Cluster based secure authentication technique using ant colony optimization in wireless sensor networks

Abstract

In cluster based wireless sensor networks the existing technique does not offer security to the cluster head. The communication between the cluster head and the sink is not secured. Also, the technique to prevent malicious cluster head is not provided. In this paper, we propose to design a cluster based secure authentication technique based on ant colony optimization in wireless sensor networks. Initially the sensor nodes are authenticated before they are deployment in the network. The authenticated sensor node with maximum energy and trust value is selected as Cluster Head (CH). The distance among the cluster member and cluster head is estimated using the ant colony optimization (ACO) technique. The estimated distance, trust value and energy consumed by each cluster member are taken as input over fuzzy logic technique to select the secure node for data aggregation. The aggregated data is delivered from the CH to the Base Station (BS) attached with a message authentication code (MAC). By simulation results, we show that the proposed technique improves the secured data communication in the network.

Keywords

Ant colony optimization (ACO) technique WSN cluster based secure authentication technique message authentication code clustering

1 Introduction

Sensors are low cost tiny devices with limited storage, computational capability and power. The individual nodes are capable of sensing their environments, processing the information data locally, and sending data to one or more collection points through a wireless link. Sensor nodes are severely constrained by the energy from batteries, which limits the lifetime and quality of the network.

1.1 Wireless sensor networks

Wireless sensor network (WSN) can provide a solution at low cost to variety of real-world problems [1]. In WSN, sensing nodes can be gathered to form clusters. These clusters are fashioned to facilitate scalability and efficiency in the network [2]. Each sensor node contained within computational and communication power [3].

1.2 Applications of sensor networks

The fields of civil and military tasks have more application of wireless sensor networks [4, 5].

Disaster response, health care and intelligent house control are some other applications of WSN [6].

WSN is more useful in hostile environment such as tracking an enemy targets in a battle field [7].

1.3 Attacks in sensor networks

Usually sensor networks are distributed in defenceless fields, this nature of node distribution brings in more security attacks [4].

In cluster based sensor networks, an attacker may mislead the nodes by intimating that the node is far away from the cluster head. This attack could also be done by hijacking the responsibilities of cluster head [2].

In hostile environment, sensor nodes are left uncared after their distribution in the network. This isolation of nodes susceptible to more security attacks [8].

1.4 Cluster based secure authentication

The wireless sensor networks can be congregated to form a number of clusters. Every single cluster includes a cluster head (CH) and encompass of resources such as powerful antenna and data processing capabilities, memory storage and power batteries. Each CH can converse with its cluster members and transmit relay in the middle of connecting cluster members and base station (sink) [6].

For the most part, sensor nodes are useful in critical region applications such as military and health care. Thus, security became a vital role of WSNs. In hostile environments, clustering protocol has to be protected against adversaries because with no trouble they can be misleading the attackers [2].

1.5 Ant Colony Optimization

Ant Colony Optimization (ACO) is an optimization technique, which was fabricated by inspiring foraging behaviour of real ants. It forms solution using artificial ants, which are instructed by pheromone trails and heuristic information. Using foraging behaviour, real ants discovers shortest paths between food sources and their nest. Ants move randomly around their nest. After it attains food, it does evaluation of food based on quantity and quality of food and then brings back food to their nest. While returning to the nest, the ants deposit a chemical substance called pheromone on its path to guide other ants towards food source. By means of this indirect communication, ants discover shortest paths. Similar behaviour is incorporated in artificial ants to resolve optimization problem [9].

1.6 Problem identification

In our previous paper [10], we have proposed a Fuzzy Based Secure Data Aggregation algorithm in Wireless Sensor Networks. This algorithm consists of 3 phases.

In phase 1, the sensor nodes are grouped into various clusters and each cluster has one elected cluster head. The cluster head estimates the distance between each member and itself.

In phase 2, the cluster head determines the trust level of each node by estimating the correctness of data.

In phase 3, Fuzzy logic is applied to select the best nodes for aggregation.

1.7 Drawbacks

The proposed technique does not provide the security to the cluster head. The communication between the cluster head and the sink is not secure.

Authentication is not provided for cluster head so, that the cluster head may become malicious.

We are extending the previous work by providing the security and authentication to the cluster based sensor networks. In our previous work, the nodes are selected based on the distance, power and trust value. For efficient estimation of the distance from the cluster head, we make use of Ant Colony Optimization (ACO) technique.

