Advances in robotics have made it possible to develop a variety of new architectures for autonomous wireless networks of sensors. Mobile nodes, essentially small robots with sensing, wireless communications, and movement capabilities, are useful for tasks such as static sensor deployment, adaptive sampling, network repair, and event detection. These advanced sensor network architectures could be used for a variety of applications including intruder detection, border monitoring, and military patrols. In potentially hostile environments, the security of unattended mobile nodes is extremely critical. The attacker may be able to capture and compromise mobile nodes, and then use them to inject fake data, disrupt network operations, and eavesdrop on network communications.
WSN Architecture
In this scenario, a particularly dangerous attack is the replica node attack. in which the adversary takes the secret keying materials from a compromised node, generates a large number of attacker-controlled replicas that share the compromised node's keying materials and ID, and then spreads these replicas throughout the network. With a single captured node, the adversary can create as many replica nodes as he has the hardware to generate. Note that replica nodes need not be identical robots; a group of static nodes can mimic the movement of a robot and other mobile nodes or even humans with handheld devices could be used. The only requirement is that they have the software and keying material to communicate in the network, all of which can be obtained from the captured node.
OBJECTIVE OF THE PROJECT:
Due to the unattended nature of wireless sensor networks, an adversary can capture and compromise sensor nodes, make replicas of them, and then mount a variety of attacks with these replicas. In this paper, we propose a novel mobile replica detection scheme based on the Sequential Probability Ratio Test (SPRT) . We use the fact that an uncompromised mobile node should never move at speeds in excess of the system-configured maximum speed. As a result, a benign mobile sensor node's measured speed will nearly always be less than the system-configured maximum speed as long as we employ a speed measurement system with a low error rate. On the other hand, replica nodes are in two or more places at the same time. This makes it appear as if the replicated node is moving much faster than any of the benign nodes, and thus the replica nodes' measured speeds will often be over the system-configured maximum speed.
CHAPTER 2
LITERATURE REVIEW
1. S. _Capkun and J.P. Hubaux, "Secure Positioning in Wireless Networks," IEEE J. Selected Areas in Comm., vol. 24, no. 2, pp. 221-
232, Feb. 2006.
The problem of positioning in wireless networks has been studied mainly in a non adversarial setting. In this paper, we analyze the resistance of positioning techniques to position and distance spoofing attacks. We propose a mechanism for secure positioning of wireless devices, that we call verifiable multilateration. We then show how this mechanism can be used to secure positioning in sensor networks. We analyze our system through simulations.
2. M. Conti, R.D. Pietro, L.V. Mancini, and A. Mei, "A Randomized,Efficient, and Distributed Protocol for the Detection of Node Replication Attacks in Wireless Sensor Networks," Proc. ACM MobiHoc, pp. 80-89, Sept. 2007.
Wireless sensor networks are often deployed in hostile environments, where an adversary can physically capture some of the nodes. Once a node is captured, the attacker can re-program it and replicate the node in a large number of clones, thus easily taking over the network. The detection of node replication attacks in a wireless sensor network is therefore a fundamental problem. A few distributed solutions have recently been proposed. However, these solutions are not satisfactory. First, they are energy and memory demanding: A serious drawback for any protocol that is to be used in resource constrained environment such as a sensor network. Further, they are vulnerable to specific adversary models introduced in this paper. The contributions of this work are threefold. First, we analyze the desirable properties of a distributed mechanism for the detection of node replication attacks. Second, we show that the known solutions for this problem do not completely meet our requirements.Third, we propose a new Randomized, efficient, and Distributed (RED) protocol for the detection of node replication attacks and we show that it is completely satisfactory with respect to the requirements. Extensive simulations also show that our protocol is highly efficient in communication, memory, and computation, that it sets out an improved attack detection probability compared to the best solutions in the literature, and that it is resistant to the new kind of attacks we introduce in this paper, while other solutions are not.
3. J. Ho, M. Wright, and S.K. Das, "Fast Detection of Replica Node Attacks in Mobile Sensor Networks Using Sequential Analysis," Proc. IEEE INFOCOM, pp. 1773-1781, Apr. 2009.
