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M.S.HajaMoideen et al. Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 12( Part 6), December 2014, pp.259-264
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Intrusion Detection and Countermeasure in Virtual Network
Systems Using NICE Technique
M.S.Haja Moideen,N.Chandru,G.SathishKumar,G.VishnuPrabu,
R.Ramachandiran
P.G Student, Department Of Master Computer Application, Sri Manakula Vinayagar Engineering
College, Pondicherry-605 107
P.G Student, Department Of Master Computer Application, Sri Manakula Vinayagar Engineering
College, Pondicherry-605 107
P.G Student, Department Of Master Computer Application, Sri ManakulaVinayagar Engineering
College, Pondicherry-605 107
P.G Student, Department Of Master Computer Application, Sri ManakulaVinayagar Engineering
College, Pondicherry-605 107
Assistant Professor, Department Of Master Computer Application, Sri Manakula Vinayagar
Engineering College, Pondicherry-605 107
ABSTRACT
The cloud computing has increased in many organizations. It provides many benefits in terms of low cost and
accessibility of data. Ensuring the security of cloud computing is a major factor in the cloud computing
environment, as users often store sensitive information with cloud storage providers but these providers may be
untrusted. In this project we propose anIntrusion Detection and Countermeasure in Virtual Network Systems
mechanism called NICE to prevent vulnerable virtual machines from being compromised in the cloud. NICE
detects and mitigates collaborative attacks in the cloud virtual networking environment. 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.
Key Terms—Network security, cloud computing, intrusion detection
I. Introduction
Wireless Sensor Networks (WSNs) can be
defined as a self-configured and infrastructure less
wireless networks to monitor physical or
environmental conditions, such as temperature,
sound, vibration, pressure, motion or pollutants and
to cooperatively pass their data through the network
to a main location or sink where the data can be
observed and analyzed. A sink or base station acts
like an interface between users and the network. One
can retrieve required information from the network
by injecting queries and gathering results from the
sink. Typically a wireless sensor network contains
hundreds of thousands of sensor nodes. The sensor
nodes can communicate among themselves using
radio signals. A wireless sensor node is equipped
with sensing and computing devices, radio
transceivers and power components. The individual
nodes in a wireless sensor network (WSN) are
inherently resource constrained: they have limited
processing speed, storage capacity, and
communication bandwidth. After the sensor nodes
are deployed, they are responsible for self-
organizing an appropriate network infrastructure
often with multi-hop communication with them.
Then the on board sensors start collecting
information of interest. Wireless sensor devices also
respond to queries sent from a ―control site‖ to
perform specific instructions or provide sensing
samples. The working mode of the sensor nodes may
be either continuous or event driven.
Global Positioning System (GPS) and local
positioning algorithms can be used to obtain location
and positioning information. Wireless sensor devices
can be equipped with actuators to ―act‖ upon certain
conditions. These networks are sometimes more
specifically referred as Wireless Sensor and Actuator
Networks as described in (Akkaya et al.,
2005).Wireless sensor networks (WSNs) enable new
applications and require non-conventional paradigms
for protocol design due to several constraints. Owing
to the requirement for low device complexity
together with low energy consumption (i.e. long
network lifetime), a proper balance between
communication and signal/data processing
capabilities must be found. This motivates a huge
RESEARCH ARTICLE OPEN ACCESS
M.S.HajaMoideen et al. Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 12( Part 6), December 2014, pp.259-264
www.ijera.com 260|P a g e
effort in research activities, standardization process,
and industrial investments on this field since the last
decade (Chiara et. al. 2009). At present time, most of
the research on WSNs has concentrated on the
design of energy- and computationally efficient
algorithms and protocols, and the application
domain has been restricted to simple data-oriented
monitoring and reporting applications (Labrador et.
al.2009). The authors in (Chen et al., 2011) propose
a Cable Mode Transition (CMT) algorithm, which
determines the minimal number of active sensors to
maintain K-coverage of a terrain as well as K-
connectivity of the network. Specifically, it allocates
periods of inactivity forcable sensors without
affecting the coverage and connectivity requirements
of the network based only on local information. In
(Cheng et al., 2011), a delay-aware data collection
network structure for wireless sensor networks is
proposed. The objective of the proposed network
structure is to minimize delays in the data collection
processes of wireless sensor networks which extends
the lifetime of the network. In (Matin et al., 2011),
the authors have considered relay nodes to mitigate
the network geometric deficiencies and used Particle
Swarm Optimization (PSO) based algorithms to
locate the optimal sink location with res pectto those
relay nodes to overcome the lifetime challenge.
