Cybersecurity in the Age of Artificial Intelligence, AI for Cybersecurity, and Cybersecurity for AI

Permanent URI for this collectionhttps://hdl.handle.net/10125/107408

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    Suggesting Alternatives for Potentially Insecure Artificial Intelligence Repositories: An Unsupervised Graph Embedding Approach
    (2024-01-03) Lazarine, Ben; Samtani, Sagar; Zhu, Hongyi; Venkataraman, Ramesh
    Emerging Artificial Intelligence (AI) applications are bringing with them both the potential for significant societal benefit and harm. Additionally, vulnerabilities within AI source code can make them susceptible to attacks ranging from stealing private data to stealing trained model parameters. Recently, with the adoption of open-source software (OSS) practices, the AI development community has introduced the potential to worsen the number of vulnerabilities present in emerging AI applications, building new applications on top of previous applications, naturally inheriting any vulnerabilities. With the AI OSS community growing rapidly to a scale that requires automated means of analysis for vulnerability management, we compare three categories of unsupervised graph embedding methods capable of generating repository embeddings that can be used to rank existing applications based on their functional similarity for AI developers. The resulting embeddings can be used to suggest alternatives to AI developers for potentially insecure AI repositories.
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    Detecting Spoofing and GPS Jamming in UAVs: Multiclass Approach to Attack Diagnosis
    (2024-01-03) Ferrão, Isadora; Da Silva, Leandro; Bonilla, Daniel; Dezan, Catherine; Espes, David; Castelo Branco, Kalinka
    As Unmanned Aerial Vehicles (UAVs) become increasingly popular and affordable, it is essential to ensure their safe operation, especially around critical devices such as the aircraft's GPS. GPS plays an indispensable role in aviation systems. This study presents an efficient multiclass detection method to identify GPS attacks on UAVs, focusing on differentiating between spoofing and jamming attacks. The proposed approach outperforms existing methods. The results obtained in this study contribute to increasing the security of UAVs and provide valuable information for developing robust detection systems to combat evolving threats in the UAV domain.
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