Evasion Streamline Intruders Using Graph Based Attacker model Analysis and Counter measures In Cloud Environment

International Journal of Computer Applications Technology and Research
Volume 3– Issue 11, 737 - 744, 2014
Evasion Streamline Intruders Using Graph Based
Attacker model Analysis and Counter measures In Cloud
Environment
D.Usha Sree
Chiranjeevi Reddy Institute of Technology
Anantapuramu-51500,
Andhra Pradesh. India
S. Sravani
Chiranjeevi Reddy Institute of Technology
Anantapuramu-51500,
Andhra Pradesh.India
Abstract: Network Intrusion detection and Countermeasure Election in virtual network systems (NICE) are used to establish a
defense-in-depth intrusion detection framework. For better attack detection, NICE incorporates attack graph analytical procedures into
the intrusion detection processes. We must note that the design of NICE does not intend to improve any of the existing intrusion
detection algorithms; indeed, NICE employs a reconfigurable virtual networking approach to detect and counter the attempts to
compromise VMs, thus preventing zombie VMs. NICE includes two main phases: deploy a lightweight mirroring-based network
intrusion detection agent (NICE-A) on each cloud server to capture and analyze cloud traffic. A NICE-A periodically scans the virtual
system vulnerabilities within a cloud server to establish Scenario Attack Graph (SAGs), and then based on the severity of identified
vulnerability toward the collaborative attack goals, NICE will decide whether or not to put a VM in network inspection state. Once a
VM enters inspection state, Deep Packet Inspection (DPI) is applied, and/or virtual network reconfigurations can be deployed to the
inspecting VM to make the potential attack behaviors prominent.
Keywords: NICE, Compromised Machines, spam zombies, Compromised Machine detection Algorithms Scenario Attack
Grapg(SAGs)
1. INTRODUCTION
RECENT studies have shown that users migrating to the
cloud consider security as the most important factor. A recent
Cloud Security Alliance (CSA) [1] .Survey shows that among
all security issues, abuse and nefarious use of cloud
computing [2] is considered as the top security threat, in
which attackers can exploit vulnerabilities in clouds and
utilize cloud system resources to deploy attacks. In traditional
data centers, where system administrators have full control
over the host machines, vulnerabilities can be detected and
patched by the system administrator in a centralized manner.
However, patching known security [3] holes in cloud data
centers, where cloud users usually have the privilege to
control software installed on their managed VMs, may not
work effectively and can violate the service level agreement
(SLA). Furthermore, cloud users can install vulnerable
software on their VMs, which essentially contributes to
loopholes in cloud security [4]. The challenge is to establish
an effective vulnerability/attack detection and response
system for accurately identifying attacks and minimizing the
impact of security breach to cloud users. Addressed that
protecting ―Business continuity and services availability‖
from service outages is one of the top concerns in cloud
computing systems.
1.1 Motivation
NICE significantly advances the current network IDS/
IPS solutions by employing programmable virtual networking
approach that allows the system to construct a dynamic
reconfigurable IDS system. By using software switching
techniques, NICE constructs a mirroring-based traffic
capturing framework to minimize the interference on users’
traffic compared to traditional bump-in-the-wire (i.e., proxy-based)
IDS/IPS. The programmable virtual networking
architecture of NICE enables the cloud to establish inspection
and quarantine modes for suspicious VMs according to their
current vulnerability state in the current SAG. Based on the
collective behavior of VMs in the SAG, NICE can decide
appropriate actions, for example, DPI or traffic filtering, on
the suspicious VMs. Using this approach, NICE does not need
to block traffic flows of a suspicious VM in its early attack
stage.
1.2 Definitions
NICE is a new multiphase distributed network intrusion
detection and prevention framework in a virtual
networking environment that captures and inspects
suspicious cloud traffic without interrupting users’
applications and cloud services.
