Fruit Fly Optimization-Based Adversarial Modeling for Securing Wireless Sensor Networks (WSN)

Introduction

Node capture attack is a comprehensive attack in which the intruder physically captures the sensor node by extracting keys and confidential data. With technological advances in the field of wireless sensor technology, various operations such as catastrophic and defense monitoring can be painlessly and quickly deployed to wireless sensor networks (WSN)(Lin, C,2016) . The scattered nodes communicate wirelessly to a central gateway, which connects to the wired world where users can collect, process, analyze, and present the measured physical data. Though WSNs have their advantages as the nodes are autonomous, they still need to be addressed. Some of the most common and important challenges are coverage, scalability, QoS, and security. Amidst the challenges listed, security is a major issue to be addressed in WSNs. Sensor networks is extremely vulnerable to node capture attacks. WSN is a group of a huge number of low price, low control, and self-organizing specialized sensor nodes (Lin, C,2015). It is very much vulnerable to different physical attacks due to limited resource capacity and screened to the external atmosphere for circulating network data. The node capture attack is one of the major attacks in WSN in which the attacker physically captures the node and can remove the secret information from the node’s memory or misuse the confidential data (Lin.2013). With technological advances in wireless sensor technology, various operations such as the health and defense monitoring can be quickly deployed to WSN. We focus on developing a multi-objective function using which we can compromise the network efficiently and quickly, unlike random attack (RA), maximum key attack (MKA), maximum link attack (MLA). WSN is a wireless network comprising a large number of self-operative and self-sufficient nodes, which comprises low cost, less control, and self-organizing qualities(Lin.2013). This type of network uses sensors for catastrophic and defense monitoring of physical and environmental situations. When such self-governing nodes are used with routers and gateways, it creates a wireless sensor network system. This technology has its applications in various fields, including military, medical, defense, environmental, and many more.There are various issues to be addressed in WSNs. Some of them are scalability, quality of service, size, security, and many more (Tague, P,2008). Out of all these, security is the biggest challenge. Due to tight resource capacity and its exposure to the outer environment, it is prone to various kinds of physical attacks. Broadly, attacks can be of two types: active attacks, including routing attacks, eavesdropping, and passive attacks, which include all attacks against privacy. The performance is measure with other methods, and thus gives the improved resilience in order capturing node, hash computations decreased, compromise probability for proxy nodes also reduced with a revoked link (Ahlawat,2018) . The result matrix is examined with old strategies in order of the number attacking rounds, capturing cost and traffic compromised.. The performance validated by number of path compromise, path length, and route ratio (Ahlawat,2018). With this analysis of keys and linear automated theory, develop a model that effectively describes the behaviour of that network with attack. Optimal control theory method design a response for the network, which provide a network with secure stability(Bonaci,2010).Node capture attacks are one of the most major attacks in WSN. Node Capture Attack is a kind of attack in which the intruder can access the entire network and perform any operation on the network. The attacker captures the sensor node by gaining access to cryptographic keys and secret information like key pre-distribution model(Shukla,2015). Earlier, node capture attacks were having limitations like a lack of attacking methods and low attack efficiency. There are different types of node capture attacks. The next stage of the internet’s development i.e. the internet of things which makes the internet a physical network requires the objects to communicate with minimal human interference. This type of network made of mobile sensor nodes communicating with each other and working synchronously in a controlled envirnments .It is a wireless network that comprises a great number of dynamic, self-directed, small, self operative, low powered devices named sensor nodes called ‘motes’. A large number of battery-operated, spatially separated and minute devices are networked to collect, process, and transfer data to the operators in these networks. It has also controlled the ability to compute and to process the received data. It finds its application in numerous fields such as - Home Applications, Commercial Applications, Forest fire detection, Area monitoring, Air pollution monitoring, Military Applications, Health care monitoring, Health Applications, Environmental Applications, Earth/Environmental sensing, Water quality monitoring, Landslide detection, Industrial monitoring, and many more applications. Thus, so many applications of WSN make its security a prime concern. Henceforth, it aims to find out the vulnerability and weak points in WSN by simulating multiple attacks and concludes to point out where a WSN must be made more robust. Thus, to make the next generation of the internet, i.e., IoT, more reliable and secured, it is important to find out the most probable and destructive attacks prone to affect WSN’s security. To conclude, we have simulated the most advanced attack, i.e., FFOA on node capture attack (Bhatt,2020). Further, a comparative study is provided by plotting graphs for FFOA and its improved version. Therefore, to enhance the attack efficiency of node capture attack, we propose an attacking approach using the Fruit Fly Optimization Algorithm (FFOA), which overcomes these limitations. We aim to develop a multi-objective function using which we can compromise the network efficiently and quickly. We would then compare the performance with few similar competing strategies to show that the FFOA outperforms its competitor.