2. ABSTRACT
The Project “Network Intrusion
Detection Systems Using Genetic
Algorithm” contains a brief overview of
Intrusion Detection System (IDS), Genetic
Algorithm (GA), and related detection
techniques. This is helpful for identification of
complex anomalous behaviors.
3. EXISTING SYSTEM
The rules in the dataset are static unless the
network administrator manually enters the
rules. It does not provide any option for
generating dynamic rule set. It requires manual
energy to monitor the Inflowing packets and
analyze their behavior .
4. DISADVANTAGES
They are complex
They are rules dependent
They are manual.
It cannot take decisions in runtime.
It cannot create its own rule depending on the
current situation.
5. PROPOSED SYSTEM
It is an artificial intelligence based
problem-solving system. It includes both
temporal and spatial information of the
network traffic in the rule set.
6. ADVANTGES
It eliminates the need for an attack to be
previously known to be detected because
malicious behavior is different from normal
behavior by nature.
It generates its own rules depending on the
real-time behavior of the packet.
Using a generalized behavioral model is
theoretically more accurate, efficient and
easier to maintain.
7. Hardware Requirements
• Processor : Intel Pentium III or above
• Memory : 128 MB or above
• Hard Disk Drive : 10 GB or above
Software Requirements
• OS Platform : Windows xp
• Software : JDK1.4.2 or later versions
10. Modules
• Client’s Communication
• IDS implementation
• Chromosome Conversion
• Implementation of Genetic Algorithm
• Creating rules in Dataset
11. Clients Communication
This module is responsible for the client side
communication system interface. It is used to
communicate between the source and the
destination. It receives the destination address,
source address and the inflowing port no and
binds them into a packet.
12. IDS Implementation
This is the server side interface which is preset in
the server system and is solely under the control of the
administrator. Any transaction in the network will be
monitored by the Server.
It sends each and every Inflowing packets header
information’s to the chromo convert module and then
receives the converted real-time Chromosomes. If the
particular chromosomes matches with the rules
provided in the rule set, it takes the decision of
whether allow or block depending on which rule set it
matches.
13. Chromosome Conversion
The collected attributes are converted into Chromosomes
within the range and in the same behavior.
The process of a genetic algorithm usually begins with a
randomly selected population of chromosomes. These
chromosomes are representations of the problem to be solved.
.
These positions are sometimes referred to as genes and are
changed randomly within a range during evolution.
The set of chromosomes during a stage of evolution are
called a population.
14. Genetic Algorithm
The Genetic Algorithm is implemented, for selecting the
best rule for matching with the connection.
During evaluation, the selection of chromosomes for
survival and combination is biased towards the fittest
chromosomes.
The Genetic Algorithm has 3 operations
1. Selection
2. Recombination
3. Mutation
16. Basic Steps of Genetic Algorithm
1.Randomly create a population of individuals.
2. Evaluate the population to see which individuals will
contribute the next generation.
3. To alter the new generation of individuals once they have
been paired off.
4. To discard the old population and perform step two on
the new population.
17. DATAFLOW Monitors the connection
DIAGRAM
Sniffer Real Time Chrom
Router Convert
Behavior
Chromosomes
Passing Converted
Source System
Chromosomes
Destination
Genetic
Algorith Check
m Data Set
Sends Passing
Result
Data System
Finalize
Decision taken by
Passing Genetic Algorithm
System
Hop Count
Found Bad User Found Good User
18. DATA FLOW
Packet Chromo
Input somes
Data Client Chromo IDS
Converter
DataSet
Check in
DataSet
New
Rules
Generate Genetic
DataSet Algorithm
20. UseCase Diagram
Enters data
Hopcount
extends
Destination
source
Passer
ChromoConverter
include
extends
extends
Genetic Algorithm
NormalData
Anomal Data
21. Usecase Diagram To Enter Rules
extends
New entry Normal
gives information
extends
extends
administrator Restrict user
Anamoly
22. Clientlogin
Activity Diagram
EntersHop
count
Enters into
Chromoconverter
Decision
taken by GA
Checks in
dataset
[ no ]
[ yes ]
message found an
sent intruder
23. Sequence Diagram
System Hopcount IDS Dataset
: Sender : Receiver
Enter sys. addr., port no and msg
check sys. addr., port no
Ask Inter Sys. no. and names
Enter Inter Sys no. and name
Check Sys. no. and name
Invalid System No. and name
Check the availability of the user
Restricted User
New rules are created
Created rules are added in the dataset
Message Send
42. Conclusion
• We discussed a methodology of applying genetic algorithm
into network intrusion detection.
• This implementation of genetic algorithm is more helpful for
identification of network anomalous behaviors.
• Future work includes creating a standard test data set for the
genetic algorithm proposed in this paper and applying it to a
test environment.
• Detailed specification of parameters to consider for genetic
algorithm should be determined during the experiments.
