The document presents an epidemic model for the spread of mobile phone viruses through Bluetooth connections. It defines key terms and establishes a differential equation model accounting for the distribution density of phones, Bluetooth signal radius, and phone velocity. The model is analyzed under varying parameters and compared to models for computer viruses. The results suggest mobile phone viruses are less likely to spread widely due to thresholds incorporating physical mobility factors.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
Secure and Reliable Data Transmission in Generalized E-MailIJERA Editor
Email is a basic service for computer users, while email malware poses critical security threats. The technique of email-borne malware will be highly effective. Email malware focuses on modeling the propagation dynamics which is a fundamental technique for developing countermeasures to reduce email malware’s spreading speed and prevalence. Modern email malware exhibits two new features, reinjection and self-start. Reinjection is an infected user sends out malware copies whenever this user visits the malicious hyperlinks or attachments. Self-start refers to the behavior that malware starts to spread whenever compromised computers restart or certain files are visited. For address this problem, to derive a novel difference equation based analytical model by introducing a new concept of virtual dirty user. Propose a new analytical model to enhanced OLSR protocol which is a trust based technique to secure the OLSR nodes against the attack. The proposed solution called EOLSR is an enhancement of the basic OLSR routing protocol, which will be able to detect the presence of malicious nodes in the network.
Ensuring Security in Emergency through SMS Alert SystemEditor IJCATR
Short Message Services are increasingly being relied upon to disseminate critical information during emergencies. In recent
days accident happens very common due to heavy traffic and increase in vehicle level and bad drivers, hence it requires a software to
inform the service centre for instant help to save life of the people. Sending the nearest emergency service and/or police officers
needed for reporting the accident can be quite tedious. The idea of this work is to reduce the time required by the emergency
personnel to reach the accident area. This can be done if the information about an accident reaches the emergency services in time and
accurately. The project eliminates any communication between the victim and the personnel which leads to confusion. This is done by
finding the accurate position of the location by making use of the GPS services available in cell phones when the victim sends a
message to the emergency number(such as 108). The project aims at reducing the severe loss due to injury and fatality rate in accidents
to a great extent.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
Secure and Reliable Data Transmission in Generalized E-MailIJERA Editor
Email is a basic service for computer users, while email malware poses critical security threats. The technique of email-borne malware will be highly effective. Email malware focuses on modeling the propagation dynamics which is a fundamental technique for developing countermeasures to reduce email malware’s spreading speed and prevalence. Modern email malware exhibits two new features, reinjection and self-start. Reinjection is an infected user sends out malware copies whenever this user visits the malicious hyperlinks or attachments. Self-start refers to the behavior that malware starts to spread whenever compromised computers restart or certain files are visited. For address this problem, to derive a novel difference equation based analytical model by introducing a new concept of virtual dirty user. Propose a new analytical model to enhanced OLSR protocol which is a trust based technique to secure the OLSR nodes against the attack. The proposed solution called EOLSR is an enhancement of the basic OLSR routing protocol, which will be able to detect the presence of malicious nodes in the network.
Ensuring Security in Emergency through SMS Alert SystemEditor IJCATR
Short Message Services are increasingly being relied upon to disseminate critical information during emergencies. In recent
days accident happens very common due to heavy traffic and increase in vehicle level and bad drivers, hence it requires a software to
inform the service centre for instant help to save life of the people. Sending the nearest emergency service and/or police officers
needed for reporting the accident can be quite tedious. The idea of this work is to reduce the time required by the emergency
personnel to reach the accident area. This can be done if the information about an accident reaches the emergency services in time and
accurately. The project eliminates any communication between the victim and the personnel which leads to confusion. This is done by
finding the accurate position of the location by making use of the GPS services available in cell phones when the victim sends a
message to the emergency number(such as 108). The project aims at reducing the severe loss due to injury and fatality rate in accidents
to a great extent.
AirHopper: Bridging the Air-Gap between Isolated Networks and Mobile Phones u...mordechaiguri
Information is the most critical asset of modern organizations, and accordingly coveted by adversaries. When highly sensitive data is involved, an organization may resort to air-gap isolation, in which there is no networking connection between the inner network and the external world. While infiltrating an air-gapped network has been proven feasible in recent years (e.g., Stuxnet), data exfiltration from an air-gapped network is still considered to be one of the most challenging phases of an advanced cyber-attack.
In this paper we present "AirHopper", a bifurcated malware that bridges the air-gap between an isolated network and nearby infected mobile phones using FM signals.
