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Recently I was asked to add additional connection protocols as a means to submit samples to be analyzed by our automated forensic analysis platform. We had our UI, and REST API and multiple REST clients (C#, Java and Python). These are all standard but all require integration or manual intervention. Protocols like FTP, SFTP, SCP, SMB and many others are used to transfer files for everyday use. They have commercially available clients and many operating systems already have built-in support as well. My challenge was providing a smart, powerful and flexible integration.
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I am working on a project where we are utilizing BPMN for authoring and controlling the processing of analysis. Any single analysis task may yield several descendants like how a .ZIP file has many child files. Additionally, many analyzers also yielded additional analysis for both the inputted artifact as well as additional artifacts. Currently we we treating each and every child artifact, as well as child workflows, as completely separate entities.
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I want to begin by saying that I am not an authority on cyber security but am trying to compile a guide of best practices to secure your digital life.
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Recently I had to look into processing user inputted expressions which would enable a user to insert control flow logic into a workflow depending on whatever criteria they wish. We are already using JBoss’s jBPMN which is an implementation of BPMN, a standard workflow schema. It is a stable, trusted and widely used tool in the enterprise community.
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I have been evaluating various databases recently for a project at work. Without going into the details about the product all you need to know is that there are several types of data and performance is a high priority. We have a very large amount of data and versatile data types. We had been using PostgreSQL for holding relational data, Riak for holding most of everything else, and ElasticSearch for handling full text search. I want to focus on multi-model databases but I cannot overemphasize that there is no such thing as a one-size fits all database. A database is built for specific uses and specific data types, and data sizes. You may need multiple storage engines (the word I am going to use instead of database) to handle your data requirements. The problems that you run into is as the number of storage engines increase your system becomes more complex. You may have difficulty with data that is stored in a single storage engine but require some sort of decimation from another storage engine. This will result in performance issues and general complications. With all of this being said there may be times that a single storage engine solution may be viable and appropriate.
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This is my attempt to extract his slides from his presentation and annotate them slightly. I write my interpretations under the headings. Watch the full talk here.