Energy efficient communication has also been
addressed in (Paul et al., 2011; Fabbri et al. 2009). In
(Paul et al., 2011), the authors proposed a
geometrical solution for locating the optimum sink
placement for maximizing the network lifetime.
Most of the time, the research on wireless sensor
networks have considered homogeneous sensor
nodes. But nowadays researchers have focused on
heterogeneous sensor networks where the sensor
nodes are unlike to each other in terms of their
energy. In (Han et al., 2010), the authors addresses
the problem of deploying relay nodes to provide
fault tolerance with higher network connectivity in
heterogeneous wireless sensor networks, where
sensor nodes possess different transmission radii.
New network architectures with heterogeneous
devices and the recent advancement in this
technology eliminate the current limitations and
expand the spectrum of possible applications for
WSN sconsiderably and all these are changing very
rapidly
Figure :1.A typical Wireless Sensor Network
II. Design issues of a wireless sensor
network
There are a lot of challenges placed by the
deployment of sensor networks which are a superset
of those found in wireless ad hoc networks. Sensor
nodes communicate over wireless, lossy lines with
no infrastructure. An additional challenge is related
to the limited,usually non-renewable energy supply
of the sensor nodes. In order to maximize the life
timeof the network, the protocols need to be
designed from the beginning with the objective of
efficient management of the energy resources
(Akyildiz et al., 2002). Wireless Sensor Network
Design issues are mentioned in (Akkaya et al.,
2005), (Akyildizet al., 2002),(SensorSim; Tossim,
Younis et al., 2004), (Pan et al., 2003) and different
possible platformsfor simulation and testing of
routing protocols for WSNs are discussed in ( NS-2,
Zeng et al.,1998, SensorSim, Tossiim ). Let us now
discuss the individual design issues in greater detail.
Fault Tolerance: Sensor nodes are vulnerable and
frequently deployed in dangerous environment.
Sensor Network Topology: Although WSNs
have evolved in many aspects, they continue to be
networks with constrained resources in terms of
energy, computing power, memory, and
communications capabilities. Of these constraints,
energy consumption is of paramount importance,
which is demonstrated by the large number of
algorithms, techniques, and protocols that have been
developed to save energy, and thereby extend the
lifetime of the network. Topology Maintenance is
one of the most important issues researched to
reduce energy consumption in wireless sensor
networks. Transmission Media: The communication
between the nodes is normally implemented using
radio communication over the popular ISM bands.
However, some sensor networks use optical or
infrared communication, with the latter having the
advantage of being robust and virtually interference
free. Power Consumption: As we have already seen,
many of the challenges of sensor networks revolve
around the limited power resources. The size of the
nodes limits the size of the battery. The software and
M.S.HajaMoideen et al. Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 12( Part 6), December 2014, pp.259-264
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hardware design needs to carefully consider the
issues of efficient energy use.
2.1 Communication structure of a WSN
The sensor nodes are usually scattered in a
sensor field as shown in Fig. Each of these scattered
sensor nodes has the capabilities to collect data and
route data back to the sink and the end users. Data
are routed back to the end user by a multi-hop
infrastructure-less architecture through the sink as
shown in Fig. 1. The sink may communicate with
the task manager node via Internet or Satellite.
Figure: 2 .Wireless Sensor Network protocol stack
III. Network Intrusion Detection System
In computer security, a Network Intrusion
Detection System (NIDS) is an intrusion detection
systemthat attempts to discover unauthorized access
to a computer networkby analyzing trafficon the
network for signs of malicious activity Application
protocol-based intrusion detection system
An application protocol-based intrusion
detection system (APIDS) is an intrusion detection
systemthat focuses its monitoring and analysis on a
specific application protocolor protocols in use by
the computing system.
3.1 Overview
An APIDS will monitor the dynamic behavior
and stateof the protocol and will typically consist of
a system or agent that would typically sit between a
process, or group of servers, monitoringand
analyzing the application protocol between two
connected devices.
A typical place for an APIDS would be between
a web server and the database management system,
monitoring the SQLprotocol specific to the
middleware/business logicas it interacts with the
database.