While it is known that software can intentionally create radio emissions from a video display unit, this is the first time that mobile phones are considered in an attack model as the intended receivers of maliciously crafted radio signals. We examine the attack model and its limitations, and discuss implementation considerations such as stealth and modulation methods. Finally, we evaluate AirHopper and demonstrate how textual and binary data can be exfiltrated from physically isolated computer to mobile phones at a distance of 1-7 meters, with effective bandwidth of 13-60 Bps (Bytes per second).
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
AirHopper: Bridging the Air-Gap between Isolated Networks and Mobile Phones u...mordechaiguri
Information is the most critical asset of modern organizations, and accordingly coveted by adversaries. When highly sensitive data is involved, an organization may resort to air-gap isolation, in which there is no networking connection between the inner network and the external world. While infiltrating an air-gapped network has been proven feasible in recent years (e.g., Stuxnet), data exfiltration from an air-gapped network is still considered to be one of the most challenging phases of an advanced cyber-attack.
In this paper we present "AirHopper", a bifurcated malware that bridges the air-gap between an isolated network and nearby infected mobile phones using FM signals.
While it is known that software can intentionally create radio emissions from a video display unit, this is the first time that mobile phones are considered in an attack model as the intended receivers of maliciously crafted radio signals. We examine the attack model and its limitations, and discuss implementation considerations such as stealth and modulation methods. Finally, we evaluate AirHopper and demonstrate how textual and binary data can be exfiltrated from physically isolated computer to mobile phones at a distance of 1-7 meters, with effective bandwidth of 13-60 Bps (Bytes per second).
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
A new system to detect coronavirus social distance violation IJECEIAES
In this paper, a novel solution to avoid new infections is presented. Instead of tracing users’ locations, the presence of individuals is detected by analysing the voices, and people’s faces are detected by the camera. To do this, two different Android applications were implemented. The first one uses the camera to detect people’s faces whenever the user answers or performs a phone call. Firebase Platform will be used to detect faces captured by the camera and determine its size and estimate their distance to the phone terminal. The second application uses voice biometrics to differentiate the users’ voice from unknown speakers and creates a neural network model based on 5 samples of the user’s voice. This feature will only be activated whenever the user is surfing the Internet or using other applications to prevent undesired contacts. Currently, the patient’s tracking is performed by geolocation or by using Bluetooth connection. Although face detection and voice recognition are existing methods, this paper aims to use them and integrate both in a single device. Our application cannot violate privacy since it does not save the data used to carry out the detection and does not associate this data to people.
“Design and Detection of Mobile Botnet Attacks”iosrjce
A mobile botnet is a type of bot that runs automatically when installed on a mobile phone, which
does not have any anti-malware. The botnet gains complete access over our mobile device. The common
propagation medium for smartphone based botnet attacks are SMS, Bluetooth and Wi-Fi. In our project, we will
demonstrate a SMS-cum-Wi-Fi based mobile botnet using a centralized C&C server. The botmaster initiates
commands to C&C server and the C&C propagates to infected smartphones i.e. bots. We will try to develop a
network which cannot be detected easily and propagates fast. The target of the propagation will be Android
Operating System. For detection, an application is created to detect whether smartphone is working as bot or
not. In this, we guide user about possible botnet attacks.
A secure routing process to simultaneously defend against false report and wo...ieijjournal
Most research related to secure routing in sensor networks has focused on how to detect and defend against a single attack. However, it is not feasible to predict which attack will occur in sensor networks. It is possible for multiple attacks to occur simultaneously, degrading the performance of the existing security schemes. For example, an attacker may try simultaneous false report and wormhole attacks to effectively damage a sensor network. Hence, a multiple simultaneous attack environment is much more complex than a single attack environment. Thus, a new security scheme that can detect multiple simultaneous attacks with a high probability and low energy consumption is needed. In this paper, we propose a secure routing scheme to defend against wormhole and false report attacks in sensor networks. The proposed method achieves a higher attack detection ratio and consumes less energy in a multi-attack scenario compared to existing schemes. It can also be extended to other types of attacks and security schemes to detect and defend against possible combinations of multiple attacks.
Face expressions, facial features, kinect sensor, face tracking SDK, neural n...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
PhD Writing Assistance has recruited their experts after rigorous assessments and as such they possess high credentials from the to UAE, Saudi, the UK, and the Indian Universities. Quite obviously it is recommended to the PhD students that they should follow all these criteria and get the services from PhD Writing Assistance to attain their desired results.