2 Literature review

Huang Lu et al. [1] have proposed a secure routing protocol for cluster-based wireless sensor networks using id-based digital signature. In their protocol, CHs are elected independently and connected directly to the base station. Sensor nodes are joined to the clusters depends on the strength of transmission signal. In addition, their routing mechanism makes use of ID based cryptography, where users public keys are their ID information, and users can obtain the corresponding private keys without auxiliary data transmission. Therefore, the secure protocol is efficient in communication.

Saswati Mukherjee et al., in paper [6] have introduced a key re-distribution and authentication based technique for secured communication in clustered wireless sensor networks with node mobility. Their approach assures that communication in clustered protocol is secure even when a node moves from one CH to another. Initially, it distributes keys to each node and redistributes the keys when a node moves to the next CH. Their authentication model checks whether the new node is an attacker or intruder.

Meng-Yen Hsieh et al., in paper [7] have proposed a dynamic authentication scheme, this technique does not depend on public-key cryptography to perform entity authentication and achieves asymmetric security properties with low-energy sensor nodes. SecCBSN adapts the TESLA Certificate scheme for dynamic authentication in the primary security module, since symmetric cryptography is most applicable to sensor nodes. SecCBSN is composed of primary security, cluster round, and intrusion detection modules to support secure cluster-based communication in MNs-to-CHs and CHs-to-BS against outside and inside malicious nodes.

Youtao Zhang et al., in paper [8] have studied en-route false report filtering in multipath routing based sensor networks. From their observation, they have identified two faults as node association node manipulation attack and association problems in multipath routing. To alleviate these problems, first they have introduced a method to defend the attack with sufficient information included in the ACK message at the node association stage. Further, they have proposed a resilient interleaved authentication method for multipath routing. Nodes are associated conservatively in multipath routing.

Jinsu Kim et al., in [11] have proposed an energy efficient cluster-based key management method. By means of multiple-key ring, their approach has assigned shared key faster and in more secure way. Further, their method provides energy efficiency, which is a greater advantage of cluster based routing method. This method has handled security and energy at hand and thus improves overall performance of network. It significantly lessens the delay that occurs during cluster formation.

In [12], Abuhelaleh et al. have proposed a cluster hierarchy of wireless sensor networks to a large number of wireless sensor networks. From their focused techniques, they have utilized the famous architecture LEACH. Further, they have also adopted a pair-wise key predistribution technique to facilitate different level of security in WSN. These public and private keys provide higher level of security and sensors are provided with alternative way of key exchange. With these existing phenomenon’s, they have developed a secured architecture called Secure Object Oriented Architecture for Wireless Sensor Networks (SOOAWSN).

Murugan et al. [13] have proposed a cluster based misbehaviour detection and authentication scheme. Their scheme has utilized threshold cryptography technique to provide secure communication. Cluster Head (CH) is responsible for distributing certificates to the member nodes. Using trust counter value of each node, certificate of a node is updated such that it may be renewed or rejected. Further, they have proposed a scheme to detect and isolate the misbehaving nodes.

Sencun Zhu et al. [14] have presented a Localized Encryption and Authentication Protocol, called LEAP. Their LEAP is a key management protocol for sensor networks. It supports four types of keys namely an individual key, a group key, a cluster key and a group key. An individual key is shared with base station; pair wise key is share with neighbouring sensor nodes, a cluster key shared with multiple neighbouring nodes, and a group key that is shared by all the nodes in the network. Their protocol updates key in such a way it lessens communication overhead, delay and energy. Further, source authentication without precluding is used in authentication protocol.

Rui jiang et al. [15] have focused on the methods against the wormhole attack mainly in the data transmission phase. NSDCP1 and NSDCP2 are the two novel secure and dynamic clustering protocols proposed by them to tackle the worm hole attack. The NSDCP1 is suitable where the cluster head would not be compromised. On the other hand, the NSDCP2 is suitable where the base station is secure and would not be compromised, which is the basic assumption and the least secure requirement in the wireless sensor networks. Their approach does not require a node to maintain any timetable.

The smart attacker model is proposed in [16] by Roberto Di Pietro et al. This model has used threat model to provide communication confidentiality in wireless sensor networks. Their model uses node information to select the best sensor to tamper with in order to compromise the communication confidentiality. Further, they have proposed a novel Efficient and Secure key Predeployment scheme (ESP). During key discovery phase, their ESP requires minimal energy and guarantees high-level resiliency against the smart attacker.

3 Cluster based secure authentication technique using ACO

3.1 Overview

In this paper, we propose to design a cluster based secure authentication technique using ant colony optimization in wireless sensor networks. Initially the sensor nodes are authenticated using dynamic authentication technique before they are deployment in the network. The authenticated sensor node with maximum energy and trust value is selected as CH. The distance among the cluster member and cluster head is estimated using ant agents. The estimated distance, trust value and energy consumed by each cluster member are applied as input to the fuzzy logic technique to choose the secure node member for data aggregation. The aggregated data is delivered from the CH to the BS attached with a message authentication code (MAC) using the CH key. This offers authentication for each of the data from CH.