3.2 Monitoring Dynamic Behavior
At a basic level an APIDS would look for, and
enforce, the correct (legal) use of the protocol.
However at a more advanced level the APIDS can
learn, be taught or even reduce what is often an
infinite protocol set, to an acceptable understanding
of the subsetof that application protocol that is used
by the application being monitored/protected.
Thus, an APIDS, correctly configured, will
allow an application to be "fingerprinted", thus
should that application be subverted or changed, so
will the fingerprint change.
IV. VIRTUAL NETWORK
COMPUTING
In computing, Virtual Network Computing
(VNC) is a graphical desktop sharing system that
uses the Remote Frame Buffer protocol (RFB) to
remotely control another computer. It transmits the
keyboard and mouse events from one computer to
another, relaying the graphical screen updates back
in the other direction, over a network.
VNC is platform-independent – There are
clients and servers for many GUI-based operating
systems and for Java. Multiple clients may connect
to a VNC server at the same time. Popular uses for
this technology include remote technical support and
accessing files on one's work computer from one's
home computer, or vice versa.
VNC by default uses TCP port 5900+N, where
N is the display number (usually: 0 for a physical
display). Several implementations also start a basic
HTTP server on port 5800+N to provide a VNC
viewer as a Java applet, allowing easy connection
through any Java-enabled web browser. Different
port assignments can be used as long as both client
and server are configured accordingly.
4.1 Security
By default, RFB is not a secure protocol. While
passwords are not sent in plain-text (as in telnet),
cracking could prove successful if both the
encryption key and encoded password are sniffed
from a network. For this reason it is recommended
that a password of at least 8 characters be used. On
the other hand, there is also an 8-character limit on
some versions of VNC; if a password is sent
exceeding 8 characters, the excess characters are
removed and the truncated string is compared to the
password.
UltraVNC supports the use of an open-source
encryption plugin which encrypts the entire VNC
session including password authentication and data
transfer. It also allows authentication to be
performed based on NTLM and Active Directory
user accounts. However, use of such encryption
plugins make it incompatible with other VNC
programs.
4.2 Limitations
M.S.HajaMoideen et al. Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 12( Part 6), December 2014, pp.259-264
www.ijera.com 262|P a g e
Unicode is not supported in RFB versions 3.x
and lower so it is impossible to transfer clipboard
text outside the Latin-1 character set.
The VNC protocol is pixel based. Although this
leads to great flexibility (e.g., any type of desktop
can be displayed), it is often less efficient than
solutions that have a better understanding of the
underlying graphic layout like X11 or Windows
Remote Desktop Protocol. Those protocols send
graphic primitives or high level commands in a
simpler form (e.g., "open window"), whereas RFB
just sends the raw pixel data.
V. DESIGN OF MODEL OR
TECHNIQUE OR ALGORITHM
5.1 Alert Correlation
Algorithm 1 presents VM protection model of NICE
consists of a VM profiler, a security indexer, and a
state monitor. We specify security index for all the
VMs in the network depending upon various factors
like connectivity, the number of vulnerabilities
present and their impact scores. The impact score of
a vulnerability, as defined by the CVSS guide [24],
helps to judge the confidentiality, integrity, and
availability impact of the vulnerability being
exploited. Connectivity metric of a VM is decided
by evaluating incoming and outgoing connections.
VM states can be defined as following:
1. Stable. There does not exist any known
vulnerability on the VM.
2. Vulnerable. Presence of one or more
vulnerabilities on a VM, which remains
unexploited.
3. Exploited. At least one vulnerability has been
exploited and the VM is compromised.