NICE incorporates a software switching solution to
quarantine and inspect suspicious VMs for further
investigation and protection. Through programmable
network approaches, NICE can improve the attack
detection probability and improve the resiliency to VM
exploitation attack without interrupting existing normal
cloud services.
NICE employs a novel attack graph approach for attack
detection and prevention by correlating attack behavior
and also suggests effective countermeasures.
NICE optimizes the implementation on cloud servers to
minimize resource consumption. Our study shows that
NICE consumes less computational overhead compared to
proxy-based network intrusion detection solutions.
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Volume 3– Issue 11, 737 - 744, 2014
Figure 1.1: Architecture of intruders
2. PROBLEM STATEMENT
2.1 Existing System
Every day in data repositories many number of
knowledgeable people are update the data. Data is
increases here. Already existing data it can add in two or
more number of databases. These kinds of data
repositories are come under dirty repositories. Any user it
can forward the query, extract and display the results here.
Extraction results are contains useless data. Query shows
much number of problems like high response amount of
time, availability, quality assurance and security. Websites
are not providing any useful services in extraction. These
services are showing the problems in performance, quality
and operational cost.
Previous existing system applies the data integration, data
cleaning under record linkage and record matching. In
record matching time and record linkage any duplicates
are present removed here. Next previous approach near
duplicate detection also remove some duplicates of data.
These approaches are not gives any efficient solution in
implementation. It cannot provide high quality data.
2.2 Proposed System
A recent Cloud Security Alliance (CSA) survey shows
that among all security issues, abuse and nefarious use of
cloud computing is considered as the top security threat,
in which attackers can exploit vulnerabilities in clouds
and utilize cloud system resources to deploy attacks. In
traditional data centers, where system administrators have
full control over the host machines, vulnerabilities can be
detected and patched by the system administrator in a
centralized manner. However, patching known security
holes in cloud data centers, where cloud users usually
have the privilege to control software installed on their
managed VMs, may not work effectively and can violate
the service level agreement (SLA). Furthermore, cloud
users can install vulnerable software on their VMs, which
essentially contributes to loopholes in cloud security.
In a cloud system, where the infrastructure is shared by
potentially millions of users, abuse and nefarious use of
the shared infrastructure benefits attackers to exploit
vulnerabilities of the cloud and use its resource to deploy
attacks [5] in more efficient ways. Such attacks are more
effective in the cloud environment because cloud users
usually share computing resources, e.g., being connected
through the same switch, sharing with the same data
storage and file systems, even with potential attackers.
The similar setup for VMs in the cloud, e.g., virtualization
techniques, VM OS, installed vulnerable software,
networking, and so on, attracts attackers to compromise
multiple VMs.
The evaluation is done by assigning to an individual a value
that measures how suitable that individual is to the proposed
problem. In our GP experimental environment, individuals are
evaluated on how well they learn to predict good answers to a
given problem, using the set of functions and terminals
available. The resulting value is also called raw fitness and the
evaluation functions are called fitness functions. Notice that
after the evaluation step, each solution has a fitness value that
measures how good or bad it is to the given problem. Thus, by
using this value, it is possible to select which individuals
should be in the next generation. Strategies for this selection
may involve very simple or complex techniques, varying from
just selecting the best n individuals to randomly selecting the
individuals proportionally to their fitness.
3. METHODOLOGY
3.1 Cloud Components
A Cloud system consists of 3 major components such as
clients, datacenter, and distributed servers. Each element has a
definite purpose and plays a specific role.
Figure 3.1: Cloud components
Clients:
End users interact with the clients to manage information
related to the cloud. Clients generally fall into three categories
as given in:
 Mobile: Windows Mobile Smartphone,
smartphones, like a Blackberry, or an iPhone.
 Thin: They don’t do any computation work. They
only display the information. Servers do all the
works for them. Thin clients don’t have any internal
memory.
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Volume 3– Issue 11, 737 - 744, 2014
 Thick: These use different browsers like IE or
Mozilla Firefox or Google Chrome to connect to the
Internet cloud. Now-a-days thin clients are more
popular as compared to other clients because of
their low price, security, low consumption of power,
less noise, easily replaceable and repairable etc.