3.2 Authentication of the sensor nodes

At first the sensor nodes are authenticated using dynamic authentication technique before they are deployment in the network. The authenticated sensor node with maximum energy and trust value is selected as Cluster Head. The distance among the cluster member and cluster head is estimated using ant agents. Thus the procedure of authentication of the sensor nodes are as follows

Procedure:

Let BS represent the base station

Let (SK₀ ← SK₁ ← SK₂ …… ← SK_n) be the key chain adopted by BS.

Let MAC be the message authentication code

Let T_e be the expiration time

Let ID be the nodes identity

Let F_pi be the private function selected from system function set F_i.

Let K_i represents individual key information whose format is as follows $K_{i} : [F_{pi}] [{ID}_{i}]$

Let D represents the data

Let Cert be the certificate for the sensor node organized by BS whose format is as follows. ${Cert}_{i} : [ID] {E [{SK}_{0}^{i} (K_{i})] [T_{e}] [D_{prev} ({SK}_{i}) MAC]}$

BS divides the lifetime of the network (LT) into multiple time intervals (t) followed by the assignment of different system keys (K) at different time t. The process involved in the authentication of the node is illustrated in the following steps

1) In prior to the process of deploying the node in the network, BS assigns each N_i with an individual key chain <SK_i > , F_pi, K_i and Cert_i.

2) The K_i and dedication of SK_i of N_i are encrypted by Cert_i as per the deployment time of N_i.

If N_i is deployed at the time interval (t-1)

Then

Cert_i is applied with SK_t

End if

3) Subsequently, N_i broadcast its Cert_i to its neighboring nodes N_nei $N_{i} \overset{C_{i}}{\to} N_{nei}$

4) Neighbouring nodes upon receiving the certificate verifies expiration time.

If Cert_i exceeded T_e

Then $N_{nei} stores {Cert}_{i} .$

End if

5) At the commencement of time interval t, the entire nodes receive SK_i which is disclosed from the BS.

6) The nodes utilize SK_i to verify the MAC of Cert_i and decrypts K_i and dedication of N_i.

7) N_j generates a pair wise key K_ji using F_pj and K_i whose evidence (EV) is transmitted to N_i.

The format of EV is as follows $EV : {[Fpj] [IDi] [{SK}_{0}^{j}] [{Cert}_{i}]}$

8) N_i upon receiving EV derives pair wise key K_ji and acquires the authenticated ${SK}_{0}^{j}$ .

9) As a result of step 8, when SK_i is revealed, N_j authenticates N_i, establishes the pairwise key and dedication of shared key among each other.

3.3 Clustering

The authenticated nodes select the cluster head (CH_p) based on the energy and trust value [10]. The nodes which possess higher trust value and maximum energy are initially selected as CH_p. These cluster heads (CH_p) then broadcast an advertisement message to all its surrounding nodes. The advertisement message includes the CH (ID) appended with application packet (APP) and message authentication code (MAC). The packet is authenticated through the MAC code. The non-cluster head nodes first record all the information from cluster heads within their communication range.

Each non-cluster head node chooses one of the strongest Received Signal Strength (RSS) of the advertisement as its cluster head and transmits a member message back to the chosen cluster head. The information about the node’s capability of being a cooperative node, i.e., its current energy status is added into the message. The message also, includes information related to consistency value, consistent sensing count and inconsistent sensing count of the node.

If an advertisement message signal is obtained at a CH_p from another CH_q, which has the RSS value greater than a threshold then CH_q will be considered as the neighbour cluster head and the ID of CH_q is stored.

The Fig. 1 shows the cluster formation process, in which the cluster head, cluster member and data delivery process is shown.

3.3.1 Secure communication within clusters

The following steps describes the secure communication among the cluster member and CH,

1) The cluster member node (CM_j) will generate a temporal pair wise key K_CMj,CH and shares with its CH.

2) Using K_CMj,CH, CM_j transmits sensed data to the CH with a Message Authentication Code (MAC) during the time t_i. ${CM}_{j} \overset{K_{{CM}_{J}}, CH [D] [MAC]}{\to} CH$

3) Each CH aggregates the sensed data from its members and delivers it to the BS at the end of the delivery phase.

4) The aggregated data is delivered from the CH to the BS is attached with a MAC code using the KCH. This offers the authentication for each of the data from CH.

