RExACtor: Automatic Regular Expression Signature Generation for Stateless Packet Inspection

Kapoor, Maya; Fuchs, Garrett; Quance, Jonathan

doi:10.1109/nca53618.2021.9685959

Cited by 5 publications

(2 citation statements)

References 32 publications

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“…This algorithm aimed to automatically generate regular expression signatures for targeted protocols [17]. Later, in software engineers research, reguler expression is used for many tasks [18]- [22]. Regular expression can be complex, making them hard for developers to write and understand.…”

Section: Methodsmentioning

confidence: 99%

Scientific reference style using rule-based machine learning

Helen,

Pradana,

Afif

2023

Int. J. Adv. Intell. Informatics

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Regular Expressions (RegEx) can be employed as a technique for supervised learning to define and search for specific patterns inside text. This work devised a method that utilizes regular expressions to convert the reference style of academic papers into several styles, dependent on the specific needs of the target publication or conference. Our research aimed to detect distinctive patterns of reference styles using RegEx and compare them with a dataset including various reference styles. We gathered a diverse range of reference format categories, encompassing seven distinct classes, from various sources such as academic papers, journals, conference proceedings, and books. Our approach involves employing RegEx to convert one referencing format to another based on the user's specific preferences. The proposed model demonstrated an accuracy of 57.26% for book references and 57.56% for journal references. We used the similarity ratio and Levenshtein distance to evaluate the dataset's performance. The model achieved a 97.8% similarity ratio with a Levenshtein distance of 2. Notably, the APA style for journal references yielded the best results. However, the effectiveness of the extraction function varies depending on the reference style. For APA style, the model showed a 99.97% similarity ratio with a Levenshtein distance of 1. Overall, our proposed model outperforms baseline machine learning models in this task. This study introduces an automated program that utilizes regular expressions to modify academic reference formats. This will enhance the efficiency, precision, and adaptability of academic publishing.

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Section: Methodsmentioning

confidence: 99%

Scientific reference style using rule-based machine learning

Helen,

Pradana,

Afif

2023

Int. J. Adv. Intell. Informatics

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“…It is an useful method for locating sub-strings in a string and allows for the identification and removal of errors and abuses. Regular expressions are used in many "search and replace" functionalities of programs because they are seen to be a particularly powerful matching technique for strings [28].…”

Section: B Tweets Pretreatment Stepmentioning

confidence: 99%

Emotion Processing by Applying a Fuzzy-Based Vader Lexicon and a Parallel Deep Belief Network Over Massive Data

et al. 2022

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Emotion processing has been a very intense domain of investigation in data analysis and NLP during the previous few years. Currently, the algorithms of the deep neural networks have been applied for opinion mining tasks with good results. Among various neuronal models applied for opinion mining a deep belief network (DBN) model has gained more attention. In this proposal, we have developed a combined classifier based on fuzzy Vader lexicon and a parallel deep belief network for emotion analysis. We have implemented multiple pretreatment techniques to improve the quality and soundness of the data and eliminate disturbing data. Afterward, we have performed a semi-automatic dataset labeling using a combination of two different methods: Mamdani's fuzzy system and Vader lexicon. As well, we have applied four feature extractors, which are: GloVe, TFIDF (Trigram), TFIDF (Bigram), TFIDF (Unigram) with the aim of transforming each incoming tweet into a digital value vector. In addition, we have integrated three feature selectors, namely: The ANOVA method, the chi-square approach and the mutual information technique with the objective of selecting the most relevant features. Further, we have implemented the DBN as classifier for classifying each inputted tweet into three categories: neutral, positive or negative. At the end, we have deployed our proposed approach in parallel way employing both Hadoop and Spark framework with the purpose of overcoming the problem of long runtime of massive data. Furthermore, we have carried out a comparison between our newly suggested hybrid approach and alternative hybrid models available in the literature. From the experimental findings, it was found that our suggested vague parallel approach is more powerful than the baseline patterns in terms of false negative rate (1.33%), recall (99.75%), runtime (32.95s), convergence, stability, F1 score (99.53%), accuracy (98.96%), error rate (1.04%), kappa-Static (99.1%), complexity, false positive rate (0.25%), precision rate (97.59%) and specificity rate (98.67%). As a conclusion, our vague parallel approach outperforms baseline and deep learning models, as well as certain other approaches chosen from the literature.

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