Keywords

1 Introduction

Wireless sensor network (WSN) contains a varied size of arranged interconnected nodes that forms cells for information dissemination (transceiver). WSN is a profoundly used system with application in numerous territories such as social insurance tracking, territory checking world and it environ sensing its observation likewise. Information received via sensors are managed by the cells. WSN utilization within a domain can either be genuine or rather without assurance [1]. The idea behind WSNs can be said to amount to its defenceless nature against numerous security dangers of various sorts and reason. With the straight forward idea of their directing strategies, security serves as the best test as they are increasingly defenceless to different system threat or exploits, of which can lead to sinkhole exploit in a corporation, priority forwarding, wormhole exploits, hello-distributed attack, Sybil-attack, node replication exploit, as well as black hole [2]. Sinkhole attack is considerable as it can prompt each other attack referenced previously. Figure 1 shows a run of the mill WSN system and all it contains.

Fig. 1.
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Node sensor architecture

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The military, as well as restorative exploration, are known commonly to utilize the functionality of sensor networks for the evaluation purpose, inclusive are threat tracing, conflict spots-review, as well as trespasser, acknowledge, WSN regularly explores threatening as well as the remote destination. Along these lines, there is a firm prerequisite for guaranteeing the distinguishing of data and recognizing readings. In detached circumstances, an interloper not only can tune in the correspondence, yet besides, the gatecrasher can get or meddle with the exchanged messages. Along these lines, various estimations, and shows don’t work in hostile conditions without adequate wellbeing estimates set up. Thusly, security winds up as one of the most significant concerns while organizing security shows in resources constrained to work in WSNs. A piece of the employments of WSNs is for combat area observation, clinical administration applications, nature watching, keen home, and vehicular exceptionally designated frameworks VANETs.

An improved Ant Colony Optimization (ACO) proffered in this study through the incorporation of a hash table in the typical ACO order to improve detection rate (DR) as well as decrease false alert rate (FAR) in Sinkhole Attack Detection. The key contributions of this study are hereby highlighted or outlined:

  • Design an “Enhanced Ant-Colony Optimization Tech (EACO)” used for sinkhole attack detection.

  • Develop an EACO using DR and FAR.

  • Evaluate the EACO comparatively with existing results in previous literature.

2 Related Works

For over a decade, research on prevention as well as forestalling Sinkhole Attack have been in steady peace, here is a brief review of some of the recent works. [1] presented a parameter evaluation used by ACS to get high values for throughput, best energy usage as well as a lag period which leads to an optimal process of packet routing was attained.

Authors [2] achieved optimal detection through a detection algorithm opined. This was made possible from the communications obtained by aggregation algorithm data for discovering the exploit that emerges from Body Area Network (BAN) as a result of sinkhole attack. In [3] authors deploy an (ESPO) to modify flocking is associated with a collection of algorithms that functions with cohesion, partitioning as well as alignment that exists in the collection of nodes within WSN deployed in a sizeable instance in order to forestall sinkhole attacker.

Authors [4] applied an enhanced ACS algorithm motivated from an alternate of ACO the remote as well as local update enhancements for discovery as well as exploitation improvement path with effective packet loss depletion while improving the efficiency of sensor edge energy. Authors in [5] employ COOJA as a simulator, with consideration of ACO-pheromone vanishing mechanism and ACO critical protection allocation for the effective balancing of edge interactions as well as speed management. Furthermore, in order to mislead an intruder, enhanced (KMT) method with Ant Colony optimization was explored to state a route in other to achieve safe as well as improve packet transmission ingress the nodes and egressing the cell and vice versa.

Authors [6] presented an enhanced ACO algorithm solving the challenges associated with traffic drop experienced when nodes communication traffic is in excess of its capacity. The research evaluation help in juxtaposing EACS with Cost Aware Ant Routing (SC) algorithm potentials as well as the strength of Efficient Ant Based Routing (EEABR) algorithm of which the presented model was planned for implementation in a WSN that is static.

There are several other works available in the literature on ant colony optimization problem [12,13,14,15]. We have not considered them for detailed explanation due to several reasons, including not much related and space issue due to conference paper.

2.1 Findings from Literature

Literature has revealed that different methodology such as the cryptographic, swarm intelligence [7] and machine learning [8] have been employed to address detection of sinkhole attack.