3.4 Ant based distance estimation algorithm

Let CM_i and CH be the cluster member node and cluster head respectively.

Let FA and BA be the forward and backward ants respectively.

The distance of the sensor node from CH_p is estimated using the ant colony optimization technique. This involves the following steps.

1) Initially FAs are launched in CM_i and it traverse through each node in the path towards CH_p.

2) The probability by which FA located at CM_i moves to next hop node CM_j among the neighbours is given using the following Equation (1). $P_{a} (i, j) = {\frac{μ_{ij}^{δ} / D_{ij}^{γ}}{\sum_{x \in J (i)_{a}} μ_{ix}^{δ} / D_{ix}^{γ}}}$ (1)

δ= virtual significance of pheromone trial

γ= virtual significance of the distance

x = neighbour node of existing node i

D_ij = potential of the node (Represents the shortest distance towards the next hop node)

3) FA upon reaching CH_p transfers the collected status of all the nodes into BA which is generated in CH_p.

4) BA traverses the similar path travelled by FA but in the opposite direction. In the reverse path, BA updates the pheromone track and D_ij. The equations concerned with this pheromone track is as follows $μ_{ij} = (1 - f) μ_{ij} + f * g / TC$ (2)

Where f and g are the parameters of Ant colony optimization.

TC represents the total cost acquired from first path.

5) The shortest distance from CM_i to CH_p is estimated based on the following equation. $D_{ij} = α * C_{r} + β * C_{D} + σ * C_{c}$ (3) where α and β are the weight factors for choosing route to CH_p.

σ represent the weight factor for selecting data link.

3.5 Fuzzy logic based secure data aggregation

Three stages are involved in the fuzzy rule based inference algorithm.

Fuzzy matching: the degree to the input fundamental steps and condition of the fuzzy logic are determined.

Inference: on the basis of the degree of match, the conclusion of the rule is determined.

Combination: the result obtained by every fuzzy rules are merged together into a single overall result.

Rule definition: A fuzzy set A in X is characterized by a membership function which are easily implemented by fuzzy conditional statements. In the case of fuzzy statement if the antecedent is true to some degree of membership then the consequent is also true to that same degree.

The rule structure: If antecedent then consequent.

The rule: If variable 1 and 2 are low and variable 3 is high then output is benign else output is malignant.

The fuzzy Logic in decision making uses the following technique.

In this study, the fuzzy if-then rules consider the parameters: distance, power consumed and trust for evaluating the nodes. For the three inputs: distance (Estimated based on section 3.3.1), power consumed and trust, the resulting possibilities are Best Node (BN), Normal Node (NN) and Worst Node (WN). Here the inputs can take 2 values Less and High. Hence the total number of outputs in this case is 2³ = 8.

The selection criterion is such that a node should have lower distance and power consumption values but with high trust value.

The first parameter, distance D can be represented as a fuzzy set as:

Distance, D = FuzzySet[{BN, a}, {NN, b}, {WN, c}]

Where:

a = The membership grade for Best Node in Distance calculation

b = The membership grade for Normal node in Distance calculation

c = The membership grade for Worst node in Distance calculation

The second parameter, power consumed P can be represented as a fuzzy set as:

Power consumed, P = FuzzySet[{BN, e}, {NN, f}, {WN, g}]

Where:

e = The membership grade for Best Node in the calculation of power consumption

f = The membership grade for Normal node in the calculation of power consumption

g = The membership grade for Worst node in the calculation of power consumption

The third parameter, trust T can be represented as a fuzzy set as:

Trust, T = FuzzySet[{BN, u}, {NN, v}, {WN, w}]

Where:

u = The membership grade for Best Node in trust calculation

v = The membership grade for Normal node in trust calculation

w = The membership grade for Worst node in trust calculation

The final decision is made on the basis of the output of the intersection of the corresponding members of the fuzzy sets of the three parameters; distance, power consumed and trust value.

The resultant of the system is the one with the high membership grade. Table 1 shows the conditions for decision making in fuzzy logic for inputs and its corresponding results. The Fig. 2 shows the block representation of the decision making in our fuzzy system.

Let distance, trust and power consumed be denoted by D, T and P:

The if-then rule simplifies this as the following.

If D and P are less and if T is high then node is a best node.

If D is less, P is high and T is high then node is a normal node.

If D is high, P is less and T is less then node is a normal node.

If D is less, P is less and T is less then node is a normal node.

If D is less, P is high and T is less then node is a worst node.

If D is high, P is less and T is less then node is a worst node.

If D is high, P is high and T is high then node is a worst node.

If D is high, P is high and T is less then node is a worst node.

