Bio-Inspired Machine Learning Based Wireless

Sensor Network Security

Heena Rathore leT, Research Scholar

lIT, Rajasthan heena7sept@ iitj . ac .in

Abstract-Exploring the symbiotic nature of biological systems can result in valuable knowledge for computer networks. Biolog­ically inspired approaches to security in networks are interesting to evaluate because of the analogies between network security and survival of human body under pathogenic attacks. Wireless Sensor Network (WSN) is a network based on multiple low­cost, low-energy sensor nodes connected to physical signals. The network is made up of sensor nodes and gateways, where the server nodes acquire physical world data, while the gateway forwards the data to the end-user. While the spread of viruses in wired systems has been studied in-depth, applying trust in wireless sensor network nodes is an emerging area. This paper uses machine learning techniques to first dif1"erentiate between fraudulent and good nodes in the system. Next, it derives inspiration from the human immune system to present an idea of virtual antibodies in the system, to disable the fraudulent nodes in the system.

Index Terms-Biologically Inspired, Machine Learning, WSN, Human Immune System, Security.

I . INTRODUCTION

Study of nature means exploring, analysing and investigat­ing the physical world around us. It encompasses study of living bodies which grow, respire, need energy and evolve. Detailed study in this area shows how everything in nature is structured to be hierarchical, adaptive and synchronized in space and time. For example, plants perform photosynthesis, bees search for nectar, birds flock together in a synchronized fashion and the sun rises and sets in a specific way. There are lots of things that researchers can learn from nature and use as a source of inspiration for solving many of the challenges in man-made systems . When we talk about biologically inspired systems, we demonstrate a strong relationship between a computer system and biology, which tries to solve a specific problem in computer domain with biological solution which follows a similar procedure or has similar capabilities . At this point of time, it may be good to answer a very basic question: Why should we look at biology as a source of inspiration? The answer to this question lies in many characteristics of these systems[ 1 ] , such as:

• Biological systems are adaptive to their environment which ensures their survival in the harshest conditions .

• They have a proven capacity to heal, remain strong, and be resilient against failures caused by many factors .

• They are able to perform and accomplish very intricate tasks using a limited set of basic rules.

978- 1 -4799- 14 1 5-9/1 3/$3 1 .00 20 1 3 IEEE 140

Sushmita Jha Assistant Professor

lIT, Rajasthan sushmitajha@iitj . ac.in

• They are efficient in learning, resolving and regenerating themselves when exposed to new conditions.

Over the recent years, there has been a paradigm shift in the development of computer networks ; from monolithic, centralised systems to independent, distributed, self organised systems . Due to this, it is imperative for these distributed systems to have the ability to adapt and organise in the changing world. In addition, they have to address numerous other challenges [2] , some of which are listed below

• Today's networks are highly dense due to strong inter­connectivity. Hence, the size of the network is a major challenge. Since the system is open, any number of nodes can be added onto it. The network should be scalable in a way such that, one can acquire large scale networking while performing normal system functions .

• In the early communication system where there was only a single receiver, transmitter and a communication channel, the system was static. Such static networks did not have to deal with varying dynamics of the system. However, today 's dynamic networks have to deal with varying behaviour resulting from traffic, bandwidth, chan­nel and network conditions .

• Resources need to be effectively used and managed, so that the network is cost effective.

• Today's networks must have the capabilities of self orga­nization, self-evolution and survivability.

If one looks at the characteristics of biological systems and the challenges faced by distributed network systems, it is pretty evident that one can apply bio-inspired techniques to solve these challenges[3] . The objective of this paper is to present the design of a security system for Wireless Sensor Network (WSN) using human immune system as inspiration. Section II describes the human immune systems and explains the concept of T-cells and B-cells in our system. Section III describes the security issues in WSN [4] and how some of the immune system concepts can be used to protect against such threats. Section IV shows initial results from using machine learning algorithms based on K-means and Support Vector Machine (SVM) for distinguishing between fradulent and good nodes. Section V summarizes the paper and presents scope for future work.

