Don’t get me wrong; Bitcoin and crypto currencies are a big deal, at least technology-wise. Bitcoin and blockchains taught us a lot on what can be done with security protocols, and at a lower level, it even taught us that computation inefficiency is not always a bad word, but something that can yield benefits, if that inefficiency is properly orchestrated and exploited. It was also the most prevalent demonstration of scarcity being artificially created by technology alone. As I wrote before, blockchains will probably have some novel use-cases one day, and Bitcoin, aside of being a mechanism for transferring money, also provides a target of speculation, which in itself can be (and is) monetized.
What I truly do not understand are the advocates who see Bitcoin wallets as the near-future replacement for bank accounts, and Bitcoin replacing banks (and other financial institutions) in the near future. I understand the motivation, as those are dreams easy to fall for, but for crypto-currency wallets to replace financial institutions much more is needed, and for the sake of this discussion I will not even delve into the many technical difficulties.
Continue reading "Your Bitcoin wallet will never be your bank account"
I recently came across this study titled “Unknown Threats are The Achilles Heel of Email Security”. It concludes that traditional e-mail scanning tools, that also utilize machine-learning to cope with emerging threats, are still not reacting fast enough to new threats. This is probably true, but I think this conclusion should be considered even more widely, beyond e-mail.
Threats are dynamic. Threat actors are creative and well-motivated enough to make threat mitigation an endlessly moving target. So aren’t we fortunate to have this new term, “machine learning”, recently join our tech jargon? Just like many other buzzwords, the term is newer than what it denotes, but nonetheless, a machine that learns the job autonomously seems to be precisely what we need for mitigating ever-changing threats.
All in all, machine-learning is good for security, but yet in some cases it is a less significant addition to our defense arsenal. Why? – Because while you learn, you often don’t do the job well enough; and a machine is no different. Eventually, the merits of learning-while-doing are to be determined by the price of the resulting temporary imperfectness.
Continue reading "An obvious limitation of machine-learning for security"