4. Zombie. VM is under control of attacker.
5.2 COUNTERMEASURE SELECTION
Algorithm 2 presents how to select the optimal
counter- measure for a given attack scenario. Input
to the algorithm is an alert, attack graph G, and a
pool of countermeasures CM. The algorithm starts
by selecting the node vAlert that corresponds to the
alert generated by a NICE-A. Before selecting the
countermeasure, we count the distance of vAlert to
the target node. If the distance is greater than a
threshold value, we do not perform countermeasure
selection but update the ACG to keep track of alerts
in the system (line 3). For the source node vAlert, all
the reachable nodes (including
thesourcenode)arecollectedintoasetT (line6).Because
the alert is generated only after the attacker has
performed the action, we set the probability of
vAlert to 1 and calculate the new probabilities for all
of its child (downstream) nodes in the set T (lines 7
and 8). Now, for all t 2 T the applicable
countermeasures in CM are selected and new
probabilities are calculated according to the
effectiveness of the selected countermeasures (lines
13 and 14). The change in probability of target node
gives the benefit for the applied counter- measure
using (7). In the next double for-loop, we compute
the Return of Investment (ROI) for each benefit of
the applied countermeasure based on (8). The
countermeasure which when applied on a node gives
the least value of ROI, is regarded as the optimal
countermeasure. Finally, SAG and ACG are also
updated before terminating the algorithm. The
M.S.HajaMoideen et al. Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 12( Part 6), December 2014, pp.259-264
www.ijera.com 263|P a g e
complexity of Algorithm 2 is Ϭ(|V| x |CM|), where
|V| is the number of vulnerabilities and |CM|
represents the number of countermeasures.
VI. Conclusion
In this paper, we presented NICE, which is
proposed to detect and mitigate collaborative attacks
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
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. 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.
REFERENCES
[1] CoudSercurity Alliance, ―Top Threats to
Cloud Computing v1.0,‖
https://cloudsecurityalliance.org/topthreats/c
sathreats. 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
’12), Jan. 2012.
[4] H. Takabi, J.B. Joshi, and G. Ahn, ―Security
and Privacy Challenges in Cloud Computing
Environments,‖ IEEE Security and Privacy,
vol. 8, no. 6, pp. 24-31, Dec. 2010.
[5] ―Open vSwitch Project,‖
http://openvswitch.org, May 2012.
[6] Z. Duan, P. Chen, F. Sanchez, Y. Dong, M.
Stephenson, and J. Barker, ―Detecting Spam
Zombies by Monitoring Outgoing
Messages,‖ IEEE Trans. Dependable and
Secure Computing, vol. 9, no. 2, pp. 198-
210, Apr. 2012.
[7] G. Gu, P. Porras, V. Yegneswaran, M. Fong,
and W. Lee, ―BotHunter: Detecting Malware
Infection through IDS-driven Dialog
Correlation,‖ Proc. 16th USENIX Security
Symp. (SS ’07), pp. 12:1-12:16, Aug. 2007.
[8] G. Gu, J. Zhang, and W. Lee, ―BotSniffer:
Detecting Botnet Command and Control
Channels in Network Traffic,‖ Proc. 15th
Ann. Network and Distributed Sytem
Security Symp. (NDSS ’08), Feb. 2008.
[9] O. Sheyner, J. Haines, S. Jha, R. Lippmann,
and J.M. Wing, ―Automated Generation and
Analysis of Attack Graphs,‖ Proc. IEEE
Symp. Security and Privacy, pp. 273-284,
2002,
[10] ―NuSMV: A New Symbolic Model
Checker,‖ http://afrodite.itc. it:1024/nusmv.
Aug. 2012.
[11] P. Ammann, D. Wijesekera, and S. Kaushik,
―Scalable, graph- based network
vulnerability analysis,‖ Proc. 9th ACM
Conf. Computer and Comm. Security (CCS
’02), pp. 217-224, 2002.
[12] X. Ou, S. Govindavajhala, and A.W. Appel,
―MulVAL: A Logic- Based Network
Security Analyzer,‖ Proc. 14th USENIX
Security Symp., pp. 113-128, 2005.
[13] R. Sadoddin and A. Ghorbani, ―Alert
Correlation Survey: Frame- work and
Techniques,‖ Proc. ACM Int’l Conf.
M.S.HajaMoideen et al. Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 12( Part 6), December 2014, pp.259-264
www.ijera.com 264|P a g e
Privacy, Security and Trust: Bridge the Gap
between PST Technologies and Business
Services (PST ’06), pp. 37:1-37:10, 2006.
[14] L. Wang, A. Liu, and S. Jajodia, ―Using
Attack Graphs for Correlating,
Hypothesizing, and Predicting Intrusion
Alerts,‖ Computer Comm., vol. 29, no. 15,
pp. 2917-2933, Sept. 2006.
[15] S. Roschke, F. Cheng, and C. Meinel, ―A
New Alert Correlation Algorithm Based on
Attack Graph,‖ Proc. Fourth Int’l Conf.
Computational Intelligence in Security for
Information Systems, pp. 58-67, 2011.

