ngrep strives to provide most of GNU grep s common features, applying them to the network layer. ngrep is a pcap-aware tool that will allow you to specify extended regular or hexadecimal expressions to match against data payloads of packets. It currently recognizes TCP, UDP and ICMP across Ethernet, PPP, SLIP, FDDI, Token Ring and null interfaces, and understands bpf filter logic in the same fashion as more common packet sniffing tools, such as tcpdump and snoop.
RFC1661 PPP協議
（RFC1661 The Point-to-Point Protocol (PPP)）
本備忘錄狀態
This memo provides information for the Internet community. It does
not specify an Internet standard of any kind. Distribution of this
memo is unlimited.
I. Introduction
This code exploits a previously undisclosed vulnerability in the bit string
decoding code in the Microsoft ASN.1 library. This vulnerability is not related
to the bit string vulnerability described in eEye advisory AD20040210-2. Both
vulnerabilities were fixed in the MS04-007 patch.
II. Screenshots
$ ./kill-bill.pl
. kill-bill : Microsoft ASN.1 remote exploit for CAN-2003-0818 (MS04-007)
by Solar Eclipse <solareclipse@phreedom.org>
Usage: kill-bill -p <port> -s <service> host
Services:
iis IIS HTTP server (port 80)
iis-ssl IIS HTTP server with SSL (port 443)
exchange Microsoft Exchange SMTP server (port 25)
smb-nbt SMB over NetBIOS (port 139)
smb SMB (port 445)
If a service is running on its default port you don t have to
specify both the service and the port.
Examples: kill-bill -s iis 192.168.0.1
kill-bill -p 80 192.168.0.1
kill-bill -p 1234 -s smb 192.168.0.1
數字運算，判斷一個數是否接近素數
A Niven number is a number such that the sum of its digits divides itself. For example, 111 is a Niven number because the sum of its digits is 3, which divides 111. We can also specify a number in another base b, and a number in base b is a Niven number if the sum of its digits divides its value.
Given b (2 <= b <= 10) and a number in base b, determine whether it is a Niven number or not.
Input
Each line of input contains the base b, followed by a string of digits representing a positive integer in that base. There are no leading zeroes. The input is terminated by a line consisting of 0 alone.
Output
For each case, print "yes" on a line if the given number is a Niven number, and "no" otherwise.
Sample Input
10 111
2 110
10 123
6 1000
8 2314
0
Sample Output
yes
yes
no
yes
no
These instructions assume that the 1.4 versions of the java
and appletviewer commands are in your path. If they aren t,
then you should either specify the complete path to the commands
or update your PATH environment variable as described in the
installation instructions for the Java 2 SDK.
A command-line file compression utility for Windows NT. It
allows you to select files for NTFS file compression based on
file name (with wild- cards), minimum file size, and/or a
minimum compression ratio. You can also specify file extensions
of files to be ignored. With C++ src
This simulation script set allows for an OFDM transmission to be
simulated. Imagetx.m generates the OFDM signal, saving it as a
windows WAV file. This allows the OFDM signal to be played out a sound
card and recorded back. Imagerx.m decodes the WAV to extract the
data.
settings.m contains all the common settings to specify all the
simulation parameters such as FFT size, number of carriers,
input data source file, input and output WAV files, etc.
design LP,HP,B S digital Butterworth and Chebyshev
filter. All array has been specified internally,so user only need to
input f1,f2,f3,f4,fs(in hz), alpha1,alpha2(in db) and iband (to specify
the type of to design). This program output hk(z)=bk(z)/ak(z),k=1,2,...,
ksection and the freq.
SVMcfg: Learns a weighted context free grammar from examples. Training examples (e.g. for natural language parsing) specify the sentence along with the correct parse tree. The goal is to predict the parse tree of new sentences.
SVMhmm: Learns a hidden Markov model from examples. Training examples (e.g. for part-of-speech tagging) specify the sequence of words along with the correct assignment of tags (i.e. states). The goal is to predict the tag sequences for new sentences.