Datacenter.
Datacenter is nothing but a collection of servers hosting
different applications. A end user connects to the datacenter to
subscribe different applications. A datacenter may exist at a
large distance from the clients.
Now-a-days a concept called virtualization is used to install a
software that allow multiple instances of virtual server
applications.
Distributed Servers:
Distributed servers are the parts of a cloud which are present
throughout the Internet hosting different applications. But
while using the application from the cloud, the user will feel
that he is using this application from its own machine.
Services provided by Cloud computing:
Service means different types of applications provided by
different servers across the cloud. It is generally given as ‖as a
service‖. Services in a cloud are of 3 types as given:
 Software as a Service (SaaS)
 Platform as a Service (PaaS)
 Hardware as a Service (HaaS) or Infrastructure as a
Service (IaaS)
Software as a Service (SaaS)
In SaaS, the user uses different software applications from
different servers through the Internet. The user uses the
software as it is without any change and do not need to make
lots of changes or doen’t require integration to other systems.
The provider does all the upgrades and patching while
keeping the infrastructure running.
Figure 3.2: Software as a service
The client will have to pay for the time he uses the software.
The software that does a simple task without any need to
interact with other systems makes it an ideal candidate for
Software as a Service. Customer who isn’t inclined to perform
software development but needs high-powered applications
can also be benefitted from SaaS.
Customer resource management (CRM)
 Video conferencing
 IT service management
 Accounting
 Web analytics
 Web content management
Benefits: The biggest benefit of SaaS is costing less money
than buying the whole application.
The service provider generally offers cheaper and more
reliable applications as compared to the organization. Some
other benefits include (given in): Familiarity with the Internet,
Better marketing, smaller staff, reliability of the Internet, data
Security[6], More bandwidth etc.
Obstacles:
 SaaS isn’t of any help when the organization has a
very specific computational need that doesn’t match
to the SaaS services
 While making the contract with a new vendor, there
may be a problem. Because the old vendor may
charge the moving fee. Thus it will increase the
unnecessary costs.
 SaaS faces challenges from the availability of
cheaper hardware’s and open source applications.
Platform as a Service (PaaS):
PaaS provides all the resources that are required for building
applications and services completely from the Internet,
without downloading or installing a software.
PaaS services are software design, development, testing,
deployment, and hosting. Other services can be team
collaboration, database integration, web service integration,
data security, storage and versioning etc.
Figure 3.3: Platform as a Service
Downfall:
 Lack of portability among different providers.
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Volume 3– Issue 11, 737 - 744, 2014
 if the service provider is out of business, the user’s
applications, data will be lost.
Hardware as a Service (HaaS):
It is also known as Infrastructure as a Service (IaaS). It offers
the hardware as a service to a organization so that it can put
anything into the hardware according to its will [1]. HaaS
allows the user to ―rent‖ resources (taken from [1]) as
 Server space
 Network equipment
 Memory
 CPU cycles
 Storage space
Figure 3.4: Hardware as a service
Cloud computing provides a Service Oriented Architecture
(SOA) and Internet of Services (IoS) type applications,
including fault tolerance, high scalability, availability,
flexibility, reduced information technology overhead for the
user, reduced cost of ownership, on demand services etc.
Central to these issues lies the establishment of an effective
load balancing algorithm.
4. IMPLEMENTATION
Design is concerned with identifying software components
specifying relationships among components. Specifying
software structure and providing blue print for the document
phase. Modularity is one of the desirable properties of large
systems. It implies that the system is divided into several
parts. In such a manner, the interaction between parts is
minimal clearly specified.
During the system design activities, Developers bridge the
gap between the requirements specification, produced during
requirements elicitation and analysis, and the system that is
delivered to the user.
Design is the place where the quality is fostered in
development. Software design is a process through which
requirements are translated into a representation of software.
