DeBot: A deep learning-based model for bot detection in industrial internet-of-things

Jayalaxmi, P.L.S.; Kumar, Gulshan; Saha, Rahul; Conti, Mauro; Kim, Tai-hoon; Thomas, Reji

doi:10.1016/j.compeleceng.2022.108214

Cited by 22 publications

(5 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A subsequent study [33] surpassed these results with F1 scores of 0.9 and 0.92 using novelty and outlier detection. Another work [31] reported an impressive accuracy of 0.998, though it did not provide an F1 score. The best F1 score is documented in [32] where they combined NF-UNSW-NB15 with other NetFlow NIDS datasets into a single dataset named NF-UQ-NIDS.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Network Anomaly Detection Using Quantum Neural Networks on Noisy Quantum Computers

Kukliansky,

Orescanin,

Bollmann

et al. 2024

IEEE Trans. Quantum Eng.

View full text Add to dashboard Cite

The escalating threat and impact of network-based attacks necessitate innovative intrusion detection systems. Machine learning has shown promise, with recent strides in Quantum Machine Learning (QML) offering new avenues. However, the potential of quantum computing is tempered by challenges in current noisy intermediate-scale quantum (NISQ) era machines. In this study, we explore Quantum Neural Networks (QNNs) for intrusion detection, optimizing their performance within current quantum computing limitations. Our approach includes efficient classical feature encoding, QNN classifier selection, and performance tuning leveraging current quantum computational power. This study culminates in an optimized multi-layered QNN architecture for network intrusion detection. A small version of the proposed architecture was implemented on IonQ's Aria-1 quantum computer, achieving a notable 0.86 F1 score using the NF-UNSW-NB15 dataset. Additionally, we introduce a novel metric, certainty factor, laying the foundation for future integration of uncertainty measures in quantum classification outputs. Moreover, this factor is used to predict the noise susceptibility of our quantum binary classification system.

show abstract

Section: Related Workmentioning

confidence: 99%

“…The NF-UNSW-NB15 dataset has been used in several ML studies [31] [32] [33], and provides a robust foundation for our QML investigation. It has 1,623,118 labeled NetFlow samples, where 72,406(4.4%) are malicious and each sample has 12 features.…”

Section: B the Datasetmentioning

confidence: 99%

Network Anomaly Detection Using Quantum Neural Networks on Noisy Quantum Computers

Kukliansky,

Orescanin,

Bollmann

et al. 2024

IEEE Trans. Quantum Eng.

View full text Add to dashboard Cite

show abstract

“…We apply the model to the BOT-IoT dataset after collecting, preprocessing, and normalizing the data. In [90]The DeBot model, a deep learning tool for BoT detection in industrial network traffic, makes use of a unique Cascade Forward Back Propagation Neural Network (CFBPNN) with a subset of features selected using the correlationbased feature selection (CFS) technique. It has been tested extensively on five BoT-IoT datasets: NF-UNSW-NB15, NF-ToN-IoT, NF-BoT-IoT, NF-CSE-CIC-IDS2018, and ToN-IoT-Windows.…”

Section: B Features Selection Optimizedmentioning

confidence: 99%

Exploring Lightweight Deep Learning Techniques for Intrusion Detection Systems in IoT Networks: A Survey

Hind Ali abdul Hassan

2024

jes

View full text Add to dashboard Cite

The proliferation of Internet users has coincided with a commensurate increase in the amount of very important, sensitive, and private information being transferred across the Internet. Malicious actors are increasingly targeting networks to breach them and obtain illegal access to critical information since this trend has revealed holes in security systems. In addition to endangering the privacy of the data concerned, these breaches disrupt the smooth functioning of systems. Therefore, in light of these dangers, intrusion detection systems (IDSs) are now an essential part of any cybersecurity program. The goal of these systems is to detect and report any suspicious activity by constantly monitoring and analyzing network traffic. Numerous review articles have investigated various methods for network intrusion detection. To improve detection accuracy while keeping computing efficiency high, this survey study investigates lightweight deep learning techniques for intrusion detection systems. These techniques include pruning, quantization, clustering, and collaborative optimization. This study analyzes five different types of new real-world traffic datasets (i.e., CSE-CIC- IDS2018, NSL-KDD, Bot-IoT, ToN IoT Network, and UNSW-NB15) and evaluates the performance of several machine learning and deep learning techniques. This survey provides metrics for measuring the accuracy of intrusion detection across various systems, which may be used to assess performance.

show abstract

“…An IoT NetFlow-based dataset generated using the BoT-IoT dataset, named NF-BoT-IoT [9]. The features were extracted from the publicly available pcap files and the flows were labelled with their respective attack categories.…”

Section: Datasetsmentioning

confidence: 99%

Classification of IoT intrusion detection data based on WGAN-gp and E-GraphSAGE

Wang,

Dong,

et al. 2023

Third International Conference on Green Communication, Network, and Internet of Things (CNIoT 2023)

View full text Add to dashboard Cite

In recent years, the frequency and complexity of IoT network attacks have significantly increased. NIDS, strategically located in IoT network nodes, is an essential tool for monitoring traffic and detecting and mitigating network-based attacks. However, with the significant increase in computer network attacks, many datasets used for training suffer from imbalanced data problems. Therefore, to address the traffic characteristics of IoT networks and the issue of imbalanced data, this paper proposes an intrusion detection method that combines graph neural networks(E-GraphSAGE) and generative adversarial networks. Based on experiments using datasets NF-BoT-IoT, we found that training ML classifiers on datasets balanced with synthetic samples generated by WGAN-gp increased their prediction accuracy to 93.7% .

show abstract

DeBot: A deep learning-based model for bot detection in industrial internet-of-things

Cited by 22 publications

References 13 publications

Network Anomaly Detection Using Quantum Neural Networks on Noisy Quantum Computers

Network Anomaly Detection Using Quantum Neural Networks on Noisy Quantum Computers

Exploring Lightweight Deep Learning Techniques for Intrusion Detection Systems in IoT Networks: A Survey

Classification of IoT intrusion detection data based on WGAN-gp and E-GraphSAGE

Contact Info

Product

Resources

About