Due to the unattended nature of wireless sensor networks, an adversary can capture and compromise sensor nodes, generate replicas of those nodes, and mount a variety of attacks with the replicas he injects into the network. These attacks are dangerous because they allow the attacker to leverage the compromise of a few nodes to exert control over much of the network. Several replica node detection schemes in the literature have been proposed to defend against these attacks in static sensor networks. These approaches rely on fixed sensor locations and hence do not work in mobile sensor networks, where sensors are expected to move. In this work, we propose a fast and effective mobile replica node detection scheme using the Sequential Probability Ratio Test. To the best of our knowledge, this is the first work to tackle the problem of replica node attacks in mobile sensor networks. We show analytically and through simulation experiments that our schemes achieve effective and robust replica detection capability with reasonable overheads.
4. J. Ho, D. Liu, M. Wright, and S.K. Das, "Distributed Detection of Replicas with Deployment Knowledge in Wireless Sensor Networks," Ad Hoc Networks, vol. 7, no. 8, pp. 1476-1488, Nov. 2009.
Several protocols have been proposed to mitigate the threat against wireless sensor networks due to an attacker finding vulnerable nodes, compromising them, and using these nodes to eavesdrop or undermine the operation of the network. A more dangerous threat that has received less attention, however, is that of replica node attacks, in which the attacker compromises a node, extracts its keying materials, and produces a large number of replicas to be spread throughout the network. Such attack enables the attacker to leverage the compromise of a single node to create widespread effects on the network. To defend against these attacks, we propose distributed detection schemes to identify and revoke replicas. Our schemes are based on the assumption that nodes are deployed in groups, which is realistic for many deployment scenarios. By taking advantage of group deployment knowledge, the proposed schemes perform replica detection in a distributed, efficient, and secure manner. Through analysis and simulation experiments, we show that our schemes achieve effective and robust replica detection capability with substantially lower communication, computational, and storage overheads than prior work in the literature.
5. L. Hu and D. Evans, "Localization for Mobile Sensor Networks," Proc. ACM MobiCom, pp. 45-57, Sept. 2004.
Many sensor network applications require location awareness, but it is often too expensive to include a GPS receiver in a sensor network node. Hence, localization schemes for sensor networks typically use a small number of seed nodes that know their location and protocols whereby other nodes estimate their location from the messages they receive. Several such localization techniques have been proposed, but none of them consider mobile nodes and seeds. Although mobility would appear to make localization more difficult, in this paper we introduce the sequential Monte Carlo Localization method and argue that it can exploit mobility to improve the accuracy and precision of localization. Our approach does not require additional hardware on the nodes and works even when the movement of seeds and nodes is uncontrollable. We analyze the properties of our technique and report experimental results from simulations. Our scheme outperforms the best known static localization schemes under a wide range of conditions.
6. J. Jung, V. Paxon, A.W. Berger, and H. Balakrishnan, "Fast Portscan Detection Using Sequential Hypothesis Testing," Proc. IEEE Symp. Security and Privacy, pp. 211-225, May 2004.
Attackers routinely perform random "portscans" of IP addresses to find vulnerable servers to compromise. Network Intrusion Detection Systems (NIDS) attempt to detect such behavior and flag these portscanners as malicious. An important need in such systems is prompt response: the sooner a NIDS detects malice, the lower the resulting damage. At the same time, a NIDS should not falsely implicate benign remote hosts as malicious. Balancing the goals of promptness and accuracy in detecting malicious scanners is a delicate and difficult task. We develop a connection between this problem and the theory of sequential hypothesis testing and show that one can model accesses to local IP addresses as a random walk on one of two stochastic processes, corresponding respectively to the access patterns of benign remote hosts and malicious ones. The detection problem then becomes one of observing a particular trajectory and inferring from it the most likely classification for the remote host. We use this insight to develop TRW (Threshold Random Walk), an online detection algorithm that identifies malicious remote hosts. Using an analysis of traces from two qualitatively different sites, we show that TRW requires a much smaller number of connection attempts (4 or 5 in practice) to detect malicious activity compared to previous schemes, while also providing theoretical bounds on the low (and configurable) probabilities of missed detection and false alarms. In summary, TRW performs significantly faster and also more accurately than other current solutions.
CHAPTER 3
WORK DONE IN PHASE 1
3.1 EXISTING SYSTEM
A particularly dangerous attack is the replica node attack, in which the adversary takes the secret keying materials from a compromised node, generates a large number of attacker-controlled replicas that share the compromised node's keying materials and ID, and then spreads these replicas throughout the network.
With a single captured node, the adversary can create as many replica nodes as he has the hardware to generate.
3.2 PROPOSED SYSTEM
We propose a novel mobile replica detection scheme based on the Sequential Probability Ratio test.
