Orbinato, Vittorio (2023) A Cyber Threat Intelligence-driven Framework for Adversary Emulation. [Tesi di dottorato]

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Tipologia del documento: Tesi di dottorato
Lingua: English
Titolo: A Cyber Threat Intelligence-driven Framework for Adversary Emulation
Autori:
Autore
Email
Orbinato, Vittorio
vittorio.orbinato@unina.it
Data: 12 Dicembre 2023
Numero di pagine: 118
Istituzione: Università degli Studi di Napoli Federico II
Dipartimento: Ingegneria Elettrica e delle Tecnologie dell'Informazione
Dottorato: Information technology and electrical engineering
Ciclo di dottorato: 36
Coordinatore del Corso di dottorato:
nome
email
Russo, Stefano
stefano.russo@unina.it
Tutor:
nome
email
Cotroneo, Domenico
[non definito]
Natella, Roberto
[non definito]
Data: 12 Dicembre 2023
Numero di pagine: 118
Parole chiave: Adversary Emulation, Cyber Threat Intelligence, Cybersecurity, Proactive Security
Settori scientifico-disciplinari del MIUR: Area 09 - Ingegneria industriale e dell'informazione > ING-INF/05 - Sistemi di elaborazione delle informazioni
Depositato il: 13 Dic 2023 07:55
Ultima modifica: 10 Mar 2026 14:40
URI: http://www.fedoa.unina.it/id/eprint/15653

Abstract

In the ever-evolving landscape of cybersecurity, the challenges faced by organizations in safeguarding their digital assets and data have grown exponentially. Traditional reactive security measures, which focus on responding to attackers after they have breached the perimeter, are no longer sufficient in the current dynamic threat environment, resulting in delayed detection and business damage. To effectively protect against Advanced Persistent Threats (APTs), organizations must shift their paradigm toward proactive security measures. Adversary emulation is the pivotal strategy within this landscape, i.e., a strategy that emulates the TTPs employed by real-world threat actors to anticipate their moves and enhance defensive capabilities accordingly. Unfortunately, adversary emulation also presents some drawbacks that limit its adoption. First, the scenarios emulated with this paradigm are not representative of real-world threat actors since adversary emulation lacks integration with CTI to provide insights into the TTPs employed by APTs. This happens since CTI still comes in unstructured forms, e.g., threat and incident reports written by security analysts, making it challenging to process this information automatically to replicate the attackers' behavior. Second, security practitioners cannot rely upon open-source adversary emulation tools to effectively emulate APTs. These solutions only provide educational emulation without being able to evade basic detection countermeasures in actual deployment scenarios. To address these issues, this dissertation devises a CTI-driven framework for adversary emulation. The framework provides a pipeline to automatically extract attack techniques from CTI documents and generate adversary emulation plans. In addition, it offers a novel solution for adversary emulation (Laccolith) able to perform malicious actions in a non-detectable way, to emulate the behavior of complex APTs realistically. Laccolith was tested against multiple AV/EDR solutions to assess its effectiveness for adversary emulation.

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