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Showing posts with label LAB MANUAL. Show all posts
Showing posts with label LAB MANUAL. Show all posts

Preprocessing On Dataset

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Support Vector Machines IMPLEMENTATION IN WEKA TOOL

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Decision Tree implementation in weka tool

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Bayesian Algorithm implementation in weka tool

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Hierarchical Clustering Algorithm Implementation in weka tool

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K-means clustering implementation in weka tool

Procedure:
Step1: Open the data file in Weka Explorer. It is presumed that the required data fields have been discretized. In this example it is age attribute.
Step2: Clicking on the associate tab will bring up the interface for association rule algorithm.
Step3: We will use K-means algorithm. This is the default algorithm.

Step4: Inorder to change the parameters for the run (example support, confidence etc) we click on the text box immediately to the right of the choose button.







Scheme:       weka.clusterers.SimpleKMeans -init 0 -max-candidates 100 -periodic-pruning 10000 -min-density 2.0 -t1 -1.25 -t2 -1.0 -N 3 -A "weka.core.EuclideanDistance -R first-last" -I 500 -num-slots 1 -S 10
Relation:     labor-neg-data
Instances:    57
Attributes:   17
              duration
              wage-increase-first-year
              wage-increase-second-year
              wage-increase-third-year
              cost-of-living-adjustment
              working-hours
              pension
              standby-pay
              shift-differential
              education-allowance
              statutory-holidays
              vacation
              longterm-disability-assistance
              contribution-to-dental-plan
              bereavement-assistance
              contribution-to-health-plan
              class
Test mode:    evaluate on training data
=== Clustering model (full training set) ===
kMeans
======
Number of iterations: 3
Within cluster sum of squared errors: 119.5224194214812

Initial starting points (random):

Cluster 0: 1,5.7,3.971739,3.913333,none,40,empl_contr,7.444444,4,no,11,generous,yes,full,yes,full,good
Cluster 1: 1,2,3.971739,3.913333,tc,40,ret_allw,4,0,no,11,generous,no,none,no,none,bad
Cluster 2: 2,2.5,3,3.913333,tcf,40,none,7.444444,4.870968,no,11,below_average,yes,half,yes,full,bad

Missing values globally replaced with mean/mode

Final cluster centroids:
                                                    Cluster#
Attribute                            Full Data             0             1             2
                                        (57.0)        (36.0)         (5.0)        (16.0)
========================================================================================
duration                                2.1607        2.2267           1.4          2.25
wage-increase-first-year                3.8036        4.4695           3.2        2.4938
wage-increase-second-year               3.9717        4.4175         4.183        2.9027
wage-increase-third-year                3.9133        4.1093        3.9133        3.4725
cost-of-living-adjustment                 none          none          none          none
working-hours                          38.0392       37.4766       39.2078         38.94
pension                             empl_contr    empl_contr          none    empl_contr
standby-pay                             7.4444        7.9938        6.7556        6.4236
shift-differential                       4.871        5.4776        3.1484        4.0444
education-allowance                         no            no            no            no
statutory-holidays                     11.0943       11.4801          10.6       10.3809
vacation                         below_average      generous below_average below_average
longterm-disability-assistance             yes           yes            no           yes
contribution-to-dental-plan               half          half          none          half
bereavement-assistance                     yes           yes            no           yes
contribution-to-health-plan               full          full          none          full
class                                     good          good           bad           bad

Time taken to build model (full training data) : 0.01 seconds

=== Model and evaluation on training set ===
Clustered Instances

0      36 ( 63%)
1       5 (  9%)
2      16 ( 28%)
Scheme:       weka.clusterers.SimpleKMeans -init 0 -max-candidates 100 -periodic-pruning 10000 -min-density 2.0 -t1 -1.25 -t2 -1.0 -N 2 -A "weka.core.EuclideanDistance -R first-last" -I 500 -num-slots 1 -S 10
Relation:     labor-neg-data
Instances:    57
Attributes:   17
              duration
              wage-increase-first-year
              wage-increase-second-year
              wage-increase-third-year
              cost-of-living-adjustment
              working-hours
              pension
              standby-pay
              shift-differential
              education-allowance
              statutory-holidays
              vacation
              longterm-disability-assistance
              contribution-to-dental-plan
              bereavement-assistance
              class
Ignored:
              contribution-to-health-plan
Test mode:    Classes to clusters evaluation on training data
=== Clustering model (full training set) ===

kMeans
======

Number of iterations: 5
Within cluster sum of squared errors: 122.05464734126849

Initial starting points (random):

Cluster 0: 1,5.7,3.971739,3.913333,none,40,empl_contr,7.444444,4,no,11,generous,yes,full,yes,good
Cluster 1: 1,2,3.971739,3.913333,tc,40,ret_allw,4,0,no,11,generous,no,none,no,bad

