Abstract: technologies to combat it. Intrusion Detection system, Data

Abstract: today cyber-security becomes
a need as it provides protection from highly vulnerable intrusions and
threats.it is impractical for human without considerable automation to handle
cyber threat and highly vulnerable intrusions. To handle this situation, it needs to develop sophisticated, flexible,
robust and adaptable software also called cyber defense system. This is enough
intelligent system to detect a variety of threats, refine and update these
technologies to combat it. Intrusion Detection system, Data Mining, and Computational Intelligence system are
Artificial Techniques which provide detection and prevention of highly vulnerable threats and intrusions. The
aim of this paper to present the progress in the field of AI for defending from
cyber-crimes, to describe how these techniques are effective as well as provide
the scope of future work.

 

Keywords: Artificial Intelligence, Data mining, Cyber Defense system, Intrusion
Detection System, Computational Intelligence system

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1.     
INTRODUCTION

Cybercrime is a most complex problem
in the cyber
world.it is defined as any illegal activity that applied to a computer to harm the system or system
files and the computer security.

A recent study on cybercrime shows that it is impractical to handle
cyber-crimes for human without considerable automation. Furthermore, conventionally fixed algorithms are also not
enough to handle the dynamically evolving cyber threats. To handle this
situation, it needs to develop
sophisticated and flexible software for protection and prevention from cyber
threats. Cyber Defense system able to detect many of the cyber-attack and
alerts the system. Human intervention is simply not enough to analysis the cyber threats and appropriate
response. Cyber-attack is carried out with smart agents of worms and viruses.
Smart semi-autonomous agents used to defend against
cyber-threats.  This so-called system able to find out the type of
threat, the response of threat, and the object of threat.it also able to find
out how to check and stop the secondary
attack. A variety of CDS were introduced but there is need to refine and
update CDS to introduce the various techniques of AI. These techniques improve the security measures.

Artificial intelligence offers many
computing methods like Data Mining,
Computational Intelligence System, Intrusion Detection
System, Neural Network, Pattern Recognition, Fuzzy Logic, Machine Learning,
Expert System, Intelligent Agents, Search, Learning, Constraint Solving etc.
Computational Intelligence System, Data
Mining, and Intrusion Detection System
have furthermore typed.

Data Mining technique is applied to observe the intrusions by recognizing the patterns of program and user
activity. .Association, Clustering, Classification, Prediction, and Sequence Patterns are Data Mining techniques.

The Computational Intelligent System usually includes Fuzzy Logic, Evolutionary Computation, Cellular Automata,
Intelligent Agent Systems, ANN, Artificial Immune Systems models. These
techniques allow efficient decision making. The
artificial immune system model is taken from the immune system. The Biological
Immune System is natural defense system provides
protection against averse to many diseases. Artificial Immune System,
Artificial Neural Network, Genetic Algorithms are important techniques of
Artificial Immune System.

Intrusion Detection (ID) is a process to monitor the traffic in the
network and monitor the strange activities and alert the system as well as a network administrator. Intrusion Prevention
(IP) is the procedure of observing the
traffic in the network, used to identify the threats and respond it quickly.
IDPS used to detect the problems in the network and solve these problems. Here
present three types of IDPS, first is network based and second host-based and third
is a honeypot. There are 2 types of IDS
anomaly and misuse detection.

The second session of the present paper
introduces the existing techniques of
artificial intelligence in information technology security. The third session explains the existing techniques of data-mining
in the information technology security. The fourth session explains the computational intelligent system
in cybersecurity. The fifth session explains
the existing techniques of IDS in cybersecurity. The Sixth session explains the abbreviation and acronyms and the
seventh session explains the conclusion and future scope.

Hence, in this paper, by implement AI on ICDS is proposed to make the
defense system more effective.

2.     
ARTIFICIAL
INTELLIGENCE

AI is an electronic machine that is enough intelligent
to act like human beings. It resolves the
complicated problems rapidly than human beings such as playing the chess game. This paper represents the specific method of AI to solve cybercrimes. These
methods are described here.

2.1.
Artificial Neural Nets

Artificial Neural Net is introduced after
inspiring the Natural Biological Nervous System.
A Neuron is formed by interconnected processing components. ANN consists of a number of
artificial neurons.it works like a human brain
but it has fewer complex neuron
connection than the biological nervous system.
Neuron received a lot of inputs and rapidly parallel respond to it. A neural net begins
with the invention of perceptron by Frank Rosenblatt in 1957.the main feature
of ANN is rapidly responding and speed of
operation. ANN is mainly configured for learning, classification, for recognizing the pattern.it is also applied to
select the appropriate response.

     An ANN is applied for DOS recognition in the network, worm
recognition in computer, malware recognition in the computer, and for zombie recognition in computer and malware
classification in forensic investigation.