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Intrusion Detection and Countermeasure in Virtual Network Systems Using NICE Technique

  • 1. M.S.HajaMoideen et al. Int. Journal of Engineering Research and Applications www.ijera.com ISSN : 2248-9622, Vol. 4, Issue 12( Part 6), December 2014, pp.259-264 www.ijera.com 259|P a g e Intrusion Detection and Countermeasure in Virtual Network Systems Using NICE Technique M.S.Haja Moideen,N.Chandru,G.SathishKumar,G.VishnuPrabu, R.Ramachandiran P.G Student, Department Of Master Computer Application, Sri Manakula Vinayagar Engineering College, Pondicherry-605 107 P.G Student, Department Of Master Computer Application, Sri Manakula Vinayagar Engineering College, Pondicherry-605 107 P.G Student, Department Of Master Computer Application, Sri ManakulaVinayagar Engineering College, Pondicherry-605 107 P.G Student, Department Of Master Computer Application, Sri ManakulaVinayagar Engineering College, Pondicherry-605 107 Assistant Professor, Department Of Master Computer Application, Sri Manakula Vinayagar Engineering College, Pondicherry-605 107 ABSTRACT The cloud computing has increased in many organizations. It provides many benefits in terms of low cost and accessibility of data. Ensuring the security of cloud computing is a major factor in the cloud computing environment, as users often store sensitive information with cloud storage providers but these providers may be untrusted. In this project we propose anIntrusion Detection and Countermeasure in Virtual Network Systems mechanism called NICE to prevent vulnerable virtual machines from being compromised in the cloud. NICE detects and mitigates collaborative attacks in the cloud virtual networking environment. 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. Key Terms—Network security, cloud computing, intrusion detection I. Introduction Wireless Sensor Networks (WSNs) can be defined as a self-configured and infrastructure less wireless networks to monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants and to cooperatively pass their data through the network to a main location or sink where the data can be observed and analyzed. A sink or base station acts like an interface between users and the network. One can retrieve required information from the network by injecting queries and gathering results from the sink. Typically a wireless sensor network contains hundreds of thousands of sensor nodes. The sensor nodes can communicate among themselves using radio signals. A wireless sensor node is equipped with sensing and computing devices, radio transceivers and power components. The individual nodes in a wireless sensor network (WSN) are inherently resource constrained: they have limited processing speed, storage capacity, and communication bandwidth. After the sensor nodes are deployed, they are responsible for self- organizing an appropriate network infrastructure often with multi-hop communication with them. Then the on board sensors start collecting information of interest. Wireless sensor devices also respond to queries sent from a ―control site‖ to perform specific instructions or provide sensing samples. The working mode of the sensor nodes may be either continuous or event driven. Global Positioning System (GPS) and local positioning algorithms can be used to obtain location and positioning information. Wireless sensor devices can be equipped with actuators to ―act‖ upon certain conditions. These networks are sometimes more specifically referred as Wireless Sensor and Actuator Networks as described in (Akkaya et al., 2005).Wireless sensor networks (WSNs) enable new applications and require non-conventional paradigms for protocol design due to several constraints. Owing to the requirement for low device complexity together with low energy consumption (i.e. long network lifetime), a proper balance between communication and signal/data processing capabilities must be found. This motivates a huge RESEARCH ARTICLE OPEN ACCESS
  • 2. M.S.HajaMoideen et al. Int. Journal of Engineering Research and Applications www.ijera.com ISSN : 2248-9622, Vol. 4, Issue 12( Part 6), December 2014, pp.259-264 www.ijera.com 260|P a g e effort in research activities, standardization process, and industrial investments on this field since the last decade (Chiara et. al. 2009). At present time, most of the research on WSNs has concentrated on the design of energy- and computationally efficient algorithms and protocols, and the application domain has been restricted to simple data-oriented monitoring and reporting applications (Labrador et. al.2009). The authors in (Chen et al., 2011) propose a Cable Mode Transition (CMT) algorithm, which determines the minimal number of active sensors to maintain K-coverage of a terrain as well as K- connectivity