Missing values globally replaced with mean/mode

Final cluster centroids:
                                                    Cluster#
Attribute                            Full Data             0             1
                                        (57.0)        (43.0)        (14.0)
==========================================================================
duration                                2.1607         2.213             2
wage-increase-first-year                3.8036        4.2024        2.5786
wage-increase-second-year               3.9717         4.221        3.2062
wage-increase-third-year                3.9133        4.0329        3.5462
cost-of-living-adjustment                 none          none          none
working-hours                          38.0392       37.6557       39.2171
pension                             empl_contr    empl_contr          none
standby-pay                             7.4444        7.7778        6.4206
shift-differential                       4.871        5.2018        3.8548
education-allowance                         no            no            no
statutory-holidays                     11.0943       11.2878          10.5
vacation                         below_average below_average below_average
longterm-disability-assistance             yes           yes           yes
contribution-to-dental-plan               half          half          none
bereavement-assistance                     yes           yes           yes
class                                     good          good           bad

Time taken to build model (full training data) : 0 seconds
=== Model and evaluation on training set ===
Clustered Instances

0      43 ( 75%)
1      14 ( 25%)

Class attribute: contribution-to-health-plan
Classes to Clusters:

  0  1  <-- assigned to cluster
 20  8 | none
  9  0 | half
 14  6 | full

Cluster 0 <-- none
Cluster 1 <-- full

Incorrectly clustered instances :           31.0        54.386  %


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DATA MINING LABORATORY- IT6711

Hardware Requirements
   RAM Memory -2 GB or more
   Intel Pentium 4 or AMD Athlon 2 GHz (or faster)
   1 GB (or more) available hard disk space

Software Requirements
         SQL SERVER 2008,WEKA TOOL,JDK 1.8
EXPERIMENTS: 

1. Creation of a Data Warehouse.
2. Apriori Algorithm.
3. FP-Growth Algorithm.
4. K-means clustering.
5. One Hierarchical clustering algorithm.
6. Bayesian Classification.
7. Decision Tree.
8. Support Vector Machines.
9. Applications of classification for web mining.

10. Case Study on Text Mining or any commercial application.

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FP-Growth Algorithm implementation in weka tool

Procedure:
Step1: Open the data file in Weka Explorer. It is presumed that the required data fields have been discretized. In this example it is age attribute.
Step2: Clicking on the associate tab will bring up the interface for association rule algorithm.
Step3: We will use FP-Growth algorithm. This is the default algorithm.
Step4: Inorder to change the parameters for the run (example support, confidence etc) we click on the text box immediately to the right of the choose button.
Data set:
Shopping.arff
@relation shopping
@attribute milk{yes,no}
@attribute bread{yes,no}
@attribute honey{yes,no}
@attribute ghee{yes,no}
@attribute jam{yes,no}
@data
yes,yes,no,no,yes
no,yes,no,yes,no
no,yes,yes,no,no
yes,yes,no,yes,no
yes,no,yes,no,no
no,yes,yes,no,no
yes,no,yes,no,no
yes,yes,yes,no,yes

yes,yes,yes,no,no





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Apriori Algorithm implementation in weka tool

Procedure:
Step1: Open the data file in Weka Explorer. It is presumed that the required data fields have been discretized. In this example it is age attribute.
Step2: Clicking on the associate tab will bring up the interface for association rule algorithm.
Step3: We will use apriori algorithm. This is the default algorithm.
Step4: Inorder to change the parameters for the run (example support, confidence etc) we click on the text box immediately to the right of the choose button.
Data set:
Shopping.arff
@relation shopping
@attribute milk{yes,no}
@attribute bread{yes,no}
@attribute honey{yes,no}
@attribute ghee{yes,no}
@attribute jam{yes,no}
@data
yes,yes,no,no,yes
no,yes,no,yes,no
no,yes,yes,no,no
yes,yes,no,yes,no
yes,no,yes,no,no
no,yes,yes,no,no
yes,no,yes,no,no
yes,yes,yes,no,yes

yes,yes,yes,no,no






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SECURITY LABORATORY CS6711

                                        Hi this is our second lab manual from gr-solution.The first lab manual for Mobile Application Development lab has been used more than hundred colleges in tamilnadu and Andra Pradesh.We thank all the buyer and  we also get lot of good feedback.

                   
Hardware Requirements
   RAM Memory -2 GB or more
   Intel Pentium 4 or AMD Athlon 2 GHz (or faster)
   1 GB (or more) available hard disk space

Software Requirements
    Java Runtime Environment 1.6 or above
    GnuPG,KF sensor,Net Strumbler,snort

Lab manual features
*A complete Lab Manual with pdf contain source code,Procedure,software installation procedure and  also software  for all experiment.
*Step by step procedure to install and execute all the programs.
*Anna University Latest Sylabus.
*Easy to execute all the experiment .
*Step by step procedure is available with the manual.So that  easily Run the experiment without the help of other.

A complete Lab Manual with source code for all experiment.

LabManual cost details
                     LAB MANUAL DVD(Rs 1000)
                                              The labmanual with dvd it includes all experiment in pdf,software and installation procedure.The labmanul cost RS1000.The dvd will be reach you with in one or two days.
                    LAB MANUAL SOFT COPY(Rs 700)
                                              The softcopy of lab manual is availabe via email.It includes all experiment in pdf,installation procedure.The labmanual cost RS700.The email will reach you shortly after payment.

