Detection Of Sinkhole Attack Research Articles

Localization and Detection of Sinkhole Attacks in Wireless Sensor Networks Based on Denial of Service (DoS) Attacks

A wireless sensor network (WSN) uses uniformly deployed and positioned sensors to regularly send sensed data to a centralized station. Sinkhole attacks, in which a malicious node draws packets from other legitimate sensor devices and drops them, are the main danger to the WSN network layer and continue to be a difficult problem on wireless sensor networks. Security becomes a major issue since these networks have limited assets including less memory, less energy, and less transmission capacity. Additionally, given the dynamic context in which they are deployed, they are vulnerable to a variety of Denial of Service (DoS) assaults, including wormhole, sinkhole, and black hole attacks. The most dangerous routing attack at the network layer is known as a "sinkhole assault," which directs all network traffic onto a fake path by sending false information under the impression that it is the quickest one to the server. We explore sinkhole attacks to prevent them, and this work proposes a method of detection based on the redundancy process. Messages are transmitted across multiple paths to the suspicious nodes. Upon a thorough evaluation of the response, the attacked nodes are ultimately verified. Finally, a simulation is run to evaluate the method's efficacy. Furthermore, the simulation indicates that the strategy might be somewhat successful. Finally, it draws attention to difficulties and offers a prospective viewpoint for identifying sinkhole attacks.

Sinkhole Attack Detection by Enhanced Reputation-Based Intrusion Detection System

Sinkhole Attack Detection by Enhanced Reputation-Based Intrusion Detection System

Time Synchronized Multivariate Regressive Convolution Deep Neural Network Model for Sinkhole Attack Detection in WSN

Time Synchronized Multivariate Regressive Convolution Deep Neural Network Model for Sinkhole Attack Detection in WSN

Sinkhole Attack Defense Strategy Integrating SPA and Jaya Algorithms in Wireless Sensor Networks.

A sinkhole attack is characterized by low difficulty to launch, high destructive power, and difficulty to detect and defend. It is a common attack mode for wireless sensor networks. This paper proposes a sinkhole attack detection and defense strategy integrating SPA and Jaya algorithms in wireless sensor networks (WSNs). Then, combined with the SPA trust model, the trust values of suspicious nodes were calculated, and the attack nodes were detected. The Jaya algorithm was adopted to avoid the attacked area so that nodes can find the route to communicate with the real Sink, and attack nodes are isolated in the network to improve the capabilities of network directional defense. The simulation results show that the improved detection algorithm can effectively detect malicious nodes in the network, and the defense strategy implemented in the attacked area can improve the packet delivery rate, reduce network delay and energy consumption, and improve the security and reliability of wireless sensor networks.

Multidirectional Trust-Based Security Mechanisms for Sinkhole Attack Detection in the RPL Routing Protocol for Internet of Things

The Internet of Things (IoT) has gained popularity in recent years by connecting physical objects to the Internet, enabling innovative applications. To facilitate communication in low-power and lossy networks (LLNs), the IPv6-based routing protocol for LLNs (RPL) is widely used. However, RPL’s lack of specified security models makes it vulnerable to security threats, particularly sinkhole attacks. Existing sinkhole attack detection techniques suffer from high detection delays and false positives. To overcome these limitations, in our research we propose a multidirectional trust-based detection approach for sinkhole attacks in the RPL routing protocol. Our model introduces a novel architecture that considers trust in parent, child, and neighbor directions, reducing detection delays. We enhance detection efficiency and reduce false positives by combining fuzzy logic systems (FLSs) and subjective logic (SL). Additionally, we introduce a new trust weight variable derived from Shannon's entropy method and multiattribute utility theory. We adaptively adjust the SL coefficient based on network conditions, replacing the constant coefficient value of SL theory. Our approach is compared to the most recent techniques, and we assess different indicators, such as false-positive rate, false-negative rate, packet delivery ratio, throughput, average delay, and energy consumption. Our results demonstrate superior performance in all these metrics, highlighting the effectiveness of our approach.

Sinkhole Detection in IOT using Elliptic Curve Digital Signature

A variety of smart applications, including homes, transportation, health, and robots in industries, are starting to gain interest due to the fast expansion of Internet of Things (IoT). Smart devices are made up of sensors and actuators that actively involved in monitoring, prediction, security, and information sharing in the IoT ecosystem. These state-of-the-art (SOTA) technologies enable people to monitor and manage their unified milieu in real-time. IoT devices are nevertheless regularly used in hostile situations, where attackers try to grab and penetrate them to take over the entire network. Due to the possibility of selective forwarding, sinkhole, blackhole, and wormhole attacks on IoT networks is a serious security risk. This research offers an effective method using a digital signature to detect and mitigate sinkhole attacks on IoT networks to resolve this problem. By doing a thorough security study of this suggested system, it shows how safe it is and how resistant it is to secure sinkhole attack detection. In this study, elliptic curve digital signature algorithm is used along with the node ranker to detect the sinkhole attack in IoT environment. According to the performance analysis and experimental findings compared to other research, the suggested system offers good detection accuracy and greatly lowers the overhead associated with computing, communication, and storage.

Detecting Sinkhole Attacks in IoT-Based Wireless Sensor Networks Using Distance From Base Station

Wireless sensor networks (WSNs) are infrastructure-less in nature, which contains numbers of autonomous sensor nodes. These sensor nodes are dedicated to monitoring the physical conditions of the environment and are organizing the collected data at a central location. Application area of WSN, like - heath care, military surveillance, are sensitive with respective to information sensed, that’s why security of WSN needs to be very effective. Providing security to WSN plays a major role as it consists of limited resources. The security system should lie within the boundary of the resource potential as well as should be competent enough to handle attacks. Intrusion detection system (IDS) is one such type of defense system, which can fulfill the measure of limitation of resources. In this paper, a detection technique is proposed against sinkhole attack using the Euclidean distance of each node from base station. The main advantage of the proposed technique is that it doesn't require any extra hardware setup as well as doesn't require extra communication cost.

FUZZY DEFINITION FOR TASKS AND RESOURCES CATEGORIZATION IN SOFTWARE PROJECTS

WSN is one of the modern networks that use technologies and applications in public places. It consists of hundreds and thousands of tiny sensor nodes scattered in the network and has limited scope and range resources connected to the base stations. The specifications of these nodes are low cost and low energy and used for monitoring purposes. Since the sensors are small and many, it is easy to attack these networks. Therefore, there will be many potential attacks on the network of sensors, and among these attacks are jamming, sinkhole, eavesdropping, and other attacks. The sinkhole attack is the most attack that works to destroy paths by announcing the update of the fake routing related to it; the attack occurs through the compromised node (the malicious node) So that it announces a file containing the routing information and works to attract the rest of the nodes to do this routing the data towards it and then operating the sphere of influence. One of the effects of this attack is to reduce the overall network performance, and it can also be used to make another attack, such as a selective redirection attack and a spoofing attack. This paper aims to analyze and detect sinkhole attacks in wireless sensor networks.

Challenges in Sinkhole Attack Detection in Wireless Sensor Network

Wireless sensor networks (WSN’s) comprise limited energy small sensor nodes having the ability to monitor the physical conditions and communicate information among the various nodes without requiring any physical medium. Over the last few years, with the rapid advancements in information technology, there has been an increasing interest of various organizations in making the use of wireless sensor networks (WSN’s). The sensor nodes in WSN having limited energy detects an event, collect data and forward this collected data to the base node, called sink node, for further processing and assessment. Few attributes of WSN’s like the energy consumption and lifetime can be impacted by the design and placement of the Sink node. Despