One of the most common causes of lack of continuity of online systems stems from a widely popular Cyber Attack known as Distributed Denial of Service (DDoS), in which a network of infected devices (botnet) gets exploited to flood the computational capacity of services through the commands of an attacker. This attack is made by leveraging the Domain Name System (DNS) technology through Domain Generation Algorithms (DGAs), a stealthy connection strategy that yet leaves suspicious data patterns. To detect such threats, advances in their analysis have been made. For the majority, they found Machine Learning (ML) as a solution, which can be highly effective in analyzing and classifying massive amounts of data. Although strongly performing, ML models have a certain degree of obscurity in their decision-making process. To cope with this problem, a branch of ML known as Explainable ML tries to break down the black-box nature of classifiers and make them interpretable and human-readable. This work addresses the problem of Explainable ML in the context of botnet and DGA detection, which at the best of our knowledge, is the first to concretely break down the decisions of ML classifiers when devised for botnet/DGA detection, therefore providing global and local explanations.

Explaining Machine Learning DGA Detectors from DNS Traffic Data / Piras, Giorgio; Pintor, Maura; Demetrio, Luca; Biggio, Battista. - (2022). (Intervento presentato al convegno ITASEC tenutosi a Roma).

Explaining Machine Learning DGA Detectors from DNS Traffic Data

Giorgio Piras
Primo
;
2022

Abstract

One of the most common causes of lack of continuity of online systems stems from a widely popular Cyber Attack known as Distributed Denial of Service (DDoS), in which a network of infected devices (botnet) gets exploited to flood the computational capacity of services through the commands of an attacker. This attack is made by leveraging the Domain Name System (DNS) technology through Domain Generation Algorithms (DGAs), a stealthy connection strategy that yet leaves suspicious data patterns. To detect such threats, advances in their analysis have been made. For the majority, they found Machine Learning (ML) as a solution, which can be highly effective in analyzing and classifying massive amounts of data. Although strongly performing, ML models have a certain degree of obscurity in their decision-making process. To cope with this problem, a branch of ML known as Explainable ML tries to break down the black-box nature of classifiers and make them interpretable and human-readable. This work addresses the problem of Explainable ML in the context of botnet and DGA detection, which at the best of our knowledge, is the first to concretely break down the decisions of ML classifiers when devised for botnet/DGA detection, therefore providing global and local explanations.
2022
ITASEC
Machine Learning, Explainability, Cybersecurity, DNS, Network Security, Monitoring and Detection
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Explaining Machine Learning DGA Detectors from DNS Traffic Data / Piras, Giorgio; Pintor, Maura; Demetrio, Luca; Biggio, Battista. - (2022). (Intervento presentato al convegno ITASEC tenutosi a Roma).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1672418
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