Payment method:
                                              The payment has  be made to the following bank account.After payment contact as we will send our lab manual.

contact details:
CELL:8344790950
          
EMAIL:hitechguil@gmail.com 

Bank Account Details:

BANK NAME :STATE BANK OF INDIA
ACCOUNT NO:20222847933
ACCOUNT HOLDER NAME:P GUILBERT RAJ
IFSC CODE:SBIN0010501



EXPERIMENTS 

1)Implement the following SUBSTITUTION & TRANSPOSITION TECHNIQUES concepts:
 a) Caesar Cipher
b) Playfair Cipher
c) Hill Cipher
d) Vigenere Cipher
e) Rail fence – row & ColumnTransformation 

2. Implement the following algorithms
a) DES
b) RSA Algorithm
c) Diffiee-Hellman
d) MD5

e) SHA-1
3 Implement the SIGNATURESCHEME - Digital Signature Standard 

4. Demonstrate how to providesecure data storage, secure data transmission and for creating digitalsignatures (GnuPG). 

5. Setup a honey pot andmonitor the honeypot on network (KF Sensor) 





1 comment:

Demonstrate how to provide secure data storage, secure data transmission and for creating digital signatures (GnuPG).

PROCEDURE :


FOR FULL PROCEDURE,CODING and lab manual
CONTACT:
CELL:9789697608
           9566627095
EMAIL:hitechguil@gmail.com 


OUTPUT:



4 comments:

Demonstrate intrusion detection system (ids) using any tool (snort or any other s/w)

PROCEDURE :


FOR FULL PROCEDURE,CODING and lab manual
CONTACT:
CELL:9789697608
           9566627095
EMAIL:hitechguil@gmail.com 


OUTPUT:

1 comment:

Installation of rootkits and study about the variety of options

PROCEDURE :


FOR FULL PROCEDURE,CODING and lab manual
CONTACT:
CELL:9789697608
           9566627095
EMAIL:hitechguil@gmail.com 


OUTPUT:



No comments:

Setup a honey pot and monitor the honeypot on network (KF Sensor)

PROCEDURE :


FOR FULL PROCEDURE ,CODING and lab manual
CONTACT:
CELL:9789697608
           9566627095
EMAIL:hitechguil@gmail.com 


OUTPUT:




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Implement the SIGNATURE SCHEME - Digital Signature Standard in java

PROGRAM CODE:
TestDigitalSignature.java
import java.security.KeyPair;
import java.security.PrivateKey;
import java.security.PublicKey;
import java.security.Signature;

//import PublicKeyUtil.*;
//import SignatureUtil.*;

public class TestDigitalSignature {
 public static void main(String args[]) throws Exception {
  String file = "emp.txt";

  /* Public key stored in this file */
  String publicKeyFile = "publicKey.txt";

  /* Signature of given file stored here */
  String signatureFile = "siganture.txt";

  /* Signature algorithm to get Signature instance */
  String sigAlgorithm = "SHA1withDSA";

  /* generate public and private keys */
  KeyPair keyPair = PublicKeyUtil.getKeyPair("DSA");
  PublicKey publicKey = keyPair.getPublic();
  PrivateKey privateKey = keyPair.getPrivate();

  /* Generate signature for given file */
  byte signature[] = SignatureUtil.getSignature(file, privateKey,
    sigAlgorithm);

  /* Save public key */
  PublicKeyUtil.saveKey(publicKey, publicKeyFile);

  /* Save signature */
  SignatureUtil.saveSignature(signatureFile, signature);

  // Verify Signature

  /* Read public key from file */
  byte[] pubKeyBytes = PublicKeyUtil.readKeyFromFile(publicKeyFile);

  /* Convert publick key bytes into PublicKey object */
  PublicKey pubKey = PublicKeyUtil.convertArrayToPubKey(pubKeyBytes,
    "DSA");

  /* Read signature from file */
  byte[] receivedSignature = SignatureUtil
    .readSignatureFromFile(signatureFile);

  /* Verify signature */
  Signature verifySignature = Signature.getInstance(sigAlgorithm);

  /* initialize signature object */
  verifySignature.initVerify(pubKey);

  /* Feed data */
  SignatureUtil.feedData(verifySignature, file);

  /* Verify signature */
  boolean isAuthenticated = verifySignature.verify(receivedSignature);

  if (isAuthenticated) {
   System.out.println("Data is authenticated");
  } else {
   System.out.println("Data is not from expected sender");
  }

 }
}

PublicKeyUtil.java

FOR FULL PORGRAM and lab manual
CONTACT:
CELL:9789697608
           9566627095
EMAIL:hitechguil@gmail.com 
SignatueUtil.java
FOR FULL PORGRAM and lab manual
CONTACT:
CELL:9789697608
           9566627095
EMAIL:hitechguil@gmail.com

OUTPUT:
PUBLIC KEY


SIGNATURE
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