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The best way say goodbye: “take care of yourself, and if you can, someone else too”

If a tree doesn’t fall in the internet, and big brother wants you to think it has, does that mean that it fell?

If a tree falls in the internet, and big brother doesn’t want you to hear it, does it make a sound?

Public figures can’t use plausible deniability as a reason for not supporting something that would benefit the taxpayer & consumer.

“The Cloud” is the thing that evaporates your data & makes it rain dollars in the location that is most beneficial to your gods.

You are better off dating a divorce attorney than someone with family ties to government.

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@nixCraft shouldn’t systems be designed with enough forethought that they are able to incentivize against this?

hakologist boosted

Things we didn't do:
- Start the fire
- Shoot the deputy
- Steal the cookies from the cookie jar

Things we did:
- Tried to fight it
- Shot the sheriff
- Put the sham in the shama-lama-ding-dong

Things we will do:
- Survive
- Rock you
- Walk 500 miles
- Walk 500 more

Things we won't do:
- Get fooled again
- Back down
- That

Things we will never do:
- Give you up
- Let you down
- Run around
- Desert you

(Oops:
- I did it again)

@lucifargundam @peterdrake data logging is only as strong as your ability to organize it. Data can’t be owned - only known. If it’s not organized, it can become forgotten. ?

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Bessie Stringfield
'In 1930, at age of 19, Bessie Stringfield flipped a penny onto a map of the US then ventured out on her bike alone. She became the first Black woman to ride a motorcycle in every one of the connected 48 states — a solo cross-country ride she undertook eight times during her lifetime'

medium.com/femalepioneers/fema

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@lucifargundam I’ll correct myself before you or somebody else does, as there is “usually always” this other option:

@lucifargundam I’ll correct myself before you or somebody else does, as there is “usually always” this other option:

@lucifargundam In this sense, “unsupervised” doesn’t mean that it doesn’t get updated - it means that the model is looking for signals (malicious traffic) without being previously informed as to what those packets look like. If you had a list of malicious fingerprints, you’d run deep packet inspection. But without this, the only option that comes to mind is the “top down” approach (with aggressive logging) - fitting a model to the steady/normal state & using that to identify anomalies.

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@lucifargundam I think I just described what it’s like to run a snooping Tor exit node (not to mention an everyday VPN/ISP)…or perhaps a full blockchain node that caches/correlates queries from upstream lite nodes (not to mention an everyday Coinbase/centralized exchange)…

@lucifargundam I think I just described what it’s like to run a snooping Tor exit node (not to mention an everyday VPN/ISP)…or perhaps a full blockchain node that caches/correlates queries from upstream lite nodes (not to mention an everyday Coinbase/centralized exchange)…

hakologist boosted

@lucifargundam I use aggressive containerization and encrypt outbound traffic. But short of requiring manual airgapped decryption, I can’t guarantee that packets aren’t compromised at the destination. I also can’t promise that all inbound traffic has been encrypted from the source. But the bigger issue is the sheer volume of packets traversing my gateway, to which I have to rely on a single Pi2 running unsupervised ML in order to identify the malicious signals amongst all the noise.

@lucifargundam I use aggressive containerization and encrypt outbound traffic. But short of requiring manual airgapped decryption, I can’t guarantee that packets aren’t compromised at the destination. I also can’t promise that all inbound traffic has been encrypted from the source. But the bigger issue is the sheer volume of packets traversing my gateway, to which I have to rely on a single Pi2 running unsupervised ML in order to identify the malicious signals amongst all the noise.

@lucifargundam these worms use TCP or UDP? Can I block the port?

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