In the previous post, we talked about some of the attack vectors on the DNS. In this post, we're going to be talking about DNSSEC, which is an attempt to make the DNS more secure. A point to note, DNSSEC does not provide Confidentiality, but only Integrity. Integrity in this case is ensuring that the … Continue reading DNSSEC

DNS Attack Vectors

Before looking at DNS Attack Vectors, let's do a quick recap of what a DNS is, and what are it's functions. What is a DNS? DNS, or Domain Name System, is a server that provides Name to IP Address resolution. When people visit websites, it's much easier for them to remember words, such as Facebook … Continue reading DNS Attack Vectors

RNN and Vanishing/Exploding Gradients

In this post, we're going to be looking at: Recurrent Neural Networks (RNN)Weight updates in an RNNUnrolling an RNNVanishing/Exploding Gradient Problem Recurrent Neural Networks A Recurrent Neural Network (RNN) is a variant of neural networks, where in each neuron, the outputs cycle back to themselves, hence being recurrent. Each neuron's output cycle back to themselves, … Continue reading RNN and Vanishing/Exploding Gradients

K-Means Clustering

K-Means Clustering is an unsupervised learning algorithm. It works by grouping similar data points together to try to find underlying patterns. The number of groups are pre-defined by the user as K. How the Algorithm works Before the iterative update starts, a random selection of centroid locations are picked on the graph. These centroids act … Continue reading K-Means Clustering

Random Forests

A random forest is an ensemble approach of combining multiple decision trees. Ensembling and Decision Trees, we first need to explain what these two things are Decision Trees Decision Trees try to encode and separate the data into if-else rules. It breaks the data down into smaller and smaller subsets. Each node poses the question, … Continue reading Random Forests

Branches of Machine Learning

Just finished reading the book "The Master Algorithm", where the author tries to find the ultimate Machine Learning algorithm that can solve different varieties of problems (text, image, predictive, time series etc) In the book, he goes over the 5 main branches (or tribes) of Machine Learning. They are: The EvoluntionariesThe ConnectionistThe SymbolistThe BayesiansThe Analogizers … Continue reading Branches of Machine Learning