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Yuaho Zheng, Director of Engineering at DataVisor, talks about building a fraud detection platform using AI and Big Data at the 2019 AWS Santa Clara Summit.

Adversarial artificial intelligence and machine learning is a growing threat in cybersecurity and data science. Algorithms are vulnerable to new types of stealthy and effective cyberattacks. Threat actors can and have altered the machine learning and artificial intelligence models without ever gaining access to their systems. This is called data poisoning or model poisoning. Find out more.

Chapters:
00:00 The implications
00:56 Data Poisoning Explained
02:20 The Poisoning Threshold
04:21 Attack Objectives
05:07 Backdoor Attacks
07:27 Attack Vectors
10:38 Vulnerability
12:46 Attack Strategies
14:34 Previous Successful Attacks

Sources Cited:
#1. https://arxiv.org/pdf/1804.00792.pdf
#2. https://github.com/aks2203/poisoning-benchmark#readme
https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.695.6555 &rep=rep1 &type=pdf
https://www.energy.gov/sites/default/files/2022-02/Cybersecurity%20Supply%20Chain%20Report%20-%20Final.pdf
https://arxiv.org/pdf/2009.07008.pdf
https://www.usenix.org/system/files/sec21fall-severi.pdf
https://arxiv.org/pdf/1811.00121.pdf
https://arxiv.org/pdf/1811.09982.pdf
https://arxiv.org/pdf/2006.12557.pdf
https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.79.2324 &rep=rep1 &type=pdf
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8685687
https://www.forbes.com/sites/naveenjoshi/2022/03/17/countering-the-underrated-threat-of-data-poisoning-facing-your-organization/?sh=17331d73b5d8
https://blogs.microsoft.com/blog/2016/03/25/learning-tays-introduction/
https://atlas.mitre.org/studies/AML.CS0009/
https://arxiv.org/pdf/2007.08432.pdf
https://manuscriptlink-society-file.s3-ap-northeast-1.amazonaws.com/kism/conference/sma2020/presentation/SMA-2020_paper_52.pdf
https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.79.2324 &rep=rep1 &type=pdf
https://arxiv.org/pdf/1910.00033.pdf
https://arxiv.org/pdf/2012.03765.pdf -knearest
https://elie.net/blog/ai/attacks-against-machine-learning-an-overview/
https://people.eecs.berkeley.edu/~tygar/papers/SML2/Adversarial_AISEC.pdf
https://arxiv.org/pdf/1804.00792.pdf

Fraudsters change their attacks based on your business model and risk management techniques. Learn how machine learning and AI can help your defense be up-to-date.

Read our post to machine learning for fraud detection here:
https://seon.io/resources/fraud-detection-with-machine-learning/

More anti-fraud video resources on:

• Device fingerprinting: https://www.youtube.com/watch?v=HCeDfZ9tJWo
• Data enrichment: https://www.youtube.com/watch?v=s-oZeFJbdF8
• Fraud detection API: https://www.youtube.com/watch?v=lmgzt8GHgIE

Wanna see how SEON can help your business reduce fraud?

Try it for free here:
https://seon.io/try-for-free/

Book a demo here:
https://seon.io/get-a-demo/

Find SEON on:

• Facebook: https://www.facebook.com/SEON.FraudFighters
• LinkedIn: https://linkedin.com/company/seon-tech
• Twitter: https://twitter.com/seon_tech
• Github: https://github.com/seontechnologies
• TikTok: https://www.tiktok.com/@seon.fraudfighters

#fraud #fraudprevention #frauddetection #machinelearning #machinelearningfrauddetection #aimachinelearning

Fraud follows speed. Faster payments leave no time for manual review of transactions and no room for high false positives. Learn how machine learning based fraud detection can mitigate fraud in real-time with low false positives, ensure positive customer experiences, and transform fraud operations from loss-recovery to loss-prevention.