PromptCloud is a Data as a Service company that helps businesses harness the power of data. We are a small bunch of people working towards shaping the imminent data-driven future by solving some of its fundamental and toughest challenges. PromptCloud is a Data as a Service company that helps businesses harness the power of data. We are a small bunch of people working towards shaping the imminent data-driven future by solving some of its fundamental and toughest challenges. The PromptCloud experience is about striving to become the best version of ourselves holistically, an experience that lasts a lifetime.

Abstract--Network Intrusion Detection Systems (NIDS) are tools or software that are widely used to maintain the computer networks and information systems keeping them secure and preventing malicious traffics from penetrating into them, as they flag when somebody is trying to break into the system. Best effort has been set up on these systems, and the results achieved so far are quite satisfying, however, new types of attacks stand out as the technology of attacks keep evolving, one of these attacks are the attacks based on Generative Adversarial Networks (GAN) that can evade machine learning IDS leaving them vulnerable. Attacks synthesized using real DDos attacks generated using GANs on the IDS. The objective is to discover how will these systems react towards synthesized attacks. Unsupervised Machine Learning, IDS systems can predict the attacks that aren't labeled but that techniques are prone to 1-I False positives [3], this gives the attackers the chance to Cyber Attacks are increasingly sophisticated, hackers keep mislead models into their desired misclassification by using adapting their strategies to exploit every possible vulnerability adversarial examples.

Logz.io is releasing its AI-powered Exceptions, a revamped version of our Application Insights, fully embedded in your Kibana Discover experience, to boost your troubleshooting experience and help you find bugs in the log haystack. How many of your production issues stem from bugs in code you deployed? The introduction of agile software methodology and its release early, release often mentality has exacerbated the problem, with more frequent code releases, in earlier stages. How do you hunt down these bugs in production? How do you ensure that your deployed code hasn't caused any issues?

Machine learning pipelines have evolved tremendously in the past several years. With a wide variety of tools and frameworks out there to simplify building, training, and deployment, the turnaround time on machine learning model development has improved drastically. However, even with all these simplifications, there is still a steep learning curve associated with a lot of these tools. In order to use machine learning in the Elastic Stack, all you really need is for your data to be stored in Elasticsearch. Once there, extracting valuable insights from your data is as simple as clicking a few buttons in Kibana.

In this article, we will go thru the installation steps for Elasticsearch and Kibana on Windows OS. However, steps will be similar to Linux, macOS, and other systems as well. In the end, we will quickly run basic commands through Kibana to make sure that Elasticsearch is working successfully. Note: When you are setting up Kibana for the first time you may run into the error "". If you encounter this error, open another command prompt and run this command: curl -X DELETE http://localhost:9200/.kibana* Now that both Elasticsearch and Kibana are up and running.

This course is for people who want to learn how to do things, not just to fill their heads with important concepts, paradigms, and heaps of information they kind of know but have no idea how to use. Apache Hive is an easy SQL based tool that allows to process large amounts of data on Hadoop fast. Hive gained popularity immediately after Hadoop MapReduce became widely used as it allows to work with data by means of SQL queries. It is used by many organisations to process their data. This course shows a number of interesting Hive queries and explains what Hive UDFs are.

Using deep learning to harness and explore large datasets has become increasingly important for businesses in every industry. There are many companies and services trying to make this a tenable problem, and yet, more people are still required to munge together home-grown solutions to meet their specific needs. Fortunately, there are many tools and resources in the market today that make building cognitive apps more doable. Here are nine interesting tools and resources I've seen and/or worked with recently to build cognitive apps: 1. Deeplearning.net: Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence.