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A Mechanism to Combat Phishing

Hacker News

A Mechanism to Combat Phishing

This is a diagram of a proposed mechanism to combat phishing and other computer attacks: https://m.imgur.com/a/MLOVR1y The fundamental problem: computers require two-way communication to usefully interact with many networked resources such as the internet. This opens the door to the injection of malicious instructions. Once a device is compromised, it can be used as a jumping off point to attack other networked devices. Proposed solution: One-way data transfer has long been possible through devices such as data diodes. Embedding a one-way device between 2 CPUs prevents a compromised CPU from being used against the other "secured" CPU. If a switching mechanism for peripheral devices such as mice, keyboards, and monitors was connected to a multi-CPU device with such a one-way data connection, the average user could simply alternate between a "secured" CPU connected to secure resources (e.g. internal business databases) and the "insecure" CPU to access public resources such as the internet. If their device was compromised by phishing or some other attack, it would be isolated and unable to be used as a jumping off point to attack deeper into the network. Long-term vision: eventually this design could be applied more broadly, for example to mobile devices. A secured network consisting of a small number of trusted entities (e.g. banks, government websites) could be accessed via the secure side, and general internet browsing could be done on the insecure side. Tradeoffs: this would cause a performance hit to all devices implementing this design, as you would require 2 CPUs to achieve the same effect as 1 in a traditional design. Ideally one CPU could be optimized for performance and the other for security, but this is a detail and design decision outside the scope of this proposal. Thank you for your time and I appreciate any feedback.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, computer, open · Missing: mac, agents, macos
59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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