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Browser security is the application of Internet security to web browsers in order to protect networked data and computer systems from breaches of privacy or malware. Security exploits of browsers often use JavaScript, sometimes with cross-site scripting (XSS) with a secondary payload using Adobe Flash. Security exploits can also take advantage of vulnerabilities (security holes) that are commonly exploited in all browsers (including Mozilla Firefox, Google Chrome, Opera, Microsoft Internet Explorer, and Safari). Web browsers can be breached in one or more of the following ways: Operating system is breached and malware is reading/modifying the browser memory space in privilege mode Operating system has a malware running as a background process, which is reading/modifying the browser memory space in privileged mode Main browser executable can be hacked Browser components may be hacked Browser plugins can be hacked Browser network communications could be intercepted outside the machine The browser may not be aware of any of the breaches above and may show user a safe connection is made. Whenever a browser communicates with a website, the website, as part of that communication, collects some information about the browser (in order to process the formatting of the page to be delivered, if nothing else). If malicious code has been inserted into the website's content, or in a worst-case scenario, if that website has been specifically designed to host malicious code, then vulnerabilities specific to a particular browser can allow this malicious code to run processes within the browser application in unintended ways (and remember, one of the bits of information that a website collects from a browser communication is the browser's identity- allowing specific vulnerabilities to be exploited). Once an attacker is able to run processes on the visitor's machine, then exploiting known security vulnerabilities can allow the attacker to gain privileged access (if the browser isn't already running with privileged access) to the "infected" system in order to perform an even greater variety of malicious processes and activities on the machine or even the victim's whole network.
[2]Paolo Prandoni, [3]LCAV, EPFL
In this notebook we will design and use filters to extract specific
audio frequencies from a music file.
In [1]:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import IPython
import scipy.signal as sp
from scipy.io import wavfile
infringment...)
In [2]:
IPython.display.Audio(s, rate=SF)
Out[2]:
Your browser does not support the audio element.
[2]Paolo Prandoni, [3]LCAV, EPFL
This notebook shows a simple way to process audio files (in WAV format)
in a block-by-block fashion; this is useful in two cases
1. when you want to process a data stream in real time but you can
accept a small processing delay; in this case a buffered approach
can yield computational gains
2. when you need to process a large file that does not fit entirely
into [2]Paolo Prandoni, [3]LCAV, EPFL
In this notebook we will investigate the effect of phase on the
perceptual quality of a sound. It is often said that the human ear is
commercial-grade audio equipment takes place in the magnitude domain
only.
In [1]:
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import IPython
from scipy.io import wavfile
In [2]:
plt.rcParams["figure.fig [2]Paolo Prandoni, [3]LCAV, EPFL
The Karplus-Strong algorithm is a simple method to generate synthetic
sounds with arbitrary pitch. In this notebook we will explore a few
variations of the method to illustrate some basic notions of
discrete-time signal processing.
In [1]:
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import IPython
In [2]:
plt.rcParams["figure.figsize"] [2]Paolo Prandoni, [3]LCAV, EPFL
In this notebook we will implement a simple fractional resampler that
uses local Lagrange interpolation. The resampler can be used to perform
any rational sampling rate change (but beware of aliasing when
downsampling!)
In [1]:
import numpy as np
import IPython
from scipy.io import wavfile
Local Lagrange interpolation[4]¶
To perform resampling we will need to compute subsample values and, in