ANN is well liked for its high speed to perform an operation.it may be
implemented in hardware as well as software. If it is implemented in hardware than
it is used in the graphics processor. A
lot of technologies of ANN is developed such as third generation neural nets.

    A distinguish feature of ANN
that it is used for intrusion detection system and perform high-speed operations.

2.2.
Intelligent Agents

Intelligent agents are computer-generated
effects that show respond when an unexpected event occurs. They exchange information with each other for motility and flexibility in the environment to make the IA technology more effectively to combat against cyber-attack. IA
give more information about the cyber-attack .it work on internet and give
information without our permission.

Intelligent behavior of intelligent agent makes them more special reactiveness, understanding of associate
agent communication language, reactivity (ability to create some alternatives
and to act).they use for mobility, reflection ability and for planning ability.

It is used against DDOS. Intelligent agents are cooperative agents that
give efficient defense against DOS and DDOS attack.  ‘Cyber police’ consist of intelligent agents
is developed after solving some commercial, industrial and legal problems. It
supports the intelligent agent’s quality and communication but inaccessible to foes.

A multi-agent tool is required for
an entire operating system of cyberspace such as a neural network-based
intrusion detection and hybrid multi-agent techniques.

One distinguishes application of
intelligent agent is agent communication language.

2.3.
Expert System

An expert system is most
commonly used AI tool. This system is used to get inquiries from system or
clients to discover the answers. It supports direct decision support. Such as
it is used in finance, medical diagnose and cyberspace.
An expert system is used for small as well large and complex problems like in hybrid
system.

The expert system consists
of large knowledge, it stores all
information regarding a specific application. Expert system shell (ESS) is used
to support the adding of knowledge in knowledge base expert system, it can be
extended with the program to cooperate
the client as well as another program
that may be utilized in the hybrid expert system.
ESS is empty knowledge base.

Hence, to make an expert system, first select an expert system shell,
second it gets knowledge about and filling the knowledge base with knowledge.
The second step is more complex and time-consuming.

An Expert system is used is cyber defense. It
determines the safety efforts and helps how to use ideally in resources that
are limited in quantity.it is used in network intrusion detection which is knowledge base. In short, the expert system is used to convert the system
knowledge into programming language code. For example,
CD expert system is used for security planning.

2.4.
Search

The method is applied to
resolve the complicated problems where there no other methods are applicable. People used it constantly in
their everyday life without knowing it. General algorithm of search is used to
search the problem, some of it is able to
check the problem and provide a solution
another only estimate the troubles.

           If additional knowledge adds to
the search algorithm than drastically improve the search. Search is almost used
in every intelligible program and it increases
the efficiency of the program. Many search application used in the AI program
to search the problem, for example, dynamic programming is applied to detect
the optimized security problem, it is hidden
from the system, it is invisible in AI applications. Such as alpha-beta search, search on trees, minimum
search, and random search and so on.

          The ??-search is developed to use for
computer chess .divide and conquer is used in complex problems especially in that application where choose the best action.
It is used to estimate the minimum and maximum possibilities. This enables
ignore many of the options and speeds up the search.

2.5.
Learning

    Learning is an extending knowledge system by
arranging or extending the knowledge base. This is a significant problem of the
Artificial Intelligence that is under consideration. Machine learning consists of a computational
method to add new knowledge, new skills and an advanced way to keep and organize the existing knowledge.

      Learning method contains two types of
method i.e. supervised learning and unsupervised learning. This is useful when
multiple types of data are present. This
is commonly used in cyber defense where
abundant data exists. Data Mining is specifically elaborate for
unsupervised learning in artificial intelligence. Unsupervised is utilitarian
for neural nets, in particular, of autonomous maps.

        Parallel algorithm method is a learning
method that executes on hardware. Genetic
algorithms and ANNs help in representing
these strategies. For example, Genetic algorithm and fuzzy logic are applied to
observe intrusions.    

      In short, applications of learning are machine learning, supervised and
unsupervised learning, malware detection, intrusion detection and for self-
organized maps.

      Machine learning is enough intelligent system which is applied for
pattern recognition.

2.6.
Constraint Solving

 Constraint satisfaction method is applied in
AI to discover solutions to those
problems that are introduced by a set of constraint on the solution e.g.
logical statements, tables, equations, inequalities etc.

    A constraint solution is consist of a collection of tuples (ordered pair, row) that
meet all restrictions. There are a lot of problems exist that have different
constraint solution because solution
depends on the character of constraints. Such as constraints on finite sets, functional
constraints, rational trees etc.

     In abstract level, almost every problem is
represented as a constraint solving problem. Constraint satisfaction method is
used in decision making and situation analysis in AI.

