Combining clustering and classification algorithms for automatic bot detection: a case study on posts about COVID-19

Lira, Diego Bezerra; Xavier, Fernando; Digiampietri, Luciano Antônio

doi:10.1145/3466933.3466970

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…LIME is also used in JITBot [204], An Explainable Just-In-Time Defect Prediction Bot, and in [205], a bot-type classification schema. SHAP and LIME are used in [206] for game BOT detection, while in [207], the authors used a Decision Tree model, Explainable by definition, for automatic detection on Twitter with a particular case study on posts about COVID-19.…”

Section: ) Explainable Artificial Intelligence In Bot(net) Detectionmentioning

confidence: 99%

Explainable Artificial Intelligence in CyberSecurity: A Survey

et al. 2022

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Nowadays, Artificial Intelligence (AI) is widely applied in every area of human being's daily life. Despite the AI benefits, its application suffer from the opacity of complex internal mechanisms and doesn't satisfy by design the principles of Explainable Artificial Intelligence (XAI). The lack of transparency further exacerbates the problem in the field of Cybersecurity because entrusting crucial decisions to a system that cannot explain itself presents obvious dangers. There are several methods in the literature capable of providing explainability of AI results. Anyway, the application of XAI in Cybersecurity can be a doubleedged sword. It substantially improves the Cybersecurity practices but simultaneously leaves the system vulnerable to adversary attacks. Therefore, there is a need to analyze the state-of-the-art of XAI methods in Cybersecurity to provide a clear vision for future research. This study presents an in-depth examination of the application of XAI in Cybersecurity. It considers more than 300 papers to comprehensively analyze the main Cybersecurity application fields, like Intrusion Detection Systems,Malware detection, Phishing and Spam detection, BotNets detection, Fraud detection, Zero-Day vulnerabilities, Digital Forensics and Crypto-Jacking . Specifically, this study focuses on the explainability methods adopted or proposed in these fields, pointing out promising works and new challenges.

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Section: ) Explainable Artificial Intelligence In Bot(net) Detectionmentioning

confidence: 99%

Explainable Artificial Intelligence in CyberSecurity: A Survey

et al. 2022

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Evaluating social bots detection approaches in different domains

Morais

Digiampietri

2022

XVIII Brazilian Symposium on Information Systems

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Spot the bot: the inverse problems of NLP

Gromov,

Dang,

Kogan

et al. 2024

PeerJ Computer Science

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This article concerns the problem of distinguishing human-written and bot-generated texts. In contrast to the classical problem formulation, in which the focus falls on one type of bot only, we consider the problem of distinguishing texts written by any person from those generated by any bot; this involves analysing the large-scale, coarse-grained structure of the language semantic space. To construct the training and test datasets, we propose to separate not the texts of bots, but bots themselves, so the test sample contains the texts of those bots (and people) that were not in the training sample. We aim to find efficient and versatile features, rather than a complex classification model architecture that only deals with a particular type of bots. In the study we derive features for human-written and bot generated texts, using clustering (Wishart and K-Means, as well as fuzzy variations) and nonlinear dynamic techniques (entropy-complexity measures). We then deliberately use the simplest of classifiers (support vector machine, decision tree, random forest) and the derived characteristics to identify whether the text is human-written or not. The large-scale simulation shows good classification results (a classification quality of over 96%), although varying for languages of different language families.

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Combining clustering and classification algorithms for automatic bot detection: a case study on posts about COVID-19

Cited by 3 publications

References 15 publications

Explainable Artificial Intelligence in CyberSecurity: A Survey

Explainable Artificial Intelligence in CyberSecurity: A Survey

Evaluating social bots detection approaches in different domains

Spot the bot: the inverse problems of NLP

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