We use the fact that an uncompromised mobile node should never move at speeds in excess of the system-configured maximum speed.
A benign mobile sensor node's measured speed will nearly always be less than the system-configured maximum speed as long as we employ a speed measurement system with a low error rate.
On the other hand, replica nodes are in two or more places at the same time. This makes it appear as if the replicated node is moving much faster than any of the benign nodes, and thus the replica nodes' measured speeds will often be over the system-configured maximum speed.
CHAPTER 4
SYSTEM ORGANIZATION
SYSTEM DESIGN
4.1. ARCHITECTURE DIAGRAM:
SUCCESSFUL
TRANSMISSON
QUARANTINE
DEFENSE STRATEGY
DETECTION AND REVOCATION
CLAIM GENERATION AND FORWARDING
SENSOR NETWORK
MOBILE REPLICA DETECTION USING SEQUENTIAL PROBABILITY TEST
REPLICA NODE ATTACK THROUGH ADVERSARY4.2. OVER ALL BLOCK DIAGRAM FOR PROPOSED SCHEME:
4.3 LIST OF MODULES:
Network Analysis model
Replica Node Attack model
Replica Detection model
Security Analysis model
4.3.1 NETWORK ANALYSIS MODEL:
NODE
NODE
NODE
NODE TRANSMIT IN RANGE
PACKETS BY HIGHER LAYERS OF NODE
SYMMETRIC TRANSMISSION AND RECEPTION
DFD:
A wireless sensor network (WSN) consists of spatially distributed autonomous sensors 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. The more modern networks are bi-directional, also enabling control of sensor activity. The
development of wireless sensor networks was motivated by military applications such as battlefield surveillance; today such networks are used in many industrial and consumer applications, such as industrial process monitoring and control, machine health monitoring, and so on.
4.3.2 REPLICA NODE ATTACK MODEL:
In this module the adversary may compromise the one of the node which is the trusted in the network is called as benign node. The adversary compromises the node and make replica of that compromised node. The replica node is injected in to the network. Then the adversary may control all subset of node in the network and capture the public key of each node so that he may get whole information of that network. This attack is the most dangerous attack in wireless network.
ADVERSARY
COMPROMISE SENSOR NODE IN NETWORK
REPLICA FOR COMPROMISED NODE
CONTROLL ALL SUBSET OF NODE IN N/W
DFD:
4.3.3 REPLICA DETECTION MODEL:
In this section presents the details of our technique to detect replica node attacks in mobile sensor networks. In static sensor networks, a sensor node is regarded as being replicated if it is placed in more than one location. If nodes are moving around in network, however, this technique does not work, because a benign mobile node would be treated as a replica due to its continuous change in location. Each time mobile sensor node moves to new location. It first discovers its location Lu and then discovers its set of neighboring nodes. Every neighboring node asks node u for an authenticated location claim by sending its current time T to node u. Upon receiving T, node u checks whether T is valid or not. The speed and location of each node is measured. The information of each node is noted by sequential probability ratio test. Since speed is measured based on location and time, the errors can come from either measurement. the time of each claim is measured and verified by the requesting node, rather than the measured node. Since claim verification and forwarding is done probabilistically, the chance of having two verified and forwarded claims from the same requesting node is low. Thus, systematic time measurement error at the requesting node is likely to result in independent errors between each location claim for the nodes being measured. Systematic location measurement error means that the measurements are not independent. Speed of each node is compared; the speed of replica node will be higher than the benign node.
4.3.3.1 Mobile Replica Detection Using Sequential Probability Ratio Test
This section presents the details of our technique to detect replica node attacks in mobile sensor networks. In static sensor networks, a sensor node is regarded as being replicated if it is placed in more than one location. If nodes are moving around in network, however, this technique does not work, because a benign mobile node would be treated as a replica due to its continuous change
in location. Hence, we must use some other technique to detect replica nodes in mobile sensor networks. Fortunately, mobility provides us with a clue to help resolve the mobile replica detection problem. Specifically, a benign mobile sensor node should never move faster than the system configured maximum speed, Vmax. As a result, a benign mobile sensor node's measured speed will appear to be at most Vmax as long as we employ a speed measurement.
SPRT (Sequential Probability Ratio Test) using Hypothesis:-
We apply the SPRT to the mobile replica detection problem as follows: Each time a mobile sensor node moves to a new location, each of its neighbors asks for a signed claim containing its location and time information and decides probabilistically whether to forward the received claim to the base station. The base station computes the speed from every two consecutive claims of a mobile node and performs the SPRT by considering speed as an observed sample.
