Detecting Physical Adversarial Patch Attacks with Object Detectors

Jutras, Melanie; Liang, Ethan; Leary, Sara; Ward, Chris; Manville, Keith

doi:10.1109/aipr57179.2022.10092200

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…In the fight against cyber threats, its capacity to automatically analyse big datasets, identify trends, and spot anomalies in real-time is a vital asset. Deep learning neural networks, reinforcement learning, and natural language processing are just a few examples of the machine learning techniques that enable security systems to adapt and change along with the threat landscape [3]. These algorithms can offer early warning of potential weaknesses and oncoming attacks by continuously learning from prior data and spotting tiny variations from usual behaviour.…”

Section: Figure 1: Proposed Model For Safeguarding Critical Infrastru...mentioning

confidence: 99%

Safeguarding Critical Infrastructures: Machine Learning in Cybersecurity

Kalnawat,

Dhabliya,

Vydehi

et al. 2024

E3S Web Conf.

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It has become essential to protect vital infrastructures from cyber threats in an age where technology permeates every aspect of our lives. This article examines how machine learning and cybersecurity interact, providing a thorough overview of how this dynamic synergy might strengthen the defence of critical systems and services. The hazards to public safety and national security from cyberattacks on vital infrastructures including electricity grids, transportation networks, and healthcare systems are significant. Traditional security methods have failed to keep up with the increasingly sophisticated cyber threats. Machine learning offers a game-changing answer because of its ability to analyse big datasets and spot anomalies in real time. The goal of this study is to strengthen the defences of key infrastructures by applying machine learning algorithms, such as CNN, LSTM, and deep reinforcement learning for anomaly algorithm. These algorithms can anticipate weaknesses and reduce possible breaches by using historical data and continuously adapting to new threats. The research also looks at issues with data privacy, algorithm transparency, and adversarial threats that arise when applying machine learning to cybersecurity. For machine learning technologies to be deployed successfully, these obstacles must be removed. Protecting vital infrastructures is essential as we approach a day where connectivity is pervasive. This study provides a road map for utilising machine learning to safeguard the foundation of our contemporary society and make sure that our vital infrastructures are robust in the face of changing cyberthreats. The secret to a safer and more secure future is the marriage of cutting-edge technology with cybersecurity knowledge.

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Section: Figure 1: Proposed Model For Safeguarding Critical Infrastru...mentioning

confidence: 99%

Safeguarding Critical Infrastructures: Machine Learning in Cybersecurity

Kalnawat,

Dhabliya,

Vydehi

et al. 2024

E3S Web Conf.

View full text Add to dashboard Cite

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The AI security pyramid of pain

Ward,

Harguess,

Tao

et al. 2024

Assurance and Security for AI-enabled Systems

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With resource constraints being a reality for many organizations, effective security for Artificial Intelligence (AI) systems has become a critical focus. To address this challenge, we introduce the AI Security Pyramid of Pain, a framework that adapts the cybersecurity Pyramid of Pain to categorize and prioritize AI-specific threats. This framework provides a structured approach to understanding and addressing various levels of AI threats.Starting at the base, the pyramid emphasizes Data Integrity, which is essential for the accuracy and reliability of datasets and AI models, including their weights and parameters. Ensuring data integrity is crucial, as it underpins the effectiveness of all AI-driven decisions and operations. The next level, AI System Performance, focuses on MLOps-driven metrics such as model drift, accuracy, and false positive rates. These metrics are crucial for detecting potential security breaches, allowing for early intervention and maintenance of AI system integrity. Advancing further, the pyramid addresses the threat posed by Adversarial Tools, identifying and neutralizing tools used by adversaries to target AI systems. This layer is key to staying ahead of evolving attack methodologies. At the Adversarial Input layer, the framework addresses the detection and mitigation of inputs designed to deceive or exploit AI models. This includes techniques like adversarial patterns and prompt injection attacks, which are increasingly used in sophisticated attacks on AI systems. Data Provenance is the next critical layer, ensuring the authenticity and lineage of data and models. This layer is pivotal in preventing the use of compromised or biased data in AI systems. At the apex is the tactics, techniques, and procedures (TTPs) layer, dealing with the most complex and challenging aspects of AI security. This involves a deep understanding and strategic approach to counter advanced AI-targeted attacks, requiring comprehensive knowledge and planning.This paper provides an overview of each layer, discussing their significance and interplay within AI system security. The AI Security Pyramid of Pain offers a strategic approach for organizations to navigate and mitigate the multifaceted threats in AI security effectively.

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Detecting Physical Adversarial Patch Attacks with Object Detectors

Cited by 2 publications

References 15 publications

Safeguarding Critical Infrastructures: Machine Learning in Cybersecurity

Safeguarding Critical Infrastructures: Machine Learning in Cybersecurity

The AI security pyramid of pain

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