Defuzzification: Defuzzification of the fuzzified values can be carried out by several techniques such as centroid average method, max centre method, mean of maxima, smallest of maximum and largest of maximum. In our case, we defuzzify using the maximum method. After decision making on the basis defuzzification, the normal and the best nodes are selected by the clusterhead for data aggregation whereas the worst nodes are neglected by the cluster head. Then the clusterhead transfers the aggregated data to the destination i.e., sink (BS).

3.5.1 Advantages

Since the malicious nodes are initially authenticated, based on the authenticated sensor node the CH is formed. Hence, there will be no chance of CH becoming malicious.

The key management technique offers authentication for each of the data from CH.

The communication among the CH and BS is secured and authenticated.

4 Simulation results

The performance of Cluster Based Secure Authentication Technique Using Ant Colony Optimization (ACBSA) is evaluated through NS2 simulation. A random network deployed in an area of 500×500 m is considered. Initially 30 sensor nodes are placed in square grid area by placing each sensor in a 50×50 grid cell. 4 phenomenon nodes which move across the grid (speed 5 m sec) are deployed to trigger the events. 4 cluster heads are deployed in the grid region according to our protocol. The sink is assumed to be situated 100 meters away from the above specified area. In the simulation, the channel capacity of mobile hosts is set to the same value: 2 Mbps. The simulated traffic is CBR with UDP source and sink. The number of sources is fixed as 4 around a phenomenon. We vary the number of attackers per clsuter from 1 to 4. Table 2 summarizes the simulation parameters used.

4.1 Performance metrics

The performance of ACBSA technique is compared with the Fuzzy Based Secure Data Aggregation Technique (FBSDA) technique [10]. The performance is evaluated mainly, according to the following metrics.

Average Packet Delivery Ratio: It is the ratio of the number. of packets received successfully and the total number of packets transmitted.

Packets Received: It is the number of packets received by the sink successfully.

Packet Drop: It refers to the no. of valid packets dropped due to malicious nodes.

Energy: It is the average energy consumed for the data transmission.

4.2 Results and discussion

In our first experiment we vary the transmission rate of the sensors as 50, 100, 150, 200 and 250 kb keeping the number of attackers as 2 per cluster

A. Results based on Rate:

The increase in data sending rate results in more traffic and hence packets will be dropped due to congestion in addition to attacks. Figure 4 shows the effect of packet drop on both the schemes. We can see that the drop occurred in our proposed ACBSA is less when compared to existing FBSDA technique, since the chances of attack is less in ACBSA because of the authentication of nodes.

Since packet drop increases, the packet delivery ratio and packets received will decrease as the rate is increased. Figures 3 and 6 show the results of packet delivery ratio and packets received for both the schemes, respectively. From that we can observe that ACBSA has more packets received and high delivery ratio. This is because of the fact that aggregation is done based on distance and the cluster head attacks are reduced.

Because of the reduction in packet received, the energy consumption tends to reduce as the rate is increased. From Fig. 5, we can see that the energy consumption of our proposed ACBSA is less than the existing FBSDA technique.

B. Results based on Attackers

In our second experiment, we vary the number of attacker nodes per cluster from 1 to 4.

The increase in attacker nodes results in more packet drops due to the attacks. Figure 8 shows the effect of packet drop on both the schemes. We can see that the drop occurred in our proposed ACBSA is less when compared to existing FBSDA technique, since the chances of attack is less in ACBSA because of the authentication of nodes.

Since packet drop increases, the packet delivery ratio and packets received will decrease as the number of attackers is increased. Figures 7 and 10 show the results of packet delivery ratio and packets received for both the schemes, respectively. From that we can observe that ACBSA has more packets received and high delivery ratio. This is because of the fact that aggregation is done based on distance and the cluster head attacks are reduced.

Because of the reduction in packet received, the energy consumption tends to reduce as the rate is increased. From Fig. 9, we can see that the energy consumption of our proposed ACBSA is less than the existing FBSDA technique.

5 Conclusion

In this paper, we have proposed to design a cluster based secure authentication technique based on ant colony optimization in wireless sensor networks. Initially the sensor nodes are authenticated before they are deployment in the network. The authenticated sensor node with maximum energy and trust value is selected as CH. The distance among the cluster member and cluster head is estimated using the ant colony optimization (ACO) technique. The estimated distance, trust value and energy consumed by each cluster member are taken as input over fuzzy logic technique to select the secure node for data aggregation. The aggregated data is delivered from the CH to the BS attached with a message authentication code (MAC). By simulation results, we have shown that the proposed technique improves the secured data communication in the network by attaining increased packet delivery ratio with reduced energyconsumption.

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