 

 

 

 

           TABLE (I): APPLICATION OF AI METHODS

AI METHODS

                      Applications

ANN(Artificial Neural Nets)

Defence against DDOS
For Forensic investigation
For intrusion detection
Very high speed of reaction
Worm detection

Intelligent Agent

Mobility
Rapid response
ACL
Defence against DOS
Reactive

Expert system

the knowledge base
for decision making
for intrusion detection and prevention

Search

for decision making
for searching algorithm
the knowledge base

Learning

for malware detection
for intrusion detection
for machine learning
for supervised learning
for autonomous maps

Constraint solving

for constraint problem
for quick decision determining
for situation examine

 

3.     
DATA MINING

 

Data
Mining technique is
applied to observe the intrusions by recognizing
the patterns of program and user activity. Association, prediction, clustering,
classification, and sequence patterns are
data mining techniques.

 

3.1.
Association

        Association rules in data mining are a conditional statement that exposes the connection among seemingly
unconnected figures and characters in RDBMS for example if a person buys a kg sugar, he is 75% likely to purchase
milk.

3.2.
Classification

 Classification in data mining is a method to assign a group of items to specific
target classes. The function of this method is to estimate the aimed class for
each instance in the data. E.g.

A classification
model used to identify the vulnerabilities in the Nessus as low, medium, high
and critical. Classification is separate
and does not imply the order. It classifies the predefined data in multiple
items of the same quality.

3.3.
Clustering

Same quality of
objects are in one class is called a cluster.
A process to collect the same quality of data in a class is a cluster. The big benefit of the cluster method
is to distinguish between different groups and also objects of different
quality.

3.4.
Prediction

Prediction is Data
Mining method which estimates a persistent value function and sequence value
function.it also predicts the relationship between dependent and independent
variables. For example data analysis task in data
mining.

3.5.
Sequential Patterns

It is data mining technique to recognize statistical
relevant patterns between data, such as consider a sequence database to
represent the client’s purchases from the general store.

 

TABLE (II). FUNCTIONS OF DATA MINING TECHNIQUES

DM Techniques

                            Function

Association

Method that discovers the relationship between an item
with respect to another

Classification

Method to classify
the items into the classes and categories.
It is separate and
do not imply in order
It is used for
mathematical techniques such as decision trees, linear programming, and statistics.

Clustering

Used to collect the
same quality object in a group

Prediction

Predict the
relationship between dependent and independent variables
Predict the
relationship between continuous and order value function

Sequence
Patterns

Identify the
similar pattern in data transaction after a specific time order

         

4.     
COMPUTATIONAL
INTELLIGENT SYSTEM

 

The Computational intelligent system usually includes Fuzzy Logic, Evolutionary Computation, Intelligent Agent
Systems, Neural Networks, Cellular Automata, Artificial Immune Systems models.
These techniques allow efficient decision making. The artificial immune system model is taken from the immune system. The
biological immune system is natural barricade system which produces defense-averse
to many diseases. Artificial neural network, genetic algorithms are important
techniques of the artificial immune system
(AIS) model.

4.1.
Artificial Immune System

The
artificial immune system is
invented after inspired by the natural immune system.(HIS) the human immune system is natural defense system against diseases.it is very complex system and
comprises of many dendritic cells T cells, B cells. D cells gain the
information about antigen and dead cells. 
T cells are built in bone marrow
and remove infectious cells present in the blood. B cells are white cell and
produce antibodies.

        Today the artificial immune system is
used in intrusion detection system, system optimization and in data
classification.it is also comprised of dendritic cells. Nowadays, a new
security-crime interest cache poisoning (ICP) attack is introduced into the network layer which destroys the routing packets. Both dendritic
cells and directed diffusion responsible for the detection of anomalous behavior
of the junction, also recognize the
antigens. Direct diffusion responsible for two packets and two tables
consequently interest packet and data packet, interest data, and cache data.

Artificial Immune
system better the detection process as it detects
many anomalies in a network such as DOS,
DDOS, R2L, U2R and probing.it also detect the MAC layer gene and routing layer
security attack. The architecture of IDS using AIS.

 

                                 

                                          Fig.1:
Architecture of IDS using AIS

 

4.2.
Artificial Neural Nets

 

 Artificial
neural nets are invented based on the human nervous system (HIS). HIS composed
of neurons that are interconnected with each other.it is responsible for Defence against
DDOS, for forensic investigation, for intrusion recognition,
high speed of appropriate respond and decision making.

 

                           

                                   Fig.2: General Architecture of
neuron

 

4.3.
Association

Genetic algorithm
(GA) is introduced based on human natural selection, evolutionary theory and
mainly on genetic inheritance. A genetic
algorithm is used to solve the complicated problems.it is responsible
for robust, adaptive and optimal solutions for many complicated problems.

         A
genetic algorithm is used for intrusion detection in network security
(NS).It is also applied for classification of security attack.

