Free analysis tool software for linux

Directory Analysis Tool is used to analyze LDAP directories and report on their contents.

Useful if you want to find inactive accounts, people who havent changed passwords, or who has administrator privileges.

Net::Analysis are modules for analysing network traffic.

SYNOPSIS

Using an existing analyser:

$ perl -MNet::Analysis -e main help
$ perl -MNet::Analysis -e main TCP,v=1 dump.tcp - basic TCP info
$ perl -MNet::Analysis -e main HTTP,v=1 dump.tcp - HTTP stuff
$ perl -MNet::Analysis -e main Example2,regex=img dump.tcp - run an example

Writing your own analyser:

package MyExample;
use base qw(Net::Analysis::Listener::Base);
# Listen to events from other modules
sub tcp_monologue {
my ($self, $args) = @_;
my ($mono) = $args->{monologue};
my $t = $mono->t_elapsed()->as_number();
my $l = $mono->length();
# Emit your own event
$self->emit(name => example_event,
args => { kb_sec => ($t) ? $l/($t*1024) : N/A }
);
}
# Process your own event
sub example_event {
my ($self, $args) = @_;
printf "Bandwidth: %10.2f KB/secn", $args->{kb_sec};
}
1;
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ABSTRACT

Net::Analysis is a suite of modules that parse tcpdump files, reconstruct TCP sessions from the packets, and provide a very lightweight framework for writing protocol anaylsers.
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I wanted a batch version of Ethereal in Perl, so I could:

- sift through parsed protocols with structured filters
- write custom reports that mixed events from multiple protocols

So here it is. Net::Analysis is a stack of protocol handlers that emit, and listen for, events.

Market Analysis System (MAS) is an open-source software application that provides tools for analysis of financial markets using technical analysis.
Market Analysis System provides facilities for stock charting and futures charting, including price, volume, and a wide range of technical analysis indicators. Market Analysis System also allows automated processing of market data - applying technical analysis indicators with user-selected criteria to market data to automatically generate trading signals - and can be used as the main component of a sophisticated trading system.
Main features:
- Includes basic technical analysis indicators, such as Simple Moving Average, Exponential Moving Average, Stochastic, MACD, RSI, On Balance Volume, and Momentum.
- Includes more advanced indicators, such as Standard Deviation, Slope of EMA of Volume, Slope of MACD Signal Line, Bollinger Bands, and Parabolic SAR.
- User can create new technical analysis indicators, including complex indicators based on existing indicators.
- User can configure criteria for automated trading-signal generation.
- Creation of weekly, monthly, quarterly, and yearly data from daily data.
- Handles intraday data.
- Handles stock and futures data.
- Accepts input data from files, from a database, or from the web. (Includes a configuration for obtaining end-of-day data from yahoo.com.)
- Can be configured and run as a server that provides services for several clients at a time running on remote machines.

Statistical Traffic Analysis Kit is a set of command-line traffic analysis tools, designed to help a network administrator to see what is happening at a router at the moment.

Unlike tcpdump (1), the stak set uses statistical and stream-oriented methods, and will rarely produce an output stream at a speed beyond human perception. The output is less accurate.

The kit consists of five different utilities, designed to perform the following tasks:
estimating overall traffic rates (stakrate),
determining network nodes generating the highest traffic (stakhosts)
monitoring the amount of traffic exchanged with particular autonomous
systems (stakasta),
extracting strings from packets (stakextract),
determining connections and flows generating the highest traffic
(stakstreams, experimental),

The Java Binary Enhancement Tool (JBET) is a general Java program analysis and manipulation tool. Existing class files can be disassembled, reassembled, or edited programmatically through the JBET API. JBET can also be used to create new Java class files from scratch. JBET uses a convenient internal representation of all the contents of Java binary (.class) files, allowing the user to edit the classes easily, in a structured manner.

JBET was developed as part of the DARPA Self-Protecting Mobile Agents project under the OASIS and Active Networks programs (contract number N66001-00-C-8602) in order to study automated software obfuscation.

The Java language was chosen for this project because of the (relative) ease of constructing binary editing tools provided by the large amount of type information present in the class files. Our two reports, the Obfuscation Techniques Evaluation Report, and the Obfuscation Report, are available from the download area. The obfuscation tool developed is not part of this release.

JBET was also used in the DARPA/AFRL Survivable Server project (contract number F30602-00-C-0183) to add additional security checks to the Java Standard Library. (The Java SecurityManager API does not support many desirable security checks, such as continued authorization of file accesses after opening.)

JBET was used to replace the native method references in the Java standard library with stubs that call a pluggable security policy. This tool, called Jpolicy, is also available for download at this website. Jpolicy is very incomplete at this time, but may be interesting to those working in Java security or changing the standard library themselves.

The internal representation of Java class files used by JBET is intented to make it easy for programmers to write Java binary code transforms. Each element of Java class files has a corresponding internal data structure: ClassInfo for entire classes, MethodInfo for methods, FieldInfo for fields, Snippit for code blocks, and Instruction for individual instructions. Snippit and Instruction understand Java opcode syntax and semantics, allowing automated creation of valid Java programs. A Java-compatible class verifier is also included.

Some code transforms are difficult to program directly by manipulating Java instructions. For those transforms, a directed acyclic graph (DAG) representation of code is available. In the DAG representation, each basic block has a corresponding DAG, with a set of input and output nodes. Edges in the graph connect "producer" nodes (such as constants, or the result of calculations) to "user" nodes (such as method calls or other calculations). Methods are divided into basic blocks and control flow is stored at the basic block level (possible because Java has only fixed jump targets)

JBET requires a Java 1.4 virtual machine to run, although it can operate on class files from earlier Java versions. The packaging and build environment supplied supports Linux and Windows with Cygwin; however, the build process is simple and could be performed manually on other platforms. Perl is required for regression testing.