of the network. Specifically, it allocates periods of inactivity forcable sensors without affecting the coverage and connectivity requirements of the network based only on local information. In (Cheng et al., 2011), a delay-aware data collection network structure for wireless sensor networks is proposed. The objective of the proposed network structure is to minimize delays in the data collection processes of wireless sensor networks which extends the lifetime of the network. In (Matin et al., 2011), the authors have considered relay nodes to mitigate the network geometric deficiencies and used Particle Swarm Optimization (PSO) based algorithms to locate the optimal sink location with res pectto those relay nodes to overcome the lifetime challenge. Energy efficient communication has also been addressed in (Paul et al., 2011; Fabbri et al. 2009). In (Paul et al., 2011), the authors proposed a geometrical solution for locating the optimum sink placement for maximizing the network lifetime. Most of the time, the research on wireless sensor networks have considered homogeneous sensor nodes. But nowadays researchers have focused on heterogeneous sensor networks where the sensor nodes are unlike to each other in terms of their energy. In (Han et al., 2010), the authors addresses the problem of deploying relay nodes to provide fault tolerance with higher network connectivity in heterogeneous wireless sensor networks, where sensor nodes possess different transmission radii. New network architectures with heterogeneous devices and the recent advancement in this technology eliminate the current limitations and expand the spectrum of possible applications for WSN sconsiderably and all these are changing very rapidly Figure :1.A typical Wireless Sensor Network II. Design issues of a wireless sensor network There are a lot of challenges placed by the deployment of sensor networks which are a superset of those found in wireless ad hoc networks. Sensor nodes communicate over wireless, lossy lines with no infrastructure. An additional challenge is related to the limited,usually non-renewable energy supply of the sensor nodes. In order to maximize the life timeof the network, the protocols need to be designed from the beginning with the objective of efficient management of the energy resources (Akyildiz et al., 2002). Wireless Sensor Network Design issues are mentioned in (Akkaya et al., 2005), (Akyildizet al., 2002),(SensorSim; Tossim, Younis et al., 2004), (Pan et al., 2003) and different possible platformsfor simulation and testing of routing protocols for WSNs are discussed in ( NS-2, Zeng et al.,1998, SensorSim, Tossiim ). Let us now discuss the individual design issues in greater detail. Fault Tolerance: Sensor nodes are vulnerable and frequently deployed in dangerous environment. Sensor Network Topology: Although WSNs have evolved in many aspects, they continue to be networks with constrained resources in terms of energy, computing power, memory, and communications capabilities. Of these constraints, energy consumption is of paramount importance, which is demonstrated by the large number of algorithms, techniques, and protocols that have been developed to save energy, and thereby extend the lifetime of the network. Topology Maintenance is one of the most important issues researched to reduce energy consumption in wireless sensor networks. Transmission Media: The communication between the nodes is normally implemented using radio communication over the popular ISM bands. However, some sensor networks use optical or infrared communication, with the latter having the advantage of being robust and virtually interference free. Power Consumption: As we have already seen, many of the challenges of sensor networks revolve around the limited power resources. The size of the nodes limits the size of the battery. The software and
  • 3. M.S.HajaMoideen et al. Int. Journal of Engineering Research and Applications www.ijera.com ISSN : 2248-9622, Vol. 4, Issue 12( Part 6), December 2014, pp.259-264 www.ijera.com 261|P a g e hardware design needs to carefully consider the issues of efficient energy use. 2.1 Communication structure of a WSN The sensor nodes are usually scattered in a sensor field as shown in Fig. Each of these scattered sensor nodes has the capabilities to collect data and route data back to the sink and the end users. Data are routed back to the end user by a multi-hop infrastructure-less architecture through the sink as shown in Fig. 1. The sink may communicate with the task manager node via Internet or Satellite. Figure: 2 .Wireless Sensor Network protocol stack III. Network Intrusion Detection System In computer security, a Network Intrusion Detection System (NIDS) is an intrusion detection systemthat attempts to discover unauthorized access to a computer networkby analyzing trafficon the network for signs of malicious activity Application protocol-based intrusion detection system An application protocol-based intrusion detection system (APIDS) is an intrusion detection systemthat focuses its monitoring and analysis on a specific application protocolor protocols in use by the computing system. 