For More: https://www.phdwritingassistance.com/
SPECIFICATION BASED TESTING OF ON ANDROID SYSTEMSijwmn
With the surging of mobile applications, mobile security draws more and more attentions from researchers
in various areas. Due to the lack of quality assurance approaches in mobile computing, many mobile
applications suffer the vulnerabilities and security flaws. In this paper, we proposed a model based unit
testing approach on the android security properties using JUnit. Both behavior and structure model of the
android application were developed on the Unified Modeling Language (UML) – behavior is described in
state diagram, while structure is described in class diagram. Our approach focus on two common security
groups – the access control and authentication properties. Both groups are represented in the operations
defined in the class diagrams and dynamic behaviors are captured (partially) in the state diagram. A set of
well defined test cases is developed to validate the desired properties based on the class diagram. All
properties on the class diagram and state diagram are described in Object Constraint Language (OCL) – a
formal specification language on the first order logic and set theory.The results of this research will
provide a sound foundation towards the specification based unit testing on mobile security.
The above PPT contains the following content:
1. SPREADING OF VIRUS
2. ANAMNESIS (CASE STUDIES)
3. CURRENT STATUS OF MOBILE MALWARE
4. PROTECTIVE MEASURES
5. THREATS OF MOBILE PHONE
6. CONCLUSION
The detailed PROTECTIVE MEASURES are given in the above PPT.
Review on mobile threats and detection techniquesijdpsjournal
Since last-decade, smart-phones have gained widespread usage. Mobile devices store personal details
such as contacts and text messages. Due to this extensive growth, smart-phones are attracted towards
cyber-criminals. In this research work, we have done a systematic review of the terms related to malware
detection algorithms and have also summarized behavioral description of some known mobile malwares
in tabular form. After careful solicitation of all the possible methods and algorithms for detection of
mobile-based malwares, we give some recommendations for designing future malware detection algorithm
by considering computational complexity and detection ration of mobile malwares.
Investigating Wireless and Internet of Things Technologies Security Threats a...ijwmn
Wireless and mobile devices are part of our lives. Wireless technology encompasses Internet of Things (IoT). Some key features of these devices are sensors, connectivity, and artificial intelligence and they can be found in health clinics, homes, buildings, vehicles, cities, wearables, etc. However, wireless and IoT technologies are sources of a variety of security threats to privacy and data and are becoming targets for attackers or hackers. In this paper, the authors strive to answer to the following research questions: 1) What types of threats can wireless and IoT technologies pose? 2) What type of threats can be exploited for attack and how? 3) What techniques are used to mitigate the threats and attacks?
4) What can wireless and IoT users do to protect their privacy and data? As a result, we investigate different types of security threats and attacks, and common security
countermeasures used in wireless and IoT.
INVESTIGATING WIRELESS AND INTERNET OF THINGSTECHNOLOGIES SECURITY THREATS AN...ijwmn
Wireless and mobile devices are part of our lives. Wireless technology encompasses Internet of Things
(IoT). Some key features of these devices are sensors, connectivity, and artificial intelligence and they can
be found in health clinics, homes, buildings, vehicles, cities, wearables, etc. However, wireless and IoT
technologies are sources of a variety of security threats to privacy and data and are becoming targets for
attackers or hackers. In this paper, the authors strive to answer to the following research questions: 1)
What types of threats can wireless and IoT technologies pose? 2) What type of threats can be exploited for
attack and how? 3) What techniques are used to mitigate the threats and attacks? 4) What can wireless and
IoT users do to protect their privacy and data? As a result, we investigate different types of security threats
and attacks, and common security countermeasures used in wireless and IoT.
Similar to An epidemic model of mobile phone virus (20)
1. 1
An Epidemic Model of Mobile Phone Virus
Hui Zheng1
, Dong Li2
, Zhuo Gao3
1
Network Research Center, Tsinghua University, P. R. China
zh@tsinghua.edu.cn
2
School of Computer Science and Technology,
Huazhong University of Science and Technology, P. R. China
lidong@hust.edu.cn
3
Department of Physics, Beijing Nomarl University, P. R. China
zhuogao@bnu.edu.cn
Abstract
Considering the characteristics of mobile network,
we import three important parameters: distribution
density of mobile phone, coverage radius of Bluetooth
signal and moving velocity of mobile phone to build an
epidemic model of mobile phone virus which is different
from the epidemic model of computer worm. Then
analyzing different properties of this model with the
change of parameters; discussing the epidemic
threshold of mobile phone virus; presenting suggestions
of quarantining the spreading of mobile phone virus.
