Articles

How to Create a Threat Model for Cloud Infrastructure Security

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The practice of creating a threat model can help teams proactively understand and develop a strategy for managing the possible vulnerabilities their organization faces, instead of waiting until after an incident occurs. OWASP defines threat modeling as “a procedure for optimizing security by identifying objectives and vulnerabilities, and then defining countermeasures to prevent, or mitigate the effects of, threats to the system.”

SecOps teams can benefit from creating a threat model for cloud infrastructure and defining an approach to operationalizing, hardening, and automating security throughout the software development lifecycle. While it’s best to build security into the design of your systems at the outset, remember the motto:

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ASP.NET Core: Read the GPS Coordinates of a Photo

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During one of my ASP.NET Core classes I made a demo about how to read GPS coordinates from photo and display location on a map. I took my favorite photo of a beer kiosk in Krakow and displayed the location of this kiosk on a map. This blog post describes my experiment on getting GPS coordinates from EXIF data in an ASP.NET Core application.

Wikipedia defines EXIF (Exchangeable image file format) as a standard that specifies the formats for images, sound, and ancillary tags used by digital cameras (including smartphones), scanners, and other systems handling image and sound files recorded by digital cameras. Most of modern devices used to take photos save meta information in EXIF format to photos. It’s great but be aware – not all people may use this information in good purposes.

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The Many Features of Apache MXNet GluonCV

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In preparation for my talk at the Philadelphia Open Source Conference, Apache Deep Learning 201, I wanted to have some good images for running various Apache MXNet Deep Learning Algorithms for Computer Vision. 

Using Apache open source tools – Apache NiFi 1.8 and Apache MXNet 1.3 with GluonCV I can easily ingest live traffic camera images and run Object Detection, Semantic Segmentation and Instance Segmentation.


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Spring Boot Annotations

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In my previous article, we discussed Spring core annotations with examples. In this quick article, we’ll explore the annotations from the org.springframework.boot.autoconfigure and org.springframework.boot.autoconfigure.condition packages.

As we know, Spring Boot is a brand new framework from the team at Pivotal, designed to simplify the bootstrapping and development of a new Spring application. The framework takes an opinionated approach to configuration, freeing developers from the need to define a boilerplate configuration. It provides defaults for code and annotation configuration to quick start new Spring projects within no time.

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Micronaut With Kotlin and Java With Groovy Tests

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Recently, I came across Micronaut, a super light framework that fills the need for something in between Spring Boot and Vert.x.

I have been playing with it and wanted to see if I could get a simple project with Micronaut, a Kotlin/Java intermix for the (main) source code, and Groovy/Spock tests. Turns out it’s not that difficult. I just had to fiddle with it a little bit to get it right. So to save you time, here it is:

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How to Test SOAP Services With JMeter

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SOAP is a messaging protocol specification for exchanging structured information in the implementation of web services. Its purpose is to induce extensibility, neutrality, and independence. It uses XML Information Set for its message format and relies on application layer protocols.

SOAP is used in a variety of messaging systems. It is delivered via a variety of transport protocols and the initial focus of SOAP is remote procedure calls transported via HTTP.

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Working With Irregular Time Series

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One of the benefits of InfluxDB is the ability to store raw events, which might come in at varying intervals, as an irregular time series. However, irregular time series present some unique challenges, and in some cases, common operations on the data simply will not work. Fortunately, InfluxDB allows you to convert an irregular time series to a regular one on the fly by calculating an aggregate of individual values for arbitrary windows of time. This gives you the best of both worlds when capturing events from your systems and working with that data.

We can take a look at a few actual data points in order to get a better understanding of what considerations need to be made when working with irregular time series. For the sake of example, we’ll use five data points, and give them values of 10, 20, 30, 40, and 50.

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One Billion Tables in MySQL 8.0 With ZFS

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The Short Version

I created > one billion InnoDB tables in MySQL 8.0 (tables, not rows) just for fun. Here is the proof:

$ mysql -A
Welcome to the MySQL monitor. Commands end with ; or g.
Your MySQL connection id is 1425329
Server version: 8.0.12 MySQL Community Server - GPL
Copyright (c) 2000, 2018, Oracle and/or its affiliates. All rights reserved.
Oracle is a registered trademark of Oracle Corporation and/or its
affiliates. Other names may be trademarks of their respective
owners.
Type 'help;' or 'h' for help. Type 'c' to clear the current input statement.
mysql> select count(*) from information_schema.tables;
+------------+
| count(*) |
+------------+
| 1011570298 |
+------------+
1 row in set (6 hours 57 min 6.31 sec)

Yes, it took 6 hours and 57 minutes to count them all!

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GOTO Elimination Algorithm

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GOTO Elimination Algorithm (Unstructured to Structured)

Problem Description

Devise an algorithm to eliminate any GOTO statements from a program, in order to get a structured program, which is logically and functionally equivalent.

For example:


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Make Your Home Smart and Secure

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“Smart but insecure” sounds like you’re talking about a high achiever who needs therapy.

Which you could be. But in the online world, it applies to semi-animate objects — the hundreds of millions of devices in American homes that are, at one level, smart.

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