Jpolicy requires a Java 1.4 virtual machine to build, either Linux or Windows NT/XP with Cygwin. gcc is required for building on Windows (supplied with Cygwin). The runtime system can be either Java 1.3 or 1.4 (with Suns JVM only), running on Linux or Windows NT/XP. Windows 9x and Windows 2000 may work as well, but have not been tested.

Installation

1. Install jdk 1.4.1.
2. Set CLASSPATH to jdk1.4.1/jre/lib/rt.jar
3. cd src; make
4. If that didnt work, examine the makefile. java or javac may not be in the path.
5. To build a jar file that can be used with "java -jar jbet.jar", run "make jar".
6. If you have perl installed, run the tests with "make test".
Optionally, run "make regen; make test".

Make a symbolic link from jbet3/bin/jbet to somewhere in your path.

Usage

JBET uses the JNI format for class names, and JNI type and method descriptors. For a summary of this syntax, use jbet help syntax. Suns JVM specification may also be helpful.

To look at a class disassembly, use jbet print. Try disassembling a class you have source for, and was built with debug info (-g): jbet -P < classpath > print < classname >. Suns JVM specification has an instruction reference.

Plucene::Analysis::PorterStemFilter - Porter stemming on the token stream.

SYNOPSIS

# isa Plucene::Analysis:::TokenFilter

my $token = $porter_stem_filter->next;

This class transforms the token stream as per the Porter stemming algorithm.
Note: the input to the stemming filter must already be in lower case, so you will need to use LowerCaseFilter or LowerCaseTokenizer farther down the Tokenizer chain in order for this to work properly!

The Porter Stemmer implements Porter Algorithm for normalization of English words by stripping their extensions and is used to generalize the searches. For example, the Porter algorithm maps both search and searching (as well as searchnessing) to search such that a query for search will also match documents that contains the word searching.

Note that the Porter algorithm is specific to the English language and may give unpredictable results for other languages. Also, make sure to use the same analyzer during the indexing and the searching.

You can find more information on the Porter algorithm at www.tartarus.org/~martin/PorterStemmer.

A nice online demonstration of the Porter algorithm is available at www.scs.carleton.ca/~dquesnel/java/stuff/PorterApplet.html.

METHODS

next

my $token = $porter_stem_filter->next;

Returns the next input token, after being stemmed.

DREAM Tool is a generic framework that aims at providing a common semantic domain which can express several (real-time, power consumption, resource) constraints.
The semantic domain has an executable C++ model which has been semantically anchored to the hybrid automata formalism. Although the model of computation corresponds to hybrid automata, so far we have been successful in analyzing the models by conservative approximation using timed automata on moderate size systems.
To deal with large-scale examples the user has the ability to use the simulation/testing interface on the executable C++ model before implementing the system. We plan to extend the genetic algorithms to solve a large number of problems.
DREAM is a simulation and verification framework which provides a formal model and analysis of your system in less than a day. Systems in DREAM are specified using XML in a straightforward way. I hope you find it useful.
Real-time middleware provides dependable and efficient platforms supporting key functional and quality of service (QoS) needs of distributed real-time embedded (DRE) systems.
Key challenges in DRE system developments include safe composition of system components and mapping the functional specifications onto the target platform. Model-based technologies help address these issues by enabling design-time analysis and providing the means for the rapid evaluation of design alternatives with respect to end-to-end QoS properties, predictability and performance measures before committing to a specific platform.
The Distributed Real-time Embedded Analysis Method DREAM is an open-source tool and method for optimizing multiple quality of service (QoS) properties of distributed real-time embedded (DRE) systems. The project focuses on the practical application of formal analysis methods to real-time middleware to automate the verification, development, configuration, and integration of middleware-based DRE systems.
Enhancements:
- This version implemented several optimizations for improved model checking performance, resulting in impressive performance gains of at least 2-3 times.
- There are no known memory leaks present in the current release.
- Balanced AVL trees are now used, resulting in exponential speedups in several steps of the model checking method.
- XML Schema validation was implemented.
- Verification time reporting was upgraded to include data on the simulation speeds.
- Random simulation-based testing now provides an execution trace when a deadline is missed.

TA-Lib provides common functions for the technical analysis of stock/future/commodity market data.
TA-Lib can be reused by trading software developers using Excel, .NET, Java, Perl or C/C++.
Main features:
- More than 120 technical analysis indicators such as ADX, MACD, RSI, Stochastic, Bollinger Bands etc...
- bullet Includes candlestick pattern recognition.
- bullet Optional abstract interface allowing your code to support new technical analysis functions without any code change!
Enhancements:
New Features
- New Functions: BETA, MINMAX, MINMAXINDEX, MININDEX, MAXINDEX
- Debian and RPM packaging available.
- Java JAR packaging available.
- New TA_FunctionDescription() returns XML description of API.
- New ta_func_api.xml file generated in root directory of the package.
- Support for unmanaged static libraries with Visual Studio 2005.
Fixes
- #1526632 : Fix bug in LINEARREG_ANGLE
- #1544555 : Now do proper divide by zero detection in TA_ADX
Other Changes
- Better Java/.NET naming convention.
- ta_func_list.txt moved in root directory of the package.
- Removed dependencies on trio and Mersenne Twister functions.
- Volume and Open Interest are now double instead of integers.
- Add license specific to Excel users.