4.1 Use Case Model
Use case diagrams represent the functionality of the system
from a user point of view. A Use case describes a function
provided by the system that yields a visible result for an actor.
an actor describe any entity that interacts with the system. The
identification of actors and use cases results in the definition
of the boundary of the system, which is , in differentiating the
tasks accomplished by the system and the tasks accomplished
by its environment. The actors outside the boundary of the
system, whereas the use cases are inside the boundary of the
system
A Use case contains all the events that can occur between an
actor and a set of scenarios that explains the interactions as
sequence of happenings.
4.2 Java Programming Language
Each of the preceding buzzwords is explained in The Java
Language Environment , a white paper written by James
Gosling and Henry McGilton.
In the Java programming language, all source code is first
written in plain text files ending with the .java extension.
Those source files are then compiled into .class files by the
javac compiler. A .class file does not contain code that is
native to your processor; it instead contains bytecodes — the
machine language of the Java Virtual Machine1 (Java VM).
The java launcher tool then runs your application with an
instance of the Java Virtual Machine.
Figure 4.1: java software development process
An overview of the software development process.
Because the Java VM is available on many different operating
systems, the same .class files are capable of running on
Microsoft Windows, the Solaris Operating System (Solaris
OS), Linux, or Mac OS. Some virtual machines, such as the
Java HotSpot virtual machine, perform additional steps at
runtime to give your application a performance boost. This
include various tasks such as finding performance bottlenecks
and recompiling (to native code) frequently used sections of
code
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Volume 3– Issue 11, 737 - 744, 2014
Figure 4.2: java compiler
Code Snippets (Logics) & Analysis
Logics
When using GP to solve a problem, there are some
basic requirements that must be fulfilled, which are based on
the data structure used to represent the solution. In our case,
we have chosen a tree-based GP representation for the
deduplication function, since it is a natural representation for
this type of function. These requirements are the following:
1. All possible solutions to the problem must be represented
by a tree, no matter its size.
2. The evolutionary operations applied over each individual
tree must, at the end, result into a valid tree.
3. Each individual tree must be automatically evaluated.
For Requirement 1, it is necessary to take into consideration
the kind of solution we intend to find. In the record
reduplication problem, we look for a function that combines
pieces of evidence.
In our approach, each piece of evidence (or simply
―evidence‖) E is a pair <attribute; similarity function> that
represents the use of a specific similarity function over the
values of a specific attribute found in the data being analyzed.
For example, if we want to reduplication a database table with
four attributes (e.g., forename, surname, address, and postal
code) using a specific similarity function.
To model such functions as a GP tree, each evidence is
represented by a leaf in the tree. Each leaf (the similarity
between two attributes) generates a normalized real number
value (between 0.0 and 1.0). A leaf can also be a random
number between 1.0 and 9.0, which is chosen at the moment
that each tree is. Such leaves (random numbers) are used to
allow the evolutionary process to find the most adequate
weights for each evidence, when necessary. The internal
nodes represent operations that are applied to the leaves. In
our model, they are simple mathematical functions (e.g. *, % ,
/ ) that manipulate the leaf values.
To enforce Requirement 2, the trees are handled by sub tree
atomic operations to avoid situations that could affect the
integrity of the overall function, resulting an invalid tree. For
a valid tree (or a valid function), there cannot be neither a case
where the value of a leaf node is replaced by the value of an
internal node nor one where the value of an internal node is
replaced by the value of a leaf node.
According to Requirement 3, all trees generated during a GP
evolutionary process is tested against pre evaluated data
repositories where the replicas have been previously
identified. This makes feasible to perform the whole process
automatically, since it is possible to evaluate how the trees
perform in the task of recognizing record pairs that are true
replicas.
The tree input is a set of evidence instances, extracted from
the data being handled, and its output is a real number value.
