Seven Pitfalls of Using Data Science in Cybersecurity

Johnstone, Mike; Peacock, Matthew

doi:10.1007/978-3-030-38788-4_6

Cited by 8 publications

(4 citation statements)

References 22 publications

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“…They are more voluminous, noisier, and more complex than the ones used in regular modeling practices for analyzing algorithms performance and comparisons. 333 As observed, these problems are not exclusive of PSE areas but of the modeling community. Several solutions have been proposed for solving or, at least, mitigating this issues.…”

Section: Critical Analysis and Identified Limitationsmentioning

confidence: 95%

“…In addition, the use of an algorithm for a typical case does not guarantee the same result in a different case. , In addition, real world problems have a different behavior of idealized or synthetic data sets. They are more voluminous, noisier, and more complex than the ones used in regular modeling practices for analyzing algorithms performance and comparisons . As observed, these problems are not exclusive of PSE areas but of the modeling community.…”

Section: Critical Analysis and Identified Limitationsmentioning

confidence: 99%

“…Data-driven modeling in PSE, as other scientific fields, is used for constructing a map of the reality. Some authors, as in ref , use a reduced data approach and consider data as equivalent to territory. However, for PSE modeling, data is extracted from the reality and the objective is to build a map with a desired level of accuracy.…”

Section: Critical Analysis and Identified Limitationsmentioning

confidence: 99%

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Machine Learning Algorithms Used in PSE Environments: A Didactic Approach and Critical Perspective

Fuentes-Cortés

Flores‐Tlacuahuac

Nigam

2022

Ind. Eng. Chem. Res.

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This work addresses recent developments for solving problems in process systems engineering based on machine learning algorithms. A general description of most popular supervised and unsupervised learning algorithms is presented, as well as the applications addressed in the current literature. Because of their wide usage and potential applications, support vector machines and neural networks are addressed as special cases. The approach used is fundamentally didactic. Therefore, several of the references included are recommendations for novice readers interested in entering the area of machine learning and data science. The applications were selected considering simplicity, popularity of the application, and accessibility for inexperienced readers, but with knowledge of the process systems engineering area. Finally, a critical perspective for future development and applications is provided. Epistemological issues and modeling limitations are discussed in order to analyze the real significance of data-driven strategies as well as a questioning of academic marketing in recent years.

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Section: Critical Analysis and Identified Limitationsmentioning

confidence: 95%

Section: Critical Analysis and Identified Limitationsmentioning

confidence: 99%

Section: Critical Analysis and Identified Limitationsmentioning

confidence: 99%

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Machine Learning Algorithms Used in PSE Environments: A Didactic Approach and Critical Perspective

Fuentes-Cortés

Flores‐Tlacuahuac

Nigam

2022

Ind. Eng. Chem. Res.

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“…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 ]. To overcome some of these issues, a variant of a recurrent neural network, called a long short-term memory or LSTM is attractive.…”

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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