Keywords: Mobile Phone Virus, Epidemic Model,
Security of Wireless Network, Bluetooth, Smart Phone.
1. Introduction
The first computer virus that attacks mobile phone is
VBS. Timofonica which was found on May 30, 2000
[1]. This virus spreads through PCs, but it can use the
message service of moviestar.net to send out rubbish
short messages to its subscriber. It is propagandized as
mobile phone virus by the media, but in fact it’s only a
kind of computer virus and can’t spread through mobile
phone which is the only attacked object. Cabir Cell
Phone Worm which was found on June 14, 2000 is
really a mobile phone virus [2]. It spreads from one cell
phone to another by Bluetooth. Now it is found in more
than 20 countries and has more than 7 variants. Cabir
has the characteristic of initiative spreading and this
pattern will be mostly adopted by “mobile phone virus”
in the future.
Table 1 lists the comparison between configuration
of smart phone and computer. This table presents the
most advanced desk-top computer configuration in
1998 and 1999. Generally, it takes 2 to 3 years for
computer with the most advanced configuration to
become popular. That is to say, when the Code Red
Worm broke out in 2001, common hardware of
computers in Internet was as same as the configuration
in table 1. With the comparison in table 1, we can see
that smart phone presently has already possessed
hardware condition for computer virus spreading.
Table 1. Hardware comparing between smart phone
and desk-top personal computer
Hardware 2005(dop
od 828)
1998 PC 1999 PC
CPU Intel
416MHz
PentiumⅡ
333MHz
Pentium III
450MHz[3]
Memory 128M 32M 64M
Hard Disk 2G~8G 2G 6G
The development and popularization of smart phone
are both very fast. According to the statistics of ARC,
in 2004 the sum of smart phone is 27,000,000,
accounting for 3% of the global amount of mobile
phones. IDC estimates that the sum of smart phone will
reach up to 130,000,000 by 2008 and account for 15%
of the global amount of mobile phones [4]. So we
should pay much attention to the security of smart
phone.
In this paper, “smart phone” is one smart mobile
terminal device with the integrated ability of data
transmission, processing and communication; “mobile
phone virus” is a malicious code that can spread
through all kinds of smart mobile terminal devices. As
to the security research, though we can refer to the
security research results in MANETs (Mobile Ad Hoc
Networks), MANETs and Sensor network emphasize
that resource is finite and all the problems about
application and security should be restricted to this
precondition [12]. Smart mobile terminal device
emphasizes that resource is abundant, even possess the
same computing ability as desk-top personal computer.
So for these two security problems, the starting points
of research are different. Recently, paper [5]
demonstrates that traditional epidemic model of
computer virus can’t be applied to virus spreading in
2. 2
mobile environment and the epidemic model when the
mobile phone moves with variable velocity is also
discussed. But in a small area, uniform motion accords
with the sport law of human being preferably. What’s
more, some important parameters such as distribution
density and signal coverage radius are not imported to
the model. Paper [6] compares to the required condition
of virus spreading in computer and gives the
corresponding required condition of virus spreading in
MANETs by simulation.
This paper first discusses several spreading modes of
mobile phone virus; The second section builds the
epidemic model of mobile phone virus which imports 3
parameters: moving velocity, signal coverage radius
and distribution density; The third section analyses
some relevant characteristics of this model; the fourth
section compares the epidemic model of mobile phone
virus with the epidemic model of Internet worm and
discusses the threshold of mobile phone virus breaking
out. At last, we make some discussions.
2. The spreading way of mobile phone virus
Though paper [7~8] presents many examples of
“mobile phone virus”, many of them are not able to
spread, so they are not real mobile phone virus.
According to analysis of all kinds of epidemic
malicious codes which have been found, such as Cabir
[2], Commwarrior [9], Brador [10], Skull [11] etc, we
can define mobile phone virus: it is a piece of data or
program that spreads among smart mobile terminal
devices by the communication interfaces and can
influence the usage of handset or leak out sensitive data.
