Deep learning algorithms for intrusion detection systems in internet of things using CIC-IDS 2017 dataset

Jose, Jinsi; Jose, Deepa V.

doi:10.11591/ijece.v13i1.pp1134-1141

Cited by 22 publications

(5 citation statements)

References 22 publications

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“…Many studies have reported promising results using deep learning architectures on this dataset, classifying network traffic as either benign or malicious (as a binary classification task) [9,11,43]. However, this does not mean that increasing the number of classes will always result in decreased accuracy, and such trends have not been observed.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Algorithms for Raw and Unbalanced Intrusion Detection Data in a Multi-Class Classification Problem

Bacevičius

Paulauskaitė-Tarasevičienė

2023

Applied Sciences

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Various machine learning algorithms have been applied to network intrusion classification problems, including both binary and multi-class classifications. Despite the existence of numerous studies involving unbalanced network intrusion datasets, such as CIC-IDS2017, a prevalent approach is to address the issue by either merging the classes to optimize their numbers or retaining only the most dominant ones. However, there is no consistent trend showing that accuracy always decreases as the number of classes increases. Furthermore, it is essential for cybersecurity practitioners to recognize the specific type of attack and comprehend the causal factors that contribute to the resulting outcomes. This study focuses on tackling the challenges associated with evaluating the performance of multi-class classification for network intrusions using highly imbalanced raw data that encompasses the CIC-IDS2017 and CSE-CIC-IDS2018 datasets. The research concentrates on investigating diverse machine learning (ML) models, including Logistic Regression, Random Forest, Decision Trees, CNNs, and Artificial Neural Networks. Additionally, it explores the utilization of explainable AI (XAI) methods to interpret the obtained results. The results obtained indicated that decision trees using the CART algorithm performed best on the 28-class classification task, with an average macro F1-score of 0.96878.

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Section: Related Workmentioning

confidence: 99%

Machine Learning Algorithms for Raw and Unbalanced Intrusion Detection Data in a Multi-Class Classification Problem

Bacevičius

Paulauskaitė-Tarasevičienė

2023

Applied Sciences

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“…Notable datasets such as CIC-IDS2017, NSL-KDD, and the UNSW-NB15 are widely used benchmarks for evaluating intrusion detection algorithms in IoT networks. Researchers like Jose and Jose [23] and Ashiku and Dagli [7] have extensively utilized these datasets to evaluate LSTM-based IDSs, showcasing improved accuracy and robustness.…”

Section: Comprehensive Evaluation Of Public Datasetsmentioning

confidence: 99%

Enhanced Intrusion Detection with LSTM-Based Model, Feature Selection, and SMOTE for Imbalanced Data

Sayegh,

Dong,

Al-madani

2024

Applied Sciences

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This study introduces a sophisticated intrusion detection system (IDS) that has been specifically developed for internet of things (IoT) networks. By utilizing the capabilities of long short-term memory (LSTM), a deep learning model renowned for its proficiency in modeling sequential data, our intrusion detection system (IDS) effectively discerns between regular network traffic and potential malicious attacks. In order to tackle the issue of imbalanced data, which is a prevalent concern in the development of intrusion detection systems (IDSs), we have integrated the synthetic minority over-sampling technique (SMOTE) into our approach. This incorporation allows our model to accurately identify infrequent incursion patterns. The rebalancing of the dataset is accomplished by SMOTE through the generation of synthetic samples belonging to the minority class. Various strategies, such as the utilization of generative adversarial networks (GANs), have been put forth in order to tackle the issue of data imbalance. However, SMOTE (synthetic minority over-sampling technique) presents some distinct advantages when applied to intrusion detection. The SMOTE is characterized by its simplicity and proven efficacy across diverse areas, including in intrusion detection. The implementation of this approach is straightforward and does not necessitate intricate adversarial training techniques such as generative adversarial networks (GANs). The interpretability of SMOTE lies in its ability to generate synthetic samples that are aligned with the properties of the original data, rendering it well suited for security applications that prioritize transparency. The utilization of SMOTE has been widely embraced in the field of intrusion detection research, demonstrating its effectiveness in augmenting the detection capacities of intrusion detection systems (IDSs) in internet of things (IoT) networks and reducing the consequences of class imbalance. This study conducted a thorough assessment of three commonly utilized public datasets, namely, CICIDS2017, NSL-KDD, and UNSW-NB15. The findings indicate that our LSTM-based intrusion detection system (IDS), in conjunction with the implementation of SMOTE to address data imbalance, outperforms existing methodologies in accurately detecting network intrusions. The findings of this study provide significant contributions to the domain of internet of things (IoT) security, presenting a proactive and adaptable approach to safeguarding against advanced cyberattacks. Through the utilization of LSTM-based deep learning techniques and the mitigation of data imbalance using SMOTE, our AI-driven intrusion detection system (IDS) enhances the security of internet of things (IoT) networks, hence facilitating the wider implementation of IoT technologies across many industries.

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“…Another work that exploits Deep Neural Networks (DNNs) for an IDS is proposed in [23]. The authors carried out a comparative study of IoT IDS with three DL models: DNN, Long Short-Term Memory (LSTM), and CNN.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Network Packet Classification Exploiting Computer Vision Architectures

Paolini,

Valcarenghi,

Maggiani

et al. 2024

IEEE Open J. Commun. Soc.

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This research is partly supported by the project CLEVER (project number 101097560). The project is supported by the Key Digital Technologies Joint Undertaking and its members (including top-up funding by the Italian Ministry of Research and University (MUR)). This work is also partially supported by the European Union under the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU, partnership on "Telecommunications of the Future" (PE00000001 -program "RESTART"), and by the project SmartComputing and Communication at the Edge (SMARTCOM) funded by Sma-RTy and CNR.

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Deep learning algorithms for intrusion detection systems in internet of things using CIC-IDS 2017 dataset

Cited by 22 publications

References 22 publications

Machine Learning Algorithms for Raw and Unbalanced Intrusion Detection Data in a Multi-Class Classification Problem

Machine Learning Algorithms for Raw and Unbalanced Intrusion Detection Data in a Multi-Class Classification Problem

Enhanced Intrusion Detection with LSTM-Based Model, Feature Selection, and SMOTE for Imbalanced Data

Real-Time Network Packet Classification Exploiting Computer Vision Architectures

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