WARNING: This tutorial is for educational purposes only, and by NO MEANS should you actually be malicious when (or after) making a rootkit. I thought I’d share how to do this for any security minded people who would like to learn more on how to prevent or look for rootkits. This will be done in C on Linux, probably using libraries and functions you’ve never seen. It is also advisable to do this in a VM to get the hang of compiling and loading modules. Messing with the kernel can cause things to go crazy, if not break- you have been warned.
Basics of Making a Rootkit: From syscall to hook!
I have been learning about machine learning and deep learning (ML/DL) for the last year. I think ML/DL is here to stay. I don’t think this is a fad or bubble! Here is why:
- ML/DL has results. It is hard to argue against success.
- ML/DL has been on the rise organically since 1985 (with the backpropagation algorithms) and went through another phase of acceleration after 2005 (with the wide availability of big data and distributed data processing platforms). The rise of ML/DL is following a rising curve pattern, not the pattern for a hyped ephemeral bubble. Since it grew gradually over many years, I betting it will be around for at least the same amount of time.
- ML/DL has been co-developed with applications. It has developed very much on the practice side with trial and error, and its theory is still lagging a bit behind and is unable explain many things. According to Nassim Taleb’s heuristics ML/DL is antifragile.
- ML/DL has the market behind it. Big money provides big incentive and has been attracting a lot of smart people. This many smart people cannot be wrong.
Last year, we did a recap with what we thought were the best Python libraries of 2015, which was widely shared within the Python community (see post in r/Python). A year has gone by, and again it is time to give due credit for the awesome work that has been done by the open source community this year.
Again, we try to avoid most established choices such as Django, Flask, etc. that are kind of standard nowadays. Also, some of these libraries date prior to 2016, but either they had an explosion in popularity this year or we think they are great enough to deserve the spot. Here we go!
Uber posted an article detailing how they built their “highest query per second service using Go”. The article is fairly short and is required reading to understand the motivation for this post. I have been doing some geospatial work in Golang lately and I was hoping that Uber would present some insightful approaches to working with geo data in Go. What I found fell short of my expectations to say the least…
Artificial intelligence (AI) got a lot of press in 2016, not least because of the victory of Google’s AI program over Lee Sedol, the world’s best Go player. That triumph of machine over human elicited numerous responses, some enthusiastic and some anxious, all sharing the assumption that the goal of artificial intelligence is to achieve “human-level intelligence” or, as some predict, “superintelligence.”
“I don’t care so much whether what we are building is real intelligence,” says Peter Norvig, Director of Research at Google. “We know how to build real intelligence—my wife and I did it twice, although she did a lot more of the work. We don’t need to duplicate humans. That’s why I focus on having tools to help us rather than duplicate what we already know how to do. We want humans and machines to partner and do something that they cannot do on their own.”
A company funded by Amazon.com’s CEO Jeff Bezos, Unity Biotechnology, is working to bring this technology to human beings.
crypto/tls was slow and
net/http young, the general wisdom was to always put Go servers behind a reverse proxy like NGINX. That’s not necessary anymore!
At Cloudflare we recently experimented with exposing pure Go services to the hostile wide area network. With the Go 1.8 release,
crypto/tls proved to be stable, performant and flexible.
However, the defaults are tuned for local services. In this articles we’ll see how to tune and harden a Go server for Internet exposure.
In short, skip lists are a linked-list-like structure which allows for fast search. It consists of a base list holding the elements, together with a tower of lists maintaining a linked hierarchy of subsequences, each skipping over fewer elements.
Skip list is a wonderful data structure, one of my personal favorites, but a trend in the past ten years has made them more and more uncommon as a single-threaded in-memory structure.
My take is that this is because of how hard they are to get right. The simplicity can easily fool you into being too relaxed with respect to performance, and while they are simple, it is important to pay attention to the details.
In the past five years, people have become increasingly sceptical of skip lists’ performance, due to their poor cache behavior when compared to e.g. B-trees, but fear not, a good implementation of skip lists can easily outperform B-trees while being implementable in only a couple of hundred lines.
How? We will walk through a variety of techniques that can be used to achieve this speed-up.
These are my thoughts on how a bad and a good implementation of skip list looks like.
How Google used artificial intelligence to transform GoogleTranslate, one of its more popular services — and howmachine learning is poised to reinvent computing itself.
LISP. It conjures up visions of a bygone age of computers the size of refrigerators, ALL CAPS CODE, and parentheses. Oh! so many parentheses! So why is Object-Oriented Programming’s creator so enamored with the idea of Lisp? And what can he mean by a programming language being an idea anyway? Should I blame my Computer Science education for not teaching it to me?