Through the analysis of spreading way, we can
conclude table 2:
Table 2. Spreading way of mobile phone virus
Wireless spreading
channel
Spreading
distance
Spreading direction Way of discovering
neighbor nodes
Relay
(Yes or No)
GPRS/CDMA 1XRTT 1000m Non-directional Appointed Yes
Wi-Fi(802.11) 100m Non-directional Appointed Yes
Bluetooth 10m Non-directional Automatic No
IrDA 1m Directional Automatic No
For the mobile phone virus that can spread by MMS
and E-mail, it can transmit data by GPRS and Wi-Fi; for
the mobile phone virus that spread by electronic file, it
can transmit data by Bluetooth and IrDA. Although
there are four wireless transmission ways, some need
relay nodes or directional angle, so Bluetooth is the best
choice for virus writer.
In this model, we mainly consider those mobile
phone viruses that spread through Bluetooth. For other
ways of transmission, we will build the model in other
papers.
3. The epidemic model of mobile phone
propagating
Supposing mobile phone has two statuses:
Susceptible and infected. The infected will come back
to susceptible with certain probability. In table 3, we
define some symbols:
Table 3. Symbol definition
Symbol Instructions
Ω moving space of mobile phone (2-dimmension)
ρ distribution density of mobile phone (uniform
distribution)
v moving velocity of mobile phone (uniform
velocity)
r coverage radius of Bluetooth signal
I The number of virus in mobile phone at time t
β epidemic rate of mobile phone virus propagating
δ resuming rate of the infected
Then we can build the epidemic model of mobile
phone virus:
I
I
vrrI
dt
dI
⋅−⋅
⋅Ω
−⋅Ω
⋅−⋅⋅⋅+⋅⋅= δβ
ρ
ρ
ρπ )1)2(( 2
Suppose:
δβρβπ −−+= )2( 2
rvra ,
ρ
βρβπ
Ω
−+
=
)2( 2
rvr
b ,
Then the differential equation is:
2
bIaI
dt
dI
−= ,
The solution is:
cat
cat
be
ae
I +
+
+
=
1
,
For )( 0tI , the initial value of c is a constant.
We can conclude from the solution: if 0<a ,then
0→I , and if 0>a , then
b
a
I → .
4. Analysis of model properties
The changes of model properties with changes of
different parameters are researched. Table 4 presents
the range of parameters.
3. 3
Table 4. The range of parameters
Symbol Instruction Range
Ω moving space of mobile
phone (2-dimmension)
1000m * 1000m
ρ distribution density of
mobile phone (uniform
distribution)
0.001~0.1/m2
v moving velocity of
mobile phone (uniform
velocity)
2m/s
r coverage radius of
Bluetooth signal
10m
β epidemic rate of mobile
phone virus
0.75
δ resuming rate of
infected
0.025
0I The number of initial
infected mobile phones
5
4.1. Influence of distribution density to virus
spreading
The connotative subject condition of equation
is
)2(
1
2
vrr ⋅⋅+⋅
>
π
ρ , mobile phone virus is able to
spread when this condition is satisfied. Figure 1 shows
the relationship between distribution density and
infection percentage. When the subject condition is not
satisfied, infection percentage is 0; when the subject
condition is satisfied, the infection percentage is very
sensitive to the change of distribution density, the small
change of distribution density can lead to great
improvement proportion of the infected.
Relationship of distribution density and infection
percentage
0
0.2
0.4
0.6
0.8
1
0.001
0.008
0.015
0.022
0.029
0.036
0.043
0.050
distribution density(number of mobile
phone in one unit area)
infection
percentage
Figure 1. Relationship of distribution density and
infection percentage
Figure 2 is the relationship between distribution
density and spreading time. It shows the influence of
distribution density to moving velocity. Mobile phone
virus can’t spread when distribution density is small.
Spreading time that the infection of mobile phone virus
gets to equilibrium reflects the spreading velocity of
virus. From these we can see that spreading velocity is
very sensitive to the change of distribution density.
Relationship between ditribution
density and spreading time
0
500
1000
1500
2000
0.0029
0.0036
0.0043
0.0050
0.0057
0.0064
distribution density
spreadingtime
Figure 2. Relationship between distribution density
and spreading time
4.2. Influence of coverage radius to virus
spreading
Considering the range of coverage radius of
Bluetooth signal r varies from 5m to 15m. Distribution
density of mobile phone is 0.005. Figure 3 is the
relationship of coverage radius and percentage of the
infected, which presents the influence of coverage
radius to virus spreading.
From these we can see that mobile phone virus can’t
spread when coverage radius is very small. If it spreads,
the infection percentage will change with coverage
radius.
