On the self-similarity of traffic generated by network traffic simulators

Fernandes, Diogo A. B.; Neto, Miguel; Soares, Liliana F. B.; Freire, Mário M.; Inácio, Pedro R. M.

doi:10.1016/b978-0-12-800887-4.00010-9

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…The Hurst parameter, also known as the Hurst exponent, emerges as a crucial statistical tool in this scenario. It measures the self-similarity degree in a time series, providing insights into its long-term memory or persistence [16]. When utilized in SCADA's Intrusion Detection Systems (IDS), the Hurst parameter aids in identifying the system's inherent patterns and behaviors, facilitating a deeper comprehension of its standard operational stance.…”

Section: Related Workmentioning

confidence: 99%

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Enhanced CNN-LSTM Deep Learning for SCADA IDS Featuring Hurst Parameter Self-Similarity

Balla,

Habaebi,

Elsheikh

et al. 2024

IEEE Access

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Supervisory Control and Data Acquisition (SCADA) systems are crucial for modern industrial processes and securing them against increasing cyber threats is a significant challenge. This study presents an advanced method for bolstering SCADA security by employing a modified hybrid deep learning model. A key innovation in this work is integrating the Self-similarity Hurst parameter into the dataset alongside a CNN-LSTM model, significantly boosting the Intrusion Detection System's (IDS) capabilities. The Hurst parameter, which quantifies the self-similarity in a dataset, is instrumental in detecting anomalies. Our indepth analysis of the CICIDS2017 dataset sheds light on contemporary attack patterns and network traffic behaviors. The CNN-LSTM architecture was substantially altered by adding multiple convolutional layers with progressively increasing filters, batch normalization for stable training, and dropout layers for regularization. Principal Component Analysis (PCA) was applied for dimensionality reduction, thereby optimizing the dataset. Test results demonstrate the superior performance of the model incorporating the Hurst parameter, achieving 95.21% accuracy and 82.59% recall, significantly surpassing the standard model. The inclusion of the Hurst parameter marks a substantial advancement in identifying emerging threats, while architectural improvements to the CNN-LSTM model led to more robust and accurate intrusion detection in industrial control settings.

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Section: Related Workmentioning

confidence: 99%

“…The "ReLU" activation function is used by our model's CNN layers, which are equipped with increasing filters (16,32,64) and kernel sizes of 1 and 2. This decision is based on ReLU's ability to overcome the vanishing gradient dilemma and accelerate deep network convergence.…”

Section: H Model Architecturementioning

confidence: 99%

Enhanced CNN-LSTM Deep Learning for SCADA IDS Featuring Hurst Parameter Self-Similarity

Balla,

Habaebi,

Elsheikh

et al. 2024

IEEE Access

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“…Una escala bien aceptada por la comunidad científica es la reportada por (Kincaid et al, 1975), presenta de manera resumida en la tabla 1. De otra parte, el parámetro de Hurst, cuantifica la persistencia, auto-similaridad ó memoria de un fenómeno (Mishura and Zili, 2018;Fernandes et al, 2015;Mitra, 2012). Se dice que un proceso dado tiene memoria cuando sus observaciones en un tiempo t 0 (presente) dependen de observaciones pasadas, y simultáneamente éste influirá en su estado futuro (Fernandes et al, 2015).…”

Section: La Escritura Como Un Proceso Cognitivounclassified

“…De otra parte, el parámetro de Hurst, cuantifica la persistencia, auto-similaridad ó memoria de un fenómeno (Mishura and Zili, 2018;Fernandes et al, 2015;Mitra, 2012). Se dice que un proceso dado tiene memoria cuando sus observaciones en un tiempo t 0 (presente) dependen de observaciones pasadas, y simultáneamente éste influirá en su estado futuro (Fernandes et al, 2015). Particularmente, para el fenómeno de la escritura (López-Ortega et al, 2020) han planteado que es posible cuantificar la persistencia de ideas con el parámetro de Hurst.…”

Section: La Escritura Como Un Proceso Cognitivounclassified

Métricas para verificación de autoría y emulación de procesos cognitivos

Pérez-Cortés

Gómez-Pozos²,

Ruíz³

2022

ICBI

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Se proponen dos medidas cuantitativas, el parámetro de Hurst y la legibilidad, para realizar la verificación de autor. Se determinan las cantidades mencionadas para seis diferentes autores considerando seis obras de cada uno de ellos. Con estos valores se construye un espacio de dos dimensiones donde cada punto corresponde a un único autor; midiendo la distancia entre dos puntos de dicho espacio es posible decidir si un texto es atribuible a un autor o no. Adicionalmente dichas medidas proporcionan una interpretación cualitativa, es decir, en términos como la facilidad al leer un texto y si existen palabras, asociados, a pensamientos, que persisten en un texto.

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Network Coding Method for Self-Similar Streaming Media Flow in Wireless Mesh Network

Sheng¹

2018

2018 International Conference on Engineering Simulation and Intelligent Control (ESAIC)

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On the self-similarity of traffic generated by network traffic simulators

Cited by 4 publications

References 16 publications

Enhanced CNN-LSTM Deep Learning for SCADA IDS Featuring Hurst Parameter Self-Similarity

Enhanced CNN-LSTM Deep Learning for SCADA IDS Featuring Hurst Parameter Self-Similarity

Métricas para verificación de autoría y emulación de procesos cognitivos

Network Coding Method for Self-Similar Streaming Media Flow in Wireless Mesh Network

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