3.1 Overview An APIDS will monitor the dynamic behavior and stateof the protocol and will typically consist of a system or agent that would typically sit between a process, or group of servers, monitoringand analyzing the application protocol between two connected devices. A typical place for an APIDS would be between a web server and the database management system, monitoring the SQLprotocol specific to the middleware/business logicas it interacts with the database. 3.2 Monitoring Dynamic Behavior At a basic level an APIDS would look for, and enforce, the correct (legal) use of the protocol. However at a more advanced level the APIDS can learn, be taught or even reduce what is often an infinite protocol set, to an acceptable understanding of the subsetof that application protocol that is used by the application being monitored/protected. Thus, an APIDS, correctly configured, will allow an application to be "fingerprinted", thus should that application be subverted or changed, so will the fingerprint change. IV. VIRTUAL NETWORK COMPUTING In computing, Virtual Network Computing (VNC) is a graphical desktop sharing system that uses the Remote Frame Buffer protocol (RFB) to remotely control another computer. It transmits the keyboard and mouse events from one computer to another, relaying the graphical screen updates back in the other direction, over a network. VNC is platform-independent – There are clients and servers for many GUI-based operating systems and for Java. Multiple clients may connect to a VNC server at the same time. Popular uses for this technology include remote technical support and accessing files on one's work computer from one's home computer, or vice versa. VNC by default uses TCP port 5900+N, where N is the display number (usually: 0 for a physical display). Several implementations also start a basic HTTP server on port 5800+N to provide a VNC viewer as a Java applet, allowing easy connection through any Java-enabled web browser. Different port assignments can be used as long as both client and server are configured accordingly. 4.1 Security By default, RFB is not a secure protocol. While passwords are not sent in plain-text (as in telnet), cracking could prove successful if both the encryption key and encoded password are sniffed from a network. For this reason it is recommended that a password of at least 8 characters be used. On the other hand, there is also an 8-character limit on some versions of VNC; if a password is sent exceeding 8 characters, the excess characters are removed and the truncated string is compared to the password. UltraVNC supports the use of an open-source encryption plugin which encrypts the entire VNC session including password authentication and data transfer. It also allows authentication to be performed based on NTLM and Active Directory user accounts. However, use of such encryption plugins make it incompatible with other VNC programs. 4.2 Limitations
  • 4. M.S.HajaMoideen et al. Int. Journal of Engineering Research and Applications www.ijera.com ISSN : 2248-9622, Vol. 4, Issue 12( Part 6), December 2014, pp.259-264 www.ijera.com 262|P a g e Unicode is not supported in RFB versions 3.x and lower so it is impossible to transfer clipboard text outside the Latin-1 character set. The VNC protocol is pixel based. Although this leads to great flexibility (e.g., any type of desktop can be displayed), it is often less efficient than solutions that have a better understanding of the underlying graphic layout like X11 or Windows Remote Desktop Protocol. Those protocols send graphic primitives or high level commands in a simpler form (e.g., "open window"), whereas RFB just sends the raw pixel data. V. DESIGN OF MODEL OR TECHNIQUE OR ALGORITHM 5.1 Alert Correlation Algorithm 1 presents VM protection model of NICE consists of a VM profiler, a security indexer, and a state monitor. We specify security index for all the VMs in the network depending upon various factors like connectivity, the number of vulnerabilities present and their impact scores. The impact score of a vulnerability, as defined by the CVSS guide [24], helps to judge the confidentiality, integrity, and availability impact of the vulnerability being exploited. Connectivity metric of a VM is decided by evaluating incoming and outgoing connections. VM states can be defined as following: 1. Stable. There does not exist any known vulnerability on the VM. 2. Vulnerable. Presence of one or more vulnerabilities on a VM, which remains unexploited. 