Ralationship between coverage radius
and infection percentage
0
0.2
0.4
0.6
0.8
1
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
coverage radius
infection
percentage
Figure 3. Relationship between coverage radius and
infection percentage
Figure 4 is the relationship between coverage radius
and spreading time, it presents the influence of coverage
radius to spreading velocity. Virus can’t spread when
coverage radius is very small. Spreading velocity is
very sensitive to the changes of coverage radius.
4. 4
Ralationship between coverage radius and
spreading time
0
200
400
600
800
1000
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
coverage radius
spreadingtime
Density=0.005
Figure 4. Ralationship between coverage radius and
spreading time
4.3. Influence of moving velocity to virus
spreading
Assuming distribution density of mobile phone is
0.0035, the range of moving velocity is 1m/s~30m/s,
figure 5 is the relationship between moving velocity and
infection percentage, it presents the influence of moving
velocity to the spreading of mobile phone virus. For the
small distribution density of mobile phone and typical
coverage radius, speeding the moving velocity can
result in the spreading of the virus which can’t spread
before.
Ralationship between moving velocity
and infenction percentage
0
0.2
0.4
0.6
0.8
1
1.0
6.0
11.0
16.0
21.0
26.0
moving velocity
infection
percentage
Density=0.0035
Figure 5. Relationship between moving velocity and
infection percentage
Figure 6 is the relationship between moving velocity
and spreading time. It presents the influence of moving
velocity to spreading velocity. From this figure we can
see that increasing of moving velocity can speed up the
spreading of virus.
Relationship between moving velocity
and spreading time
0
500
1000
1500
2000
2500
1.0
5.5
10.0
14.5
19.0
23.5
28.0
moving velocity
spreadingtime
Density=0.0035
Figure 6. Relationship between moving velocity and
spreading time
The time that virus file transfers from one mobile
phone to another is fT , the discussion above supposes
that the moving of mobile phone has no influence to
virus spreading. If we take into account the influence of
moving velocity of mobile phone, we can add one
subject condition:
fT
r
v < . When this condition is
satisfied, virus can spread. When this condition is not
satisfied, that is to say, mobile phone moves too fast,
then the time that virus stay in the coverage area of
signal is too short, virus can’t spread.
5. Results of comparison with epidemic
models of worm
The corresponding epidemic model of worm in
computer network can be expressed as [13]:
III
dt
dI
⋅−⋅−⋅Ω⋅= δβρ )(
In computer network, ρ⋅Ω is the sum of computer
and it is a fixed value in short time. The threshold of its
spreading is: ρ
β
δ
⋅Ω< . If this condition is satisfied,
worm can spread. This condition can be satisfied easily.
Different from the spreading threshold of computer
virus, the spreading threshold of mobile phone virus is
subject to coverage radius of wireless signal, moving
velocity and distribution density. According to the
stabilized solution of differential equation, we can see:
if 0<a , then 0→I ;
for
δβρβπ −−+= )2( 2
rvra ,
we can get a new threshold:
1)2( 2
−⋅⋅+⋅< ρπ
β
δ
vrr .
When this condition is satisfied, virus will break out;
if this condition is not satisfied, virus can’t break out.
5. 5
From these we can see: the condition that mobile
phone virus breaks out is much more rigorous than
worm in computer network. So the probability of that
mobile phone virus breaks out in large area is very
small, but it is possible in local area.
6. Conclusions
Because of the mobility, mobile phone has some
relevant characteristics: moving velocity, moving scope
etc, which make the epidemic model of mobile phone
virus very different from the model of computer virus
and worm.
We can make use of stochastic mobile model (such
as Random Waypoint model, Random Direction model
[14]) to build spreading model of mobile phone virus.
But these stochastic models have some limitations and
can’t accord with the fact preferably. For simplification
of this problem, we build this model with uniform
motion.
Through the analysis of this model, we can conclude
some measures of quarantining mobile phone virus:
reducing coverage radius, such as reducing signal
power, or interfering signal etc; decreasing moving
velocity, such as restricting the flowage of person;
lessening distribution density of mobile phone, such as
controlling the moving area of someone with mobile
phone; these measures have distinct differences with the
usual ways of quarantining mobile phone virus
spreading.
Acknowledgement
This work is supported in part by National Science
Foundation of China under contract 60203004; by
High-Tech Program (863) of China under contract
2003AA142080. Points of view in this document are
those of the authors and do not necessarily represent the
official position of Tsinghua University, Huazhong
University of Science and Technology, or Beijing
Nomarl University.
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