; moreover, they respond directly to these security threats to prevent them from harming the

APA, Harvard, Vancouver, ISO, and other styles

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