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🦑Pro hacking -Use the pandas library to filter and save csv files-

1) This operation does not seem to be difficult now, but in undercode I have been searching for relevant information for a long time.

2) Most of the gangsters are directly throwing me on the pandas official website, and then give an entry-level example.

https://pandas.pydata.org/docs/reference/index.html

3) First import the pandas library

import pandas as pd

4) Then use read_csv to open the specified csv file

df = pd.read_csv('./IP2LOCATION.csv',encoding= 'utf-8')

5)In this function, the path of the csv file needs to be written. If the csv file is saved to the python project folder, only the ./ file name is needed, and encoding = 'utf-8' is encoded using utf-8 , Sometimes need to change to gbk.

6) Although we are reading csv files, in fact, because we are using the pandas library, so we actually get a DataFrame data structure.

7) You can use print (type (df)) to check

print(type(df))

🦑DataFrame is a table-type data structure. Therefore, we can use it as a table. The DataFrame is displayed similar to a table, and also contains row labels and column labels.

We can add a column label using pandas.DataFrame.columns

In our example, the variable of DataFrame type is df, so the usage method is df.columns, and the column labels we add are a, b, c, d, e, f


> df.columns = ['a','b','c','d','e','f']

🦑Then, we want to extract those rows that are equal to a certain value in a certain column

You can think of the read content as a list, and then the elements of this list are each row in the table, and then each row is also a list, that is, a list in the list.

For example, I want to extract the row with the value of Andhra Pradesh in the fifth column of the table, and because we defined the column label of the fifth column as e

So the code is as example :

> data = df[df['e'] == 'Andhra Pradesh']

🦑Finally, we can use to_csv in pandas to save the filtered data to a new csv file.

data.to_csv('my_IP2LOCATION.csv')

Usage is table name.to_csv ('path to save place / table name.csv')

Finally, summarize our code

> import pandas as pd

df = pd.read_csv('./IP2LOCATION.csv',encoding= 'utf-8')
# print(type(df))
df.columns = ['a','b','c','d','e','f']
data = df[df['e'] == 'Andhra Pradesh']
data.to_csv('my_IP2LOCATION.csv')

🦑The content of IP2LOCATION.csv is as follows:

Then my_IP2LOCATION.csv after our screening

🦑Only 3461 rows

PS: You can use print (len (df.values)) to view the number of rows

🦑Pro hacking -Use the pandas library to filter and save csv files- final of guide>
> can't find any video for this modern tutorial :)

> you can simple create it as bash script and uploaded it to github

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🦑Google engineer: 70% of Chrome security vulnerabilities are memory security issues, and Rust has become an alternative language
recently from our tweets @ UndercodeNews :


1) Recently, a Google engineer analyzed 912 security bugs fixed in the Chrome stable branch since 2015. And found that among all the security vulnerabilities marked as "high" or "serious", about 70% are memory management and security issues.

2) Half of these are use-after-free vulnerabilities. This security issue is caused by the wrong management of memory pointers (addresses), opening the door for attackers to attack Chrome's internal components.

🦑 This data happens to be the same as Microsoft ’s previous research results : The Microsoft Security Response Center (MSRC) classified all reported Microsoft security vulnerabilities since 2004, and about 70% of all Microsoft ’s annual patches are for memory security vulnerabilities. Fix.

1) The Microsoft Security Response Center once explained that this is because most of their products are written in C and C ++, and these two programming languages ​​belong to the category of "memory-unsafe". A vulnerability in the developer code that manages memory execution can lead to a series of memory security errors.

2)) Google also faces a similar situation. Only from March 2019 to the present, out of 130 Chrome vulnerabilities rated "serious", 125 are related to memory corruption, which shows that memory management is still a big problem.

3) To this end, Google engineers must follow " The Rule of 2 " ( The Rule of 2 ). That is, whenever an engineer writes a new Chrome feature, its code must not break the following two or more conditions:

>This code handles untrusted input
Code runs without sandbox
Code is written in an unsafe programming language (C / C ++)

1)So far, Google has been trying to use the sandbox method in Chrome. They isolate dozens of processes into their own sandboxes, and recently introduced the "site isolation" feature, which puts the resources of each site into their own sandbox processes. But Google engineers said that considering performance issues, their method of using sandboxed Chrome components has reached the maximum benefit, and they must now seek new methods.

2) Therefore, Google plans to research and develop custom C ++ libraries for use with Chrome ’s codebase, which can better protect memory-related errors.

3) At the same time, Google is also exploring the MiraclePtr project, which aims to turn "use-after-free bugs into unsafe crashes with acceptable performance, memory, binary size, and minimal stability impact."

4) Finally, it is worth noting that Google has indicated that it plans to use "safe" language to explore when possible. Candidates include Rust, Swift, JavaScript, Kotlin, and Java.

@UndercodeTesting
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🦑Topic hack news by undercode

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🦑2020 Min Linux Steam Requirements-from git sources :

team for Linux requires the following:

1 GHz Pentium 4 or AMD Opteron with:

1) x86-64 (AMD64) instruction set

2) CMPXCHG16B instruction support (cx16 in /proc/cpuinfo flags)

3) SSE3 instruction support (pni in /proc/cpuinfo flags)

4) 512 megabytes of RAM and 5 gigabytes of hard drive space, or better

5) Internet connection (Cable/DSL speeds recommended)

6) Latest Ubuntu LTS, fully updated

7) 64-bit (x86-64, AMD64) Linux kernel

8) 64-bit (x86-64, AMD64) and 32-bit (i386, IA32) graphics drivers and glibc

9) Latest graphics driver

10) NVidia driver support - For recent cards (e.g. series 8), you will need to install 310.x. For older cards, driver 304.x supports the NVidia 6 and 7 GPU series. To access these drivers, first update your cache and then install the specific driver you need from the list in Additional Drivers.

11) AMD driver support - For recent cards (e.g. series 5 and above), we recommend installing the 12.11 driver. For older cards, Catalyst 13.1 Legacy supports the HD 2400 Pro card and is the latest for the 2 and 4 GPU series.

12) Intel HD 3000/4000 driver support - you will need to use the latest Mesa drivers, Mesa 9 or later.

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