Each time the mobile node's speed exceeds (respectively, remains below) Vmax, it will expedite the random walk to hit or cross the upper (respectively, lower) limit and thus lead to the base station accepting the alternate (respectively, null) hypothesis that the mobile node has been (respectively, not been) replicated. Once the base station decides that a mobile node has been replicated, it revokes the replica nodes from the network.
EMPLOYMENT OF SEQUENTIAL PROBABILITY RATIO TEST
Network status and detection
MONITORING MECHANISM DFD:
COMPARISON OF BENIGN NODES
CLAIM GENERATION AND FORWARDING
DETECTION AND REVOCATION
4.3.4 SECURITY ANALYSIS
In this section, we will first describe the detection accuracy of our proposed scheme and then present attack scenarios to break this scheme and a defense strategy we propose to limit these attacks. Finally, we will show that the attacker's gain is substantially limited by the defense strategy. We now quantitatively determine a limit on the amount of time for which a set of replicas can avoid detection and quarantine when they follow a strategy of responding only to selected claims. Our underlying argument is that the replica nodes must ignore a minimum number of claim requests to avoid detection, but we will configure the quarantine system to react and stop the replica node attacks when many claims are ignored. We model the arrival of claim requests to each replica as a homogeneous Poisson process. We use a Poisson process due to the following reasons: First, we assume that mobile nodes' movements in disjoint intervals are independent from each other and thus the number of times that mobile nodes meet to replicas in disjoint intervals is accordingly independent from each other. Second, the probability distribution of the number of claim requests received by replicas in a time interval should be modeled to only depend on the length of the interval. This is reasonable in the sense that the number of claim requests received by replicas in a time interval varies in accordance with the length of the interval.
Replica node sends claim request
Removes the replica nodes from the network
Compares the no of claim request of the replica node with the response of claims w
Game theoretic Analysis of quarantine Defense Strategy
qDFD:
4.4 SOFTWARE DESCRIPTION
4.4.1 Core JAVA
Java is a powerful but lean object-oriented, multi-threaded programming language. It is designed to be the small, simple and portable across different operating systems.
The powerful of java is due to its unique technology that is design on the basis of 3 key elements. They are the usage of applets, powerful programming language constructs and a rich set of significant object classes.
When a program is compiled it is translated in to machine code or processor instructions that are specific to the processor. In the java development environment there two parts:
1. Java Compiler - that generates byte code instead of machine code.
2. Java Interpreter - executes java program.
The disadvantage of using byte code is the execution speed. Since system specific programs run directly on the hardware, they are faster than the java byte codes that are processed by the interpreter.
Java is actually a platform consist of three components.
1. Java programming language.
2. Java library of classes and interfaces.
3. Java Virtual Machine.
4.4.2 SWING
4.4.2.1 Introduction
1.1 GUI's
Unlike our previous Java programs, today we will discuss how to make applications that use a
Graphical User Interface or GUI. In this tutorial we will use Swing, a huge set of classes (sometimes
called a widget set that implements almost all common GUI entities. Although there are
other widget sets to choose from, Swing has some advantages that we might benefit from during
this course:
• It has a high level of abstraction
• It is extremely flexible
• It is written in Java, so available on all Java platforms
• It can be used in Applets (client side Java applications stored on web pages)
Ofcourse there are some downsides too:
• The look of Swing objects is the same on all platforms. This means your java application
may have a different look and feel compared other programs on your computer.
• It is not fast (some people say it is slow)
For the time being Swing seems to be a good choice. If you are interested in more speedy and
fance widget sets, you might want to check out Swt, the widget set used by eclipse.