This value is compared against a replica identification
boundary value as follows: if it is above the boundary, the
records are considered replicas, otherwise, the records are
considered distinct entries. It is important to notice that this
classification enables further analysis, especially regarding the
transitive properties of the replicas.
4 This can improve the efficiency of clustering algorithms,
since it provides not only an estimation of the similarity
between the records being processed, but also a judgment of
whether they are replicas or not.
After doing these comparisons for all candidate record pairs,
the total number of correct and incorrect identified replicas is
computed. This information is then used by the most
important configuration component in our approach: the
fitness function.
5. NICE OUTPUTS
Figure 5.1:NICE
Login into NICE web-page
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Volume 3– Issue 11, 737 - 744, 2014
Figure 5.2:login page NICE
Figure 5.3:Registration page NICE
Figure 5.4:Upload file
Figure 5.5:Downloading file
Figure 5.6: Intruder found the NICE
Figure 5.7: Detection and Prevention process of file in cloud
services
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Volume 3– Issue 11, 737 - 744, 2014
Figure 5.8: Output of cloud servers
Figure 5.9: Benefit RIO chart
6. CONCLUSION
In this paper, we presented NICE, which is proposed to detect
and mitigate collaborative attacks [9] in the cloud virtual
networking environment. NICE utilizes the attack graph
model to conduct attack detection and prediction. The
proposed solution investigates how to use the
programmability of software switches-based solutions to
improve the detection accuracy and defeat victim exploitation
phases of collaborative attacks. The system performance
evaluation demonstrates the feasibility of NICE and shows
that the proposed solution can significantly reduce the risk of
the cloud system from being exploited and abused by internal
and external attackers.
NICE only investigates the network IDS [7] approach to
counter zombie explorative attacks. To improve the detection
accuracy, host-based IDS solutions are needed to be
incorporated and to cover the whole spectrum of IDS in the
cloud system.
7. FUTURE ENHANCEMENT
This should be investigated in the future work.
Additionally, as indicated in the paper, we will investigate the
scalability of the proposed NICE solution by investigating the
decentralized network control and attack analysis model based
on current study.
8. ACKNOWLWDGEMENTS
We are grateful to express sincere thanks to our faculties who
gave support and special thanks to our department for
providing facilities that were offered to us for carrying out this
project.
REFERENCES
[1] Coud SercurityAlliance,―Top Threats to Cloud Computing
v1.0,‖https://cloudsecurityalliance.org/topthreats/csathreats.
v1.0.pdf, Mar. 2010.
[2] M. Armbrust, A. Fox, R. Griffith, A.D. Joseph, R. Katz,
A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica,
and M. Zaharia, ―A View of Cloud Computing,‖ ACM
Comm., vol. 53, no. 4, pp. 50-58, Apr. 2010.
[3] B. Joshi, A. Vijayan, and B. Joshi, ―Securing Cloud
Computing Environment Against DDoS Attacks,‖ Proc.
IEEE Int’l Conf. Computer Comm. and Informatics (ICCCI
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[4] H. Takabi, J.B. Joshi, and G. Ahn, ―Security and
PrivacyChallenges in Cloud Computing Environments,‖ IEEE
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[5] ―Open vSwitch Project,‖ http://openvswitch.org, May
2012.
[6] Z. Duan, P. Chen, F. Sanchez, Y. Dong, M. Stephenson,
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[7] G. Gu, P. Porras, V. Yegneswaran, M. Fong, and W. Lee,
―BotHunter: Detecting Malware Infection through IDS-driven
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[8] G. Gu, J. Zhang, and W. Lee, ―BotSniffer: Detecting
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Proc. 15thAnn. Network and Distributed Sytem Security
Symp. (NDSS’08), Feb. 2008.
[9] O. Sheyner, J. Haines, S. Jha, R. Lippmann, and J.M.
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[10] ―NuSMV: A New Symbolic Model Checker,‖
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Evasion Streamline Intruders Using Graph Based Attacker model Analysis and Counter measures In Cloud Environment

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