Matrix Profile data mining for BGP anomaly detection

Scott, Ben A.; Johnstone, Michael N.; Szewczyk, Patryk; Richardson, Steven

doi:10.1016/j.comnet.2024.110257

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…Machine learning is one solution to the addition of relevant (authentic) prosodic information to audio. It has been used successfully in other domains, such as network traffic analysis [ 45 , 46 ] and biometrics [ 47 ]. There are several pitfalls, however, as described by Johnstone and Peacock [ 48 ].…”

Section: Related Workmentioning

confidence: 99%

Voice Synthesis Improvement by Machine Learning of Natural Prosody

Kane,

Johnstone,

Szewczyk

2024

Sensors

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Since the advent of modern computing, researchers have striven to make the human–computer interface (HCI) as seamless as possible. Progress has been made on various fronts, e.g., the desktop metaphor (interface design) and natural language processing (input). One area receiving attention recently is voice activation and its corollary, computer-generated speech. Despite decades of research and development, most computer-generated voices remain easily identifiable as non-human. Prosody in speech has two primary components—intonation and rhythm—both often lacking in computer-generated voices. This research aims to enhance computer-generated text-to-speech algorithms by incorporating melodic and prosodic elements of human speech. This study explores a novel approach to add prosody by using machine learning, specifically an LSTM neural network, to add paralinguistic elements to a recorded or generated voice. The aim is to increase the realism of computer-generated text-to-speech algorithms, to enhance electronic reading applications, and improved artificial voices for those in need of artificial assistance to speak. A computer that is able to also convey meaning with a spoken audible announcement will also improve human-to-computer interactions. Applications for the use of such an algorithm may include improving high-definition audio codecs for telephony, renewing old recordings, and lowering barriers to the utilization of computing. This research deployed a prototype modular platform for digital speech improvement by analyzing and generalizing algorithms into a modular system through laboratory experiments to optimize combinations and performance in edge cases. The results were encouraging, with the LSTM-based encoder able to produce realistic speech. Further work will involve optimizing the algorithm and comparing its performance against other approaches.

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Section: Related Workmentioning

confidence: 99%

Voice Synthesis Improvement by Machine Learning of Natural Prosody

Kane,

Johnstone,

Szewczyk

2024

Sensors

Self Cite

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Enhancing Cloud Network Security with Innovative Time Series Analysis

Al-Musawi,

ALMAZRAWE

2024

Preprint

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Cloud computing has revolutionized computing infrastructure abstraction and utilisation, characterized by its cost-effective and high-quality services. However, the challenge of securing cloud networks persists, primarily due to the extensive exchange of data and the inherent complexity of these systems. Anomaly detection emerges as a promising solution to enhance cloud network security, offering insights into system behaviour and alerting operators for further actions. This paper presents a novel time series analysis technique for detecting anomalies in cloud networks. Our approach utilises a multi-dimensional matrix profile, an innovative time series analysis method, to highlight anomalous patterns within multiple features extracted from network traffic streams. Additionally, we employ the Kneedle algorithm to pinpoint the highlighted patterns that identify anomalies. To evaluate the effectiveness of our method, we implemented timestamp-based and index-based methods to two distinct datasets: the most widely used UNSW-NB15 and the recently introduced CICIoT2023 datasets. The results highlight the efficacy of our proposed method in identifying cloud network anomalies. It achieved an impressive accuracy of 99.6% and an F1-score of 99.8% using the timestamp-based analysis method. For the index-based analysis method, the accuracy reached 98%, accompanied by an outstanding F1-score of 99.9%.

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Matrix Profile data mining for BGP anomaly detection

Cited by 2 publications

References 24 publications

Voice Synthesis Improvement by Machine Learning of Natural Prosody

Voice Synthesis Improvement by Machine Learning of Natural Prosody

Enhancing Cloud Network Security with Innovative Time Series Analysis

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