II . HUMAN IMMUNE SYSTEM

Biological immune systems have intelligent capabilities of detecting antigens (foreign bodies in the system) in the body. As shown in Figure 1 , inunune system can be classified as two types, innate and adaptive. Innate immunity is the first

Fig 1 Human Immune System

I m m u ne System

� � I n n ate I m m u n e Ada pt i ve

System I m m u ne System

/ � / � T·Ce l l s {Ce l l ·

B-Ce l l s (H u mo ra l Physi ca l Ba rri e rs B l ood Borne M e d i ated

I m m u n ity) I m m u n ity)

I-- S k i n

I-- M u c u s

I-- Sa l iva � Tea rs

lie of defense for pathogens. It is non-specific and is meant for rapid detection and elimination of pathogens . It generally refers to non-specific defence mechanisms that come into play within hours of an antigen's appearance in the body. It is referred to as non-specific defence mechanism since it is not designed for any specific pathogen. It can be further classified as physical barriers and blood borne. Physical barriers, such as skin, tears, saliva and mucus, stop pathogens from entering the body[5] . If pathogens manage to get past the physical barriers, blood borne body cells come into picture. Their response will also be non-specific . This process, called phagocytosis, is carried out by a number of different phagocytes, the most common types being the neutrophils and macrophages. For example, neutrophil has protein molecules on their cell walls that help them in identifying foreign particles. Once foreign particles are identified, it will attach to the pathogenic wall, thus engulfing it, and enclosing the pathogen in the vacuole. Pathogens containing vacuoles fuse with the lysosome that contains digestive enzymes. Macrophages perform the same task outside blood vessels, so that pathogens can be removed from tissue. If the innate immune system cannot remove the pathogen, then the adaptive immune system takes over. Adaptive immune system is made up of a network of cells, tissues, and organs that work together to protect the body. The cells involved are white blood cells, or leukocytes, which come in two basic types, phagocytes and lymphocytes . Classification is depicted in Figure 2. Phagocytes have already been discussed in innate ilmnune system. Lymphocytes are of two types, namely T-cells and B-cells . Leukocytes are developed from undifferentiated stem cells in the bone marrow. Lymphocytes start out in the bone marrow, stay there and mature into B­cells . Alternatively, they leave for the thymus gland, where

they mature into T-cells . They are called T-cells because the latter stages of their development occur in the thymus. Spleen, bone marrow and thymus are also called as lymphoid tissues. Lymph nodes are specialized tissue harbouring cells of the innate immune system called lekocytes and macrphages, in addition to specialized cells of the adaptive immune system; T and B cells . These nodes are connected by the lymphatic circulation of the body and help the 2 arms of our immune system coordinately fight a pathogenic attack.

Fig. 2 . Classification of Leukocytes Leu kocytes

(Wh ite B l ood

Ce l l s)

P h a gocyctes Lym phocytes

M a cropha ges

Neutro p h i l s T-Ce l l s (Ce l l ­

M e d i ated

I m m u n ity)

B-Ce l l s ( H u mo ra l

I m m u n ity)

H e l pe r T-Ce l l s

K i l i e r T-Ce l i s

The adaptive immune system consists of two complementary systems, namely cellular immune system and humoral immune system. The humoral immune system is aimed at bacterial infections and extracellular viruses, but can also respond to individual foreign proteins. This system contains soluble proteins called antibodies which bind bacteria, viruses, or large molecules identified as foreign and target them for destruction. Antibodies are produced by B-cells . Antigens are secreted by the pathogens which causes the immune system to respond. B-cells produce and secrete antibodies after they encounter antigens . The cellular immune system destroys host cells infected by viruses and also destroys some parasites. The agents at the heart of this system are a class of T-cells . B­cells are like the body's military intelligence system, seeking out their targets and sending defences to lock onto them[6] . T-cells are like the soldiers , destroying the invaders that the intelligence system has identified. T-cells are broadly of two types, namely Helper T-cells and Killer T-cells . Killer T-cells interact with infected host cells through receptors on T-cell surface. Helper T-cells interact with macrophages and secrete cytokines that stimulate killer T-cells, helper T-cells , and B­cells to proliferate and produce antibodies specific to the pathogen. Mathematical Model In 1977, Dibrov' s et al. devised a model to study the rate of change of antibodies and antigen. Dibrov Model consists of three coupled equations for the antibody quantity a, the antigen quantity g, and the small B cell population x [7] ,Since x is generally considered as a constant, the rate of change of x is zero and the third equation is ignored. Now consider the

2013 World Congress on Nature and Biologically Inspired Computing (NaBIC) 14 1

set of equations describing antigen-antibody interactions : dg dt

= Kg - Qag ( 1 )

da dt

= AH(t - T)g(t - T) - Rag - Ea (2)

where Equation 1 and 2 are the rate of change of antigen and antibody respectively. Also K, Q, A, R, E are rate constants . K is the overall growth rate of antigen. H(t) in equation 2 is the Heaviside step function whose value is zero for negative argument and one for positive argument.