3. Exploited. At least one vulnerability has been exploited and the VM is compromised. 4. Zombie. VM is under control of attacker. 5.2 COUNTERMEASURE SELECTION Algorithm 2 presents how to select the optimal counter- measure for a given attack scenario. Input to the algorithm is an alert, attack graph G, and a pool of countermeasures CM. The algorithm starts by selecting the node vAlert that corresponds to the alert generated by a NICE-A. Before selecting the countermeasure, we count the distance of vAlert to the target node. If the distance is greater than a threshold value, we do not perform countermeasure selection but update the ACG to keep track of alerts in the system (line 3). For the source node vAlert, all the reachable nodes (including thesourcenode)arecollectedintoasetT (line6).Because the alert is generated only after the attacker has performed the action, we set the probability of vAlert to 1 and calculate the new probabilities for all of its child (downstream) nodes in the set T (lines 7 and 8). Now, for all t 2 T the applicable countermeasures in CM are selected and new probabilities are calculated according to the effectiveness of the selected countermeasures (lines 13 and 14). The change in probability of target node gives the benefit for the applied counter- measure using (7). In the next double for-loop, we compute the Return of Investment (ROI) for each benefit of the applied countermeasure based on (8). The countermeasure which when applied on a node gives the least value of ROI, is regarded as the optimal countermeasure. Finally, SAG and ACG are also updated before terminating the algorithm. The
  • 5. M.S.HajaMoideen et al. Int. Journal of Engineering Research and Applications www.ijera.com ISSN : 2248-9622, Vol. 4, Issue 12( Part 6), December 2014, pp.259-264 www.ijera.com 263|P a g e complexity of Algorithm 2 is Ϭ(|V| x |CM|), where |V| is the number of vulnerabilities and |CM| represents the number of countermeasures. VI. Conclusion In this paper, we presented NICE, which is proposed to detect and mitigate collaborative attacks 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 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. 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. REFERENCES [1] CoudSercurity Alliance, ―Top Threats to Cloud Computing v1.0,‖ https://cloudsecurityalliance.org/topthreats/c sathreats. 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 ’12), Jan. 2012. [4] H. Takabi, J.B. Joshi, and G. Ahn, ―Security and Privacy Challenges in Cloud Computing Environments,‖ IEEE Security and Privacy, vol. 8, no. 6, pp. 24-31, Dec. 2010. [5] ―Open vSwitch Project,‖ http://openvswitch.org, May 2012. [6] Z. Duan, P. Chen, F. Sanchez, Y. Dong, M. Stephenson, and J. Barker, ―Detecting Spam Zombies by Monitoring Outgoing Messages,‖ IEEE Trans. Dependable and Secure Computing, vol. 9, no. 2, pp. 198- 210, Apr. 2012. [7] G. Gu, P. Porras, V. Yegneswaran, M. Fong, and W. Lee, ―BotHunter: Detecting Malware Infection through IDS-driven Dialog Correlation,‖ Proc. 16th USENIX Security Symp. (SS ’07), pp. 12:1-12:16, Aug. 2007. [8] G. Gu, J. Zhang, and W. Lee, ―BotSniffer: Detecting Botnet Command and Control Channels in Network Traffic,‖ Proc. 15th Ann. Network and Distributed Sytem Security Symp. (NDSS ’08), Feb. 2008. [9] O. Sheyner, J. Haines, S. Jha, R. Lippmann, and J.M. Wing, ―Automated Generation and Analysis of Attack Graphs,‖ Proc. IEEE Symp. Security and Privacy, pp. 273-284, 2002, [10] ―NuSMV: A New Symbolic Model Checker,‖ http://afrodite.itc. it:1024/nusmv. Aug. 2012. [11] P. Ammann, D. Wijesekera, and S. Kaushik, ―Scalable, graph- based network vulnerability analysis,‖ Proc. 9th ACM Conf. Computer and Comm. Security (CCS ’02), pp. 217-224, 2002. [12] X. Ou, S. Govindavajhala, and A.W. Appel, ―MulVAL: A Logic- Based Network Security Analyzer,‖ Proc. 14th USENIX Security Symp., pp. 113-128, 2005. [13] R. Sadoddin and A. Ghorbani, ―Alert Correlation Survey: Frame- work and Techniques,‖ Proc. ACM Int’l Conf.
  • 6. M.S.HajaMoideen et al. Int. Journal of Engineering Research and Applications www.ijera.com ISSN : 2248-9622, Vol. 4, Issue 12( Part 6), December 2014, pp.259-264 www.ijera.com 264|P a g e Privacy, Security and Trust: Bridge the Gap between PST Technologies and Business Services (PST ’06), pp. 37:1-37:10, 2006. [14] L. Wang, A. Liu, and S. Jajodia, ―Using Attack Graphs for Correlating, Hypothesizing, and Predicting Intrusion Alerts,‖ Computer Comm., vol. 29, no. 15, pp. 2917-2933, Sept. 2006. [15] S. Roschke, F. Cheng, and C. Meinel, ―A New Alert Correlation Algorithm Based on Attack Graph,‖ Proc. Fourth Int’l Conf. Computational Intelligence in Security for Information Systems, pp. 58-67, 2011.