Chapter
Potential Risks of Backdoor Attacks in Machine Learning Systems.
12:46 - 21:07 (08:21)
The risks of backdoor attacks in machine learning systems, which involve feeding poisoned data points to create incorrect models, can cause major implications for security and efficiency. Such attacks may only be identifiable in specific situations or trigger inputs, leading to biased or incorrect predictions.
Clips
The use of NLP and chatbot techniques are making it easier to identify phishing attacks where attackers pose as relatives or remote correspondents of the victim to solicit money.
12:46 - 14:25 (01:38)
Summary
The use of NLP and chatbot techniques are making it easier to identify phishing attacks where attackers pose as relatives or remote correspondents of the victim to solicit money. These AI-powered chatbots can recognize suspicious circumstances and generate probing questions to verify the identity of the correspondent.
ChapterPotential Risks of Backdoor Attacks in Machine Learning Systems.
Episode#95 – Dawn Song: Adversarial Machine Learning and Computer Security
PodcastLex Fridman Podcast
A powerful chatbot can not only capture patterns associated with social engineering attacks but also engage in conversations with the attacker to learn more information.
14:25 - 16:48 (02:23)
Summary
A powerful chatbot can not only capture patterns associated with social engineering attacks but also engage in conversations with the attacker to learn more information. Such a chatbot serves as a representative in the security space, testing the claims made by an attacker and analyzing their semantics to learn more about them.
ChapterPotential Risks of Backdoor Attacks in Machine Learning Systems.
Episode#95 – Dawn Song: Adversarial Machine Learning and Computer Security
PodcastLex Fridman Podcast
Machine learning systems are susceptible to attacks at different stages, including at the training stage and during inference.
16:48 - 21:07 (04:18)
Summary
Machine learning systems are susceptible to attacks at different stages, including at the training stage and during inference. Attackers can manipulate inputs to result in malicious perturbations which can cause the system to give incorrect answers.