Minimal tradeoff is experienced in swarm intelligence as against all other methods as reviewed in the literature, based on the fact the WSN still remain fragile field that requires lots of attention in terms of energy utilization management, overhead computation management as well as package monitoring. The outperformance of ACO, swarm intelligence algorithm against others of such family algorithm in addressing challenges in various fields like the Travel Salesman Problem (TSP) have proved its prospect for success in its application. Furthermore, the reviewed literature points out gaps such as the need for novel technique, enhancement of existing techniques and addressing false alarm rate reduction in quest or an improved detection rate as well as reduced FAR in WSN.

3 Research Methodology

This study is composed of two major phases, formulation of the problem, planning as well as design. The implementation was achieved through simulation in NS-3.30.1.

An attacker edge was introduced into the network in order to determine the havoc incurred. Furthermore, an evaluation was carried out based on performance. A flow chart of EACO is Fig. 2.

Fig. 2.
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Flowchart for EACO

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A solution list is generated based on the interaction of nodes in a wireless sensor network serving as a pointer In the record is a hash collision is stored as collision list which builds up as a suspect list circulated between communicating edge to track an attacker after signing and voting against the existing ACO with the functionality of transmitting solution list unto suspect list (Table 1).

Table 1. Pseudo code for EACO
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4 Results and Discussion

Table 2 depicts the various parameters used in simulation with 300 edges.

Table 2. Parameters of simulation
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4.1 Simulation

The simulation was based on a normal flow, sinkhole attackers and EACO technique as outlined in the described scenario of WSN:

  • Normal flow scenario: comprising 300 edges sending to and from the base station as well as negligible latency in packet ratio and communication of end-to-end node.

  • Sinkhole attack scenario: variation in parameter was tracked, in this scenario which comprise of 200 edges as well as 50 attacker edges.

  • EACO Implementation scenario: parameter variation was noted under WSN attack based on the simulation. The following were computed; An end to end latency (in ns), detection rate (DR), Packet delivery ratio (PDR), through put (in kps) as well as FA/FPR.

Figure 3 depicts influence on end to end latency which defines the period of packet getting to the base station for a normal flow, at point a sinkhole is compromised and after the implementation of EACO given as 70.06 ms, 736.66 and 153.46 ms respectively.

Fig. 3.
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End to end delay(latency) performance

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  • Figure 4 depicts influence of packet delivery ratio which defines ratio to packet value ingress base station and origination from a remote edge. The output of normal flow WSN PDR ratio, sinkhole under attack and EACO implement method are 0.93, 0.46 and 0.9 respectively with a clearly notable performance in regards to PDR.

Fig. 4.
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Packet delivery performance

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  • Figure 5 depicts impact of throughput which defines transmitted bits value in unit time with a network expressed in (kps), the WSN normal flow, sinkhole under attack and EACO implementation results are 9.2 kps, 4.25 kps and 8.72 kps respectively with 94.32% improvement experienced under EACO.

Fig. 5.
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Network performance

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Furthermore, the simulation achieved the following: in our detection technique

  1. I.

    48 TP real attacks (attacker nodes) legitimate node (false positive) node of 2, legitimate nodes (True Negative) 198 and false negative (attacker mistaken as legitimate node) 2.

  2. II.

    A total 200 legitimate node, 50 sinkhole attack node that was deployed in our study simulation achieved 96% and 1.0% respectively for DR and FPR

4.2 Performance Evaluation

A benchmark analysis in terms of performance comparison against some related literature was carried out as presented in Table 3 as well as Fig. 6 with an indication of distinct performance of our study against benchmark technique.

Table 3. Accuracy comparison
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Fig. 6.
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Accuracy comparison

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5 Conclusion and Future Work

A hash table was integrated into Ant Colony Optimization technique which serves as our design in enhancing performance in terms of time reduction in addressing an attacker alongside indexing which builds an accurate suspect list thereby addressing also false alarm associated with detection of the attacker. Secondly an optimal detection rate of 96% was achieved in our enhanced ant colony optimization method in regards to WSN. Furthermore, future research will look into security enhancement based on encryption of sensors as security protocols are given preference in WSN to aid addressing energy depletion, operational period lagging as well as detection enhancement.