Can anyone advice me how i can generate IDX3-ubtye file(s) from a Network traffic data ?
I Intend to run a certain experiment (https://github.com/echowei/DeepTraffic/tree/master/1.malware_traffic_classification)
but keep getting a lot of errors so i thought to explore a different techniques for
converting Traffic data into an IDX3-ubyte which should then be used as input to the classifier.
My goal future goal is to repeat /implement this experiment by using CIC-IDS 2017 raw dataset which is listed as "Friday-WorkingHours.pcap 2019-09-10 17:04 8.2G"
at http://205.174.165.80/CICDataset/CIC-IDS-2017/Dataset/PCAPs/
Any assistance will be highly appreciated !!!
Related
I am building a package that uses the Google Analytics API for Python.
But, in severous cases when I have multiple dimensions the extraction by day is sampled.
I know that if I use sampling_level = LARGE will use a sample more accurate.
But, somebody knows if has a way to reduce a request that you can extract one day without sampling?
Grateful
setting sampling to LARGE is the only method we have to decide the amount of sampling but as you already know this doesn't prevent it.
The only way to reduce the chances of sampling is to request less data. A reduced number of dimensions and metrics as well as a shorter date range are the best ways to ensure that you dont get sampled data
This is probably not the answer you want to hear but, one way of getting unsampled data from Google analytics is to use unsampled reports. However this requires that you sign up for Google Marketing Platform. With these you can create an unsampled report request using the API or the UI.
There is also a way to export the data to Big Query. But you lose the analysis that Google provides and will have to do that yourself. This too requires that you sign up for Google Marketing Platform.
there are several tactics of building unsampled reports, most popular is splitting your report into shorter time ranges up to hours. Mark Edmondson did a great work on anti-sampling in his R package so you might find it useful. You may start with this blog post https://code.markedmondson.me/anti-sampling-google-analytics-api/
So a bit of a general question. I work as a data analyst for a startup. My primary process involves taking existing customer data a client has and cleansing/normalizing it to fit into our platform once as part of our onboarding process. A member of our team exports their data from their system they are transitioning from or, if they kept track of it in house, we receive their Excel log they used to track it. It is always in a different format and requires extensive cleansing (avg 1 min/record). We take what is usually one large table (.xlxs format), and after cleansing, split it into four .csv files; which we load as four tables on our platform.
I feel I have optimized the process quite well in terms of the process steps and cleansing with excel functions (if, concat, text-to-columns, etc). I have beginner-intermediate skills in VBA and SQL and have just scratched the surface in R; what is frustrating is that I know there is the potential to automate this process but I just don't know where to start. If anyone has experience with something like this, code, a link to an article / another thread, or just some general direction would be much appreciated. Please ask for clarification where you feel it is needed. Thanks.
This will be really hard to do in Excel. If you have the time you can try out Optimus, a Data Cleansing library written in Python and Pyspark (you don't need to know spark). Here is the webpage https://hioptimus.com.
You can create Data Pipelines with it, and I recommend that you do that, try to generalize your processes, and asking the client for more a structure way of passing the data.
The good thing is that you don't need Big Data for running Optimus, bit if you have it some day, the same code will work.
Check out the documentation for more:
http://optimus-ironmussa.readthedocs.io/en/latest/
Let me know if you have doubts!
I am using spatstat package in R to read my road network shapefile which also has some additional attributes.
When i am reading my shapefiles and converting them to as.psp(before I make them an object of linnet), I am getting n columns of data frame discarded. I do not understand why? The columns being discarded are my covariates for a linear network, so I am not able to bring them into my analysis.
Could someone give me an idea why this happens and how to correct it?
Why it happens:
I would guess that we (spatstat authors) need to spend a bit of time discussing with the maptools guys how to handle the additional info in the SpatialLinesDataFrame object, and we haven't done that yet.
How to correct it:
You have to write some code on your own at the moment. You can extract the data from SpatialLinesDataFrame object by accessing the #data slot. Please provide specific data and how you need to use the additional data (what format do you need it in) if you need more help. You can find a few helpful commands here: https://cran.r-project.org/web/packages/spatstat/vignettes/shapefiles.pdf
I'm eager to try out some more with Microsoft Azure Machine Learning and would like to find a data set to make a use case concerning predictive manufacturing. Microsoft already offers a data set (semi conductor) for a use case like this, but I would like to try out some more. Does anybody of you know where I can find another data set similiar to the one provided by MS?
Basically I'm looking for a bunch of sensor data in a manufacturing process and a classification whether it came to a failure or not.
Your help would be greatly appreciated ;)
Thanks,
Clemens
This blog post contains interesting sources of data that you can use with Azure ML. From the post:
Data.gov – http://www.data.gov/
Kaggle - http://www.kaggle.com/
UCI Machine Learning Repository - http://archive.ics.uci.edu/ml/
Specifically, you could check out the manufacturing data set on data.gov.
Don't know where to start on this one so hopefully you guys can clear up my question. I have project where email will be searched for specific words/patterns and stored in a structured manner. Something that is done with Trip it.
The article states that they developed a DataMapper
The DataMapper is responsible for taking inbound email messages
addressed to plans [at] tripit.com and transforming them from the
semi-structured format you see in your mail reader into a highly
structured XML document.
There is a comment that also states
If you're looking to build this yourself, reading a little bit about
Wrappers and Wrapper Induction might be helpful
I Googled and read about wrapper induction but it was just too broad of a definition and didn't help me understand how one would go about solving such problem.
Is there some open source project out there that does similar things?
There are a couple of different ways and things you can do to accomplish this.
The first part, which involves getting access to the email content I'll not answer here. Basically, I'll assume that you have access to the text of emails, and if you don't there are some libraries that allow you to connect java to an email box like camel (http://camel.apache.org/mail.html).
So now you've got the email so then what?
A handy thing that could help is that lingpipe (http://alias-i.com/lingpipe/) has an entity recognizer that you can populate with your own terms. Specifically, look at some of their extraction tutorials and their dictionary extractor (http://alias-i.com/lingpipe/demos/tutorial/ne/read-me.html) So inside of the lingpipe dictionary extractor (http://alias-i.com/lingpipe/docs/api/com/aliasi/dict/ExactDictionaryChunker.html) you'd simply import the terms you're interested in and use that to associate labels with an email.
You might also find the following question helpful: Dictionary-Based Named Entity Recognition with zero edit distance: LingPipe, Lucene or what?
Really a very broad question, but I can try to give you some general ideas, which might be enough to get started. Basically, it sounds like you're talking about an elaborate parsing problem - scanning through the text and looking to apply meaning to specific chunks. Depending on what exactly you're looking for, you might get some good mileage out of a few regular expressions to start - things like phone numbers, email addresses, and dates have fairly standard structures that should be matchable. Other data points might benefit from some indicator words - the phrase "departing from" might indicate that what follows is an address. The natural language processing community also has a large tool set available for text processing - check out things like parts of speech taggers and semantic analyzers if they're appropriate to what you're trying to do.
Armed with those techniques, you can follow a basic iterative development process: For each data point in your expected output structure, define some simple rules for how to capture it. Then, run the application over a batch of test data and see which samples didn't capture that datum. Look at the samples and revise your rules to catch those samples. Repeat until the extractor reaches an acceptable level of accuracy.
Depending on the specifics of your problem, there may be machine learning techniques that can automate much of that process for you.