Multi-Class Network Traffic Generators and Classifiers Based on Neural Networks

Bikmukhamedov, Radion; Nadeev, Adel

doi:10.1109/ieeeconf51389.2021.9416067

Cited by 9 publications

(3 citation statements)

References 12 publications

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“…Transformers are a deep learning architecture originally designed for NLP tasks like translation and text classification [80]. However, their success has extended to other domains, including network traffic analysis and classification [81], image recognition [82], and intrusion detection. This is due to their ability for parallel processing, transfer learning, and achieving high accuracy.…”

Section: Related Workmentioning

confidence: 99%

Securing the IoT Cyber Environment: Enhancing Intrusion Anomaly Detection With Vision Transformers

Sana,

Nazir,

Yang

et al. 2024

IEEE Access

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Background and Motivation:The ever-expanding Internet of Things (IoT) landscape presents a double-edged sword. While it fosters interconnectedness, the vast amount of data generated by IoT devices creates a larger attack surface for cybercriminals. Intrusions in these environments can have severe consequences. To combat this growing threat, robust intrusion detection systems (IDS) are crucial. The data comprised by this attack is multivariate, highly complex, non-stationary, and nonlinear. To extract the complex patterns from this complex data, we require the most robust, optimized tools. Methods: Machine learning (ML) and deep learning (DL) have emerged as powerful tools for IDSs, offering high accuracy in detecting and preventing security breaches. This research delves into anomaly detection, a technique that identifies deviations from normal system behavior, potentially indicating attacks. Given the complexity of anomaly data, we explore methods to improve detection performance. Proposed Approach: This research investigates the design and evaluation of a novel IDS. We leverage and optimize supervised ML methods like tree-based Support Vector Machines (SVM), ensemble methods, and neural networks (NN) alongside the cutting-edge DL approach of long short-term memory (LSTM) and vision transformers (ViT). We optimized the hyperparameters of these algorithms using a robust Bayesian optimization approach. Results: The implemented ML models achieved impressive training accuracy, with Random Forest and Ensemble Bagged Tree surpassing 99.90% of accuracy, an AUC of 1.00, an F1-score, and a balanced Matthews Correlation Coefficient (MCC) of 99.78%. While the initial deep learning LSTM model yielded an accuracy of 99.97%, the proposed ViT architecture significantly boosted performance with 100% of all metrics, along with a validation accuracy of 78.70% and perfect training accuracy. Conclusion: Our findings demonstrate the potential of the proposed methods for enhanced attack and intrusion detection. This improved detection capability can be instrumental in safeguarding system integrity, identifying fraudulent activity, and optimizing system performance in IoT networks.

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Section: Related Workmentioning

confidence: 99%

Securing the IoT Cyber Environment: Enhancing Intrusion Anomaly Detection With Vision Transformers

Sana,

Nazir,

Yang

et al. 2024

IEEE Access

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“…Transformers have also become very prominent in the network and cybersecurity domain over recent years. Bikmukhamedov et al [49], [50] introduced a new generative transformer-based model for network traffic that serves the purpose of generating and classifying network data. The model uses packet size and inter-packet time sequences as flow features to simplify inputs and can be trained in two ways: for generating network traffic and as a network flow classifier.…”

Section: Transformersmentioning

confidence: 99%

PAC-GPT: A Novel Approach to Generating Synthetic Network Traffic With GPT-3

Kholgh,

Kostakos

2023

IEEE Access

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The application of machine learning models, particularly in cybersecurity, has surged significantly in the past few years. However, the effectiveness of these models is predominantly tethered to the quality and breadth of the training data they ingest. The scarcity of realistic datasets within the cybersecurity field constitutes a considerable challenge to the development of industry-grade tools intended for real-world application scenarios. Specifically, current datasets are either significantly outdated or fall short on both qualitative and quantitative fronts, primarily because many organizations exhibit reluctance in data sharing, stemming from privacy concerns or the potential threat to trade secrets. To address this challenge, the paper introduces PAC-GPT, a novel framework to generate reliable synthetic data for machine learning methods based on Open AI's Generative Pre-trained Transformer 3 (GPT-3). The core components of this framework are two modules, namely a Flow Generator, which is responsible for capturing and regenerating patterns in a series of network packets, and Packet Generator, which can generate individual network packets given the network flow. We also propose a packet generator based on LLM chaining and then proceed to assess, compare, and evaluate its performance using metrics such as loss, accuracy and success rate, concluding that transformers are a suitable approach for synthetic packet generation with minimal fine-tuning performed. Lastly, a streamlined command line interface (CLI) tool has been devised to facilitate the seamless access of this innovative data generation strategy by professionals from various disciplines.

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“…В докладе [24] на Международной конференции представлен фреймворк нейронной сети, который позволяет строить многоклассовые модели сетевого трафика, подходящие для задач генерации потоков и классификации.…”

Section: виды машинного обученияunclassified

Analyzing Traffic Identification Methods for Resource Management in SDN

Dmitrieva,

Okuneva,

Elagin

2023

jour

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The article is devoted to the analysis of traffic classification methods in SDN network. The review of analytical approaches of traffic identification to identify the solutions used in them, as well as assessing their applicability in the SDN network. Types of machine learning are considered and input parameters are analyzed. The methods of intelligent analysis covered in the scientific articles are systematized according to the following criteria: traffic identification parameters, neural network model, identification accuracy. Based on the analysis of the review results, the conclusion is made about the possibility of applying the considered solutions, as well as the need to form a scheme of SDN network with a module of artificial intelligence elements for load balancing.

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Multi-Class Network Traffic Generators and Classifiers Based on Neural Networks

Cited by 9 publications

References 12 publications

Securing the IoT Cyber Environment: Enhancing Intrusion Anomaly Detection With Vision Transformers

Securing the IoT Cyber Environment: Enhancing Intrusion Anomaly Detection With Vision Transformers

PAC-GPT: A Novel Approach to Generating Synthetic Network Traffic With GPT-3

Analyzing Traffic Identification Methods for Resource Management in SDN

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