H(t) = 0 , t < 0

H(t) = I , t ?: O

(3) (4)

The product 'ag ' is the complex formed as antibody-antigen complex. As the complex is formed, it results in net loss of the antibody and antigen. The simplest assumption is that of the law of mass action, valid when the densities are below a saturation level, that is that the losses are proportional to the product of the antibody and antigen densities. The rate constants Q and R are necessarily not same. The rate of antibody production at time t is supposed proportional to the rate of small B cell stimulation at time t - T: that is, there is a delay T between stimulation of a small B-cell and the subsequent production of plasma cells from it. When simulations were carried out using the Runge­Kutta(variable) method for solving the differential equations, following results were seen as shown in the Figure 3. It shows

1 .2 1

., 0 .8 -u :J { 0 .6 E

q 0 .4 0 .2 0 I

a

Fig. 3. Rate of change of antigen and antibody ,-----::=::--. Anti b o dy l/'vl

Anti g en 1/\/1

I I I I 500 1000 1500 2001 Ti m e

the graph of rate of change of antigen and antibody as a function of time, for values of K = 0 .01 , Q = 1 , A = 1 , R = 1 , E = 1 with initial conditions ao = 0 and go = l . This shows that the antigen count linearly increases and when the body comes to know about it, the B-cells start producing antibodies and when the antigen antibody complex is formed, the count of antigen decreases linearly and rate of change of antibody becomes constant. Future work would be aimed at usage of this similar equation for formalizing the concepts of virtual antibodies described in this paper.

III . MITIGATION AGAINST SECURITY THREATS

This paper explores the opportunities in biological systems and translate them as solutions that work in distributive manner

in resource constrained wireless sensor networks. Sensor nodes acquire data and send them to the gateway in a wireless fash­ion. Since the sensor nodes are remotely located, it is possible that someone tampers with the physical signals connected to the sensors . Figure 4 shows three possible scenarios of data being reported by a sensor node which is measuring temperature of a remote site. The plot Normal Stable reflects normal temperature

45 40

� 35 " � 30 � 25 � 20 f-

15 10

-

-

I I o

Fig 4 Fraudulent Data

/ � VI Iv .--'\ I

\ T \ I I \1/ \

I I I I I I

Tim e

N o rm a l Sta b l e � N o rm a l Ri si n g 161 Fra u d u l ent

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behaviour under stable conditions . The plot Normal Rising reflects normal behaviour in a situation where the temper­ature of the remote site is increasing. The plot Fraudulent reflects possible fraudulent behaviour. While in the first two scenarios, the trust factor associated with the node should be high, it should be low for the third scenario. A second example of malicious behaviour can occur when the node is communicating the data. It is possible that the node can start misbehaving while forwarding data, can become selfish or can all together exclude the data[8] . Detection of such fraudulent nodes become mandatory in such type of networks . Architecture of wireless sensor network can either be a flat configuration or clustered configuration[9] . This paper fo­cusses on clustered approach having sensor nodes, cluster head and gateway, as shown in the Figure 5 . While the spread of viruses in wired systems has been studied

M ...... St.lI ....

Flat Orga nisation

Fig. 5. Architectures in WSN � .-.. "" � /�� � , -.

. ......... J � Cl ustered Orga nisation

in-depth, applying trust in wireless sensor network nodes is an emerging area[ 1 1 ] , [ 12] , [ 14] , [ 1 5 ] , [ 16] . Let us assume that there is one cluster head for every n sensor nodes in the system. Our research suggests that the cluster head will keep history of the previous data received from these n sensors. Based on statistics, cluster head will generate virtual antibodies for nodes as described by Equation 1 and 2. At initialization,

142 2013 World Congress on Nature and Biologically Inspired Computing (NaBIC)

all trust factors maintained at gateway are maximum (highly trusted) . As the statistical variation changes , cluster nodes modify the virtual antibodies . In essence, the cluster heads act as B-cells in the human body. Gateways will observe the trust factors coming from the b-cell nodes and then make a decision whether or not to accept the data [ 10] . When the trust rating falls below a certain threshold, gateways will decide to attack the fraudulent node; a behaviour similar to the T-cell node trying to engulf infected cell. They do so by sending a signal to disable the radio on the fraudulent node. In this scenario, the gateway acts as the lymph node in human body. Table I shows a one to one mapping between the immune system and our proposed security system. Cluster head mimics B-cell as

