"Making AI Work for Cyber Defense: The Accuracy-Robustness Tradeoff "

Hoffman, Wyatt

doi:10.51593/2021ca007

Cited by 4 publications

(6 citation statements)

References 9 publications

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“…-(A.6.2) Automated red agents must be provided with a wide variety of cyber attacks (specified within the MITRE ATT&CK framework) -(A.6.3) along with a variety of algorithmic attacks [59] to address systems vulnerabilities.…”

Section: Resiliencementioning

confidence: 99%

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Automated Cyber Defence: A Review

Vyas¹,

Hannay²,

Bolton³

et al. 2023

Preprint

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Within recent times, cybercriminals have curated a variety of organised and resolute cyber attacks within a range of cyber systems, leading to consequential ramifications to private and governmental institutions. Current security-based automation and orchestrations focus on automating fixed purpose and hard-coded solutions, which are easily surpassed by modern-day cyber attacks. Research within Automated Cyber Defence will allow the development and enabling intelligence response by autonomously defending networked systems through sequential decision-making agents. This article comprehensively elaborates the developments within Automated Cyber Defence through a requirement analysis divided into two sub-areas, namely, automated defence and attack agents and Autonomous Cyber Operation (ACO) Gyms. The requirement analysis allows the comparison of automated agents and highlights the importance of ACO Gyms for their continual development. The requirement analysis is also used to critique ACO Gyms with an overall aim to develop them for deploying automated agents within real-world networked systems. Relevant future challenges were addressed from the overall analysis to accelerate development within the area of Automated Cyber Defence.

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Section: Resiliencementioning

confidence: 99%

“…8.1.9 Impact of Incorrect Action (G.6.1, G.1.3) [41]. The above issue also leads on a gap within the ACD literature for automated decision-making agents.…”

Section: Explainable Rl (A24)mentioning

confidence: 99%

Automated Cyber Defence: A Review

Vyas¹,

Hannay²,

Bolton³

et al. 2023

Preprint

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show abstract

“…This can not only invalidate testing and verification, but also make the system potentially vulnerable to a patient attacker that gradually tweaks normal behavior to avoid appearing anomalous. 28 Third, fixing ML vulnerabilities often creates other problems. 29 To address vulnerabilities in ML systems, developers have to retrain the system so that it is no longer susceptible to that deception.…”

Section: Whilementioning

confidence: 99%

“…Instead, they tend to make trade-offs, making the system perform better under one set of conditions but potentially worse in others. 35 This is a problem in adversarial contexts; an attacker can adapt its behavior to exploit the lingering weaknesses of the system. These persistent problems with safety and security raise the question of whether decision makers will trust applications of ML for decision advantage.…”

Section: Whilementioning

confidence: 99%

Reducing the Risks of Artificial Intelligence for Military Decision Advantage

Hoffman¹,

Kim²

2023

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Militaries seek to harness artificial intelligence for decision advantage. Yet AI systems introduce a new source of uncertainty in the likelihood of technical failures. Such failures could interact with strategic and human factors in ways that lead to miscalculation and escalation in a crisis or conflict. Harnessing AI effectively requires managing these risk trade-offs by reducing the likelihood, and containing the consequences of, AI failures.

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“…Primarily for security tasks, such as NIDSs, robustness is the main concern for trustworthy real-world ML applications [6]. The considerable demand for robustness partially constrains the real-world implementation of ML-based NIDSs [7]. On one hand, research on the reliability and trustworthiness of ML-based NIDSs is still in the early stage [8,9].…”

Section: Introductionmentioning

confidence: 99%

On the Robustness of ML-Based Network Intrusion Detection Systems: An Adversarial and Distribution Shift Perspective

Wang,

Yang,

Gunasinghe

et al. 2023

Computers

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Utilizing machine learning (ML)-based approaches for network intrusion detection systems (NIDSs) raises valid concerns due to the inherent susceptibility of current ML models to various threats. Of particular concern are two significant threats associated with ML: adversarial attacks and distribution shifts. Although there has been a growing emphasis on researching the robustness of ML, current studies primarily concentrate on addressing specific challenges individually. These studies tend to target a particular aspect of robustness and propose innovative techniques to enhance that specific aspect. However, as a capability to respond to unexpected situations, the robustness of ML should be comprehensively built and maintained in every stage. In this paper, we aim to link the varying efforts throughout the whole ML workflow to guide the design of ML-based NIDSs with systematic robustness. Toward this goal, we conduct a methodical evaluation of the progress made thus far in enhancing the robustness of the targeted NIDS application task. Specifically, we delve into the robustness aspects of ML-based NIDSs against adversarial attacks and distribution shift scenarios. For each perspective, we organize the literature in robustness-related challenges and technical solutions based on the ML workflow. For instance, we introduce some advanced potential solutions that can improve robustness, such as data augmentation, contrastive learning, and robustness certification. According to our survey, we identify and discuss the ML robustness research gaps and future direction in the field of NIDS. Finally, we highlight that building and patching robustness throughout the life cycle of an ML-based NIDS is critical.

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"Making AI Work for Cyber Defense: The Accuracy-Robustness Tradeoff "

Cited by 4 publications

References 9 publications

Automated Cyber Defence: A Review

Automated Cyber Defence: A Review

Reducing the Risks of Artificial Intelligence for Military Decision Advantage

On the Robustness of ML-Based Network Intrusion Detection Systems: An Adversarial and Distribution Shift Perspective

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