• Jul 6 2019 - IPFS Camp 2019 (June 27-30)

    I was extremely humbled to attend the first ever Interplanetary File System (IPFS) camp last week courtesy of Monax. It comprised approximately three days of tutorials, lectures and problem solving in the idyllic Campus La Mola, a short distance away from Barcelona, Spain. There were roughly 150 attendees from around the globe who came together to discuss ideas for the distributed web, focusing on content addressable methods of data dissemination and persistence but expanding on higher level technologies which build upon these ideas.

  • Apr 27 2019 - Let's Go Kubernetes

    Welcome to the first post from what will hopefully become a series on my adventures with Go! I’m really lucky to be able to experiment with some super awesome technologies which I will endeavour to write about more, so if you find this post helpful please let me know on twitter! If you’re new to Go, follow the getting started docs. You’ll also need to configure access to a Kubernetes cluster, or install Minikube - a single local node.

  • Feb 4 2019 - Evolving Infrastructure

    We’ve undergone a lot of infrastructure changes recently at work. We actually submitted the very first DLT framework into Helm’s stable charts over a year ago. This allows anyone with a Kubernetes cluster to deploy a custom blockchain courtesy of Burrow (our contribution to the Hyperledger Greenhouse). We’re a great believer in cloud first and open source technologies so not only is Kubernetes a great fit for what we do, but Helm extraordinarily simplifies the whole deployment process through Go templating.

  • Oct 8 2018 - Generating & Modelling Cryptography

    CryptoKnight is a framework I recently released which follows the methodology described in my publication for synthesizing a scalable dataset of cryptographic primitives to feed a unique Convolutional Neural Network. In effect, this allows us to generate and model a substantial amount of data to quickly identify cryptographic algorithms (such as AES, RSA, RC4, or MD5) in reference binary executables. By safely learning this statistical representation, malware analysts can efficiently compare it against crypto-ransomware samples in a controlled environment.

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