TABLE I RELATIONSHIP BETWEEN IMMUNE SYSTEM AND WSN

Immune System I WSN

B-cells Cluster Head Lymph Node Gateway Nodes Antigen Corrupted data from nodes Antibody virtual antibodies Normal cells in human body Sensor nodes T-cells attacking the infected cells Disable radios on fraudulent nodes

it produces virtual antibodies , similar to the B-cells producing antibodies . Lymph node is the store house area where T-cells and B-cells interact with each other. Based on the ratings received from cluster head, gateway makes a final decision on whether the data received from the sensor nodes is malicious or benevolent. Finally gateway disables the radio signal from the malicious nodes, just like the T-cell engulfs the infected cell from the antigen and kills it by secretion of factors .

IV. MACHINE LEARNING BASED B IOLOGICALLY INSPIRED MODEL

Machine Learning Based Biologically Inspired Security model for WSNs can be divided into three essential blocks . Flowchart for the same is as shown in the Figure 6. First

Fig. 6. Trust Reputation Model

Machine Lea rning

Algorith m to claSS ify

data I nto fra u d u lent

or benevolent

Generation of

v irtua l antibodies

Trust Ratings either

O orl

classification is done to distinguish the data received from the sensor nodes as or fraudulent data. After this initial classifica­tion of data using machine learning algorithms, cluster head will generate virtual antibodies with the help of Equation 6. Cluster heads will then transmit these virtual anti-bodies to the the sensor nodes. This process will continue for sometime, and finally, gateway will generate trust rating for the nodes. These

trust ratings will be used to disable the fraudulent nodes by sending a signal to disable them.

A. Intrusion Detection Machine Learning Algorithms Intrusion comes into picture as and when the attacker breaks

into computer system by exploiting the piece of system and gaining the access to data [ l 3] . IDS act as a second line of defence just like human immune system second line of defence when intrusion prevention scheme (first line of defence) like authentication and encryption fails . Intrusion Detection System is a system that dynamically monitors the events taking place in the machine just like our lymphatic system. It checks the network traffic and decides whether these are symptoms of an attack or not. It has two major components misuse detection and anomaly detection.

• Misuse Detection : It matches new observations with the signatures stored in the database.

• Anomaly Detection: It detects abnormal activities from a predefined normal profile in order to identify possible attack. It can be supervised (having prior knowledge of the classes), semi-supervised (knowledge of one class) or unsupervised (No knowledge) .

Machine learning is one of the intrusion detection system. Machine learning techniques develop algorithms for making predictions from data, to develop a model for accomplishing a particular task. Tom Mitchell described it as making a machine learn with time. A computer program is said to learn from ex­perience E with respect to some task T and some performance measure P, if P improves in direct proportion with E [ 17 ] . There are two broad algorithms for anomaly detection that makes machine learn: unsupervised and supervised.

1) Unsupervised Learning: Unsupervised learning works on principle of finding a structure out of an unlabelled data set. K-means algorithm groups data to make clusters [ 1 8] . It has two major tasks :

• Cluster assignment: Assign random centroids , for sim­plicity take 2 centroids, and find the distance of each data point with the two centroids and assign each data point to the nearest centroid.

• Move centroids by taking the average of all points pointing to each centroid and then move each of these to the average position.

Repeat the above two steps till convergence.

Algorithm for K-means:

Input: k(Number of clusters), Training set(x ( 1 ) , X(2) . . . . . x (m» ) , where X(i) E R(n)

Procedure: Randomly initialize k cluster centroids j.L I , j.L2 . . . . . . . . j.LK , repeat [ for i=l to m d =index from 1 to k of cluster centroid closest to Xi

(5)

2013 World Congress on Nature and Biologically Inspired Computing (NaBIC) 143

for k=l to k J.Lk = average(mean) of points assigned to cluster k. J

Figure 7 first part shows the random data and second part shows two clusters generated out of random data. Fraudulent data cluster is shown as green in color and good data cluster is shown as blue in color.