CHAPTER 5
IMPLEMENTATION AND RESULTS
5.1 ALGORITHM 1
SPRT for replica detection
INITIALIZATION: n = 0, !n = 0
INPUT: location information L and time information T
OUTPUT: accept the hypothesis H0 or H1
cur loc = L
cur time = T
if n > 0 then
compute ¿0(n) and ¿1(n)
compute speed o from cur loc and prev loc, cur time
and prev time
if o > Vmax then
!n = !n + 1
end if
if !n >= ¿1(n) then
accept the hypothesis H1 and terminate the test
end if
if !n <= ¿0(n) then
initialize n and !n to 0 and accept the hypothesis H0
return;
end if
end if
n = n + 1
prev loc = cur loc
prev time = cur time
5.2. SAMPLE SOURCE CODE:
import java.awt.Color;
import java.awt.Font;
import java.sql.*;
import java.util.Random;
import javax.swing.*;
class MoveNode1 extends Thread {
JDesktopPane jdp;
JPanel lbl;
int packets;
Connection con = DB.getConnection();
String table;
JPanel p;
public String getTable() {
return table;
}
public void setTable(String table) {
this.table = table;
}
MoveNode1(JDesktopPane jdp){
this.jdp = jdp;
}
public void run() {
send();
}
private void send() {
try {
Thread.sleep(500);
ResultSet rs = DB.getResultSet(con,"select * from "+getTable()+" order by node asc");
while(rs.next()){
long startTime = System.currentTimeMillis();
System.out.println(" Start Time "+startTime);
int node = rs.getInt("node");
int xpos = rs.getInt("xpos");
int ypos = rs.getInt("ypos");
int key = rs.getInt("accesskey");
if(Global.ACCESS_KEY!=key) {
DB.executeUpdate(con, "update "+getTable()+" set status='BLOCKED' where node="+node);
UserInterface.baseStationJta.append("Node:"+node+" is blocked \n");
} else {
DB.executeUpdate(con, "update "+getTable()+" set status='VALID' where node="+node);
UserInterface.baseStationJta.append("Node:"+node+" is accepted \n");
}
/*JPanel pan = new JPanel();
pan.setBorder(BorderFactory.createRaisedBevelBorder());
pan.setBounds((UserInterface.baseStationPanel.getX()+100),
(UserInterface.baseStationPanel.getY()+100),
Global.moveNodeWidth,Global.moveNodeHeight);
pan.setBackground(Global.tempNodesColour);
jdp.add(pan);
Thread.sleep(1000);
pan.setBounds(0,0,0,0);
jdp.remove(pan);
*/
/*p =new JPanel();
p.setBorder(BorderFactory.createTitledBorder(BorderFactory.createBevelBorder(1,Global.nodesColour,Color.GRAY),"",2,3,new Font("Arial",Font.BOLD,8),Color.white));
p.setLayout(null);*/
/*int lineNo = 0;
for(int i=UserInterface.baseStationPanel.getY()+100;i<ypos;i++) {
p.setBounds((UserInterface.baseStationPanel.getX()+100),i,Global.moveNodeWidth,
Global.moveNodeHeight);
lineNo = i;
}
for(int i=UserInterface.baseStationPanel.getX()+100;i<xpos;i++) {
p.setBounds(i,lineNo,Global.moveNodeWidth,
Global.moveNodeHeight);
}*/
MoveComponent mv = new MoveComponent(jdp,xpos,ypos,table);
if(mv.checkStatus()) {
}
mv.join();
/*p.setBackground(Global.tempNodesColour);
jdp.add(p);
Thread.sleep(500);
p.setBounds(0,0,0,0);
jdp.remove(p);*/
long endTime = System.currentTimeMillis();
System.out.println(" End Time "+endTime);
System.out.println(" Difference Time ************* "+(endTime-startTime));
}
}
catch(Exception e){
e.printStackTrace();
}
}
}
5.3. SCREEN SHOTS:
CHAPTER 6
CONCLUSION AND FUTURE WORKS
CONCLUSIONS:
In this paper, we have proposed a replica detection scheme for mobile sensor networks based on the SPRT. We have analytically demonstrated the limitations of attacker strategies to evade our detection technique. In particular, we first showed the limitations of a group attack strategy in which the attacker controls the movements of a group of replicas. We presented quantitative analysis of the limit on the amount of time for which a group of replicas can avoid detection and quarantine. We also modeled the interaction between the detector and the adversary as a repeated game and found Nash equilibrium. This Nash equilibrium shows that even the attacker's optimal gains are still greatly limited by the combination of detection and quarantine. We Performed simulations of the scheme under a random movement attack strategy in which the attacker lets replicas randomly move in the network and under a static placement attack strategy in which he keeps his replicas from moving to best evade detection. The results of these Simulations show that our scheme quickly detects mobile replicas with a small number of location claims against either strategy.
FUTURE WORKS:
Using PDF(Probability distribution function) to calculate average speed of each nodes.
Using OLSR(Optimized link state routing) protocol for efficient routing.
Optimized key authentication.
CHAPTER 7