 

 

                                 

 

                            Fig.3: General
Architecture of Genetic Algorithm

 

 

TABLE (III). USES OF COMPUTATIONAL INTELLIGENCE SYSTEM
APPLICATION

Computational   intelligence system application

                                                                                                                                                  
Uses of  Computational   intelligence system application

Artificial immune system

Intrusion detection
Data classification
System optimization
Detection of R2L,
u2R
MAC layer gene and
routing layer genetic attack

Artificial Neural Nets

Defence against DDOS
For Forensic investigation
For intrusion detection
Very high speed of reaction
Worm detection

Genetic Algorithm

For optimal
solution
For adaptive and
robust solution
For intrusion
recognition
For classification
of security attack

 

5.     
INTRUSION
DETECTION SYSTEM

Intrusion
detection is the process of monitor the traffic in the network and monitor the
strange activities and alert the system as well as a network administrator. There are three groups of IDS first is
network based and second host-based and
third is a honeypot.  There two types of IDS. There are two types
of IDS. Anomaly and misuse detection.

5.1.
Network-based

A system that recognizes the intrusion
after monitoring the traffic in the network devices. For example Network
interface card (NIC).

5.2.
Host-based

It monitors the files and process
activities that associate with a software environment related to a specific
host. For example, blocking IDS that
relate the Host-based IDS with modified
firewall rules.

5.3.
Honeypot

It is introduced to trap the intruder, it traces down the location of the
intruder and gives a response to the attack .it work on the network
base sensor.

TYPES OF IDS

There two types of
IDS anomaly and misuse detection

5.4.
Anomaly Detection

It is the abnormal behavior of the
system. For example system calls etc.

5.5.
Misuse Detection

The method to penetrate a system. These penetrations
are signature and pattern. These penetrations are static and set of sequence of
action. The system responds differently depending on the penetrations.

 

6.                 
ABBREVIATION AND
ACRONYMS

 

A.    
 (AI) abbreviate as Artificial Intelligence: AI
is an electronic machine that is enough intelligent to behave like the human
beings.

B.    
(DM)
abbreviate as Data mining: Data mining
technique is applied to observe the intrusions by recognizing the patterns of program and user activity.

C.    
(CDS)
abbreviate as Cyber Defense system: Cyber Defense system able to detect many of
the cyber-attack and alerts the system.

D.    
(IDS)
abbreviate as Intrusion Detection System: Intrusion detection (ID) is the
operation of monitor the traffic in the network and monitor the strange
activities and alert the system as well as a network
administrator.

E.     
(CIS)
abbreviate as Computational Intelligence system: CIS allows efficient decision
making.

F.     
(ML)
abbreviate as Machine learning:       
Learning is an extending knowledge system by arranging or extending the
knowledge base.

G.    
(ES)Expert
system: An expert system is most commonly
used AI tool. This system is used to get inquiries from system or clients to
discover the answers.

H.    
(IA)
abbreviate as intelligent agents: Intelligent agents are computer generated
forces that show respond when an unexpected event occurs.

I.       
(AIS)
abbreviate as an Artificial immune system:
The artificial immune system is invented
after inspired by the natural immune system.(HIS) the human immune system is natural defense system against
diseases.

J.      
(ANN)
abbreviate as an artificial neural network:       Artificial Neural Net is introduced by
inspiring the natural biological nervous system.

K.    
(GA)
abbreviate as Genetic algorithms:       
Genetic algorithm (GA) is introduced based on human natural selection,
evolutionary theory and mainly on genetic inheritance. A genetic algorithm is used to solve the complicated problems.

L.     
(IPS)
abbreviate as intrusion prevention system:

Intrusion prevention (IP) is the
procedure of observing the traffic in the
network, used to identify the threats and respond it quickly.

 

7.     
FUTURE WORK AND
CONCLUSION

In this paper present the defense against sophistication attack.
Application of AI used to increase the efficiency of the cyber defense system. This application monitors the
strange activity in the network, worm detection in the computer and alerts the system and administrator that some
unwanted things occur. Combine the use of the different techniques of AI, DM,
IDPS, and Computational intelligent system in the security management system to
improve the security defense against security threats and intrusions. Some AI
and DM techniques applied in the cyber defense system to remove the immediate
cyber defense problems that require more
intelligent solutions that are present. In the future,
some more of the applications of AI can be used for decision making and
furthermore for the cyber defense system.
 

8.      ACKNOWLEDGMENT

Sadaf Safdar thanks, DR. Sheraz
Ahmad Malik and DR. AWAIS for their helping in writing the paper and also
special thanks, DR. Sheraz for reviewing my paper and encourage me to submit
it. I thank my co-authors for their contribution. Lastly special thanks to the
institute GCUF which supported us.