Fig. 7. Learned Data from K-Means Algo

Centro i d� � Ne' gbh ourho od Graph U C lass O �, ". Data I Data Ii:!l I learned O ata Fro m K - M ea n s C l a ss i II - I 15 .0 -

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2) Supervised Learning: Classification of data comes under this category and it is used when we are given labelled data and we need to describe pattern and create decision boundary. Support vector machine(SVM) is a popular tool used to classify the data and create a decision boundary to distinguish between fraudulent and good data. So the data which was classified into clusters from k-means algorithm when given to support vector machine creates the decision boundary as shown in the Figure 8. As seen from the figure, red is the fraudulent data and green is the good data. Now as new data is received, if it lies in the red region , it is considered as fraudulent, else it is considered as good data. All the simulations were done in LabVIEW 2012 environment.

3) Anomaly Detection Algorithm: SVM creates the deci­sion boundary however the data which lies on the boundary needs to be further evaluated for better accuracy and preci­sion. Anomaly detection thus helps in making more accurate decisions. So the data which is on the boundary of benevolent region could be given more time to analyse. The algorithm works in following manner.

• After having done support vector machine implementa­tion we can choose features Xi that might be indicative of anomalous examples for clear cut demarcation between malicious and fraudulent data.

• Calculate the Gaussian distribution by computing mean and standard deviation of the features . In our case we have two features . Figure 9 showing the data features x and y.

Fig. 9. Data Read;::i""ng;,:;s::--',........n Data Po ints I P l ot 0 I. · .1 I 8 .0 -,--,-----,---,-.,--..'::;::::':::;:=-;u 7 .0 -1--+---+-+---1---+-+----i • . O -I-_+_--+-+--+--+-t----i 5.0 -1--+---+�.f--+__+-+-___1 4.0 -1--+---+----;.---1---+-t----i

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.2 .0

.i.o 0 ,'0 2 .'0 4 .'0 6.'0 8 �O 1(}.O 12'.0

• Given new example x, we compute p(x) .

( ) rrn ( 2 ) rrn - (Xj - J.Lj ) 2 p x = j= lP Xj , J.Lj ' O'j = j= l exp 2 2 O'j

(8) The probability distribution function of x and y is as shown in Figure l O.

The combined probability distribution function of x and y is

PDF of X 0.2

� Ol

'"

Fig. 10 .

v--"" II 1\ /

II

Probability Distribution Function I Pl ot O N I PDF of Y 'I P"',,,,"o -=n

\ 1\

"" 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0

as shown in the Figure 1 1 .

p(X) = p(X , J.Lx , O'� )p(y , J.Ly , O'� ) (9)

Threshold E is maintained and if the probability of new data point < E, it is considered as anomaly otherwise not. Paper aims in detection of anomaly in wireless sensor net­work with the aid of machine learning classification, thus development of novel intrusion detection system. Flowchart describing machine learning based biological intrusion system

144 2013 World Congress on Nature and Biologically Inspired Computing (NaBIC)

0.06 .� 0.05 « N 0.03

Fi . I I . Combined PDF X Axis

is as shown in the Figure 12 [ 1 9] .

' . , � N

Packets are captured and are checked whether they lie in the

Fig. 12 . Intrusion Detection System Ca ptu red Pa ckets

good region or fraudulent region using SVM and K-means algorithm. For better accuracy and precision it is then passed through anomaly detection algorithm for boundary values of good and fraudulent data. If found anomalous, then trust development module comes into picture and virtual antibodies are produced with the help of differential equations 1 and 2. Finally in the end the gateway would turn off the signal of fraudulent node.

V. CONCLUSION

This paper described the human immune system, specifi­cally focussing on the adaptive immune system consisting of the T-cells and B-cells . Aim was to derive inspiration from these cells to design a security system for next generation wireless sensor network (WSN). Objective was to define the combination of cluster heads and gateway as the lymph nodes in human body. Cluster heads will calculate the virtual antibod­ies for all the sensor nodes connected to it thereby transmuting these ratings to the gateway. Gateway will then initiate action to disable the radio on that node by production of trust ratings

either 0 or 1 . Our aim is to build a system to implement the mathematical model and evaluate the performance of our system under real-world conditions. Scope of work would be the development of virtual antibodies with the help of Dibrov differential equations and its significance on the trust ratings .

ACKNOWLEDGEMENTS

This work was carried out under the National Instruments PhD Sununer Internship Program under the supervision of Abhay Samant. Authors would like to thank Mr. Abhay Samant, Mr. Chinmay Misra for their help with the simulations performed using National Instruments LabVIEW 20 12 .

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2013 World Congress on Nature and Biologically Inspired Computing (NaBIC) 145

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146 2013 World Congress on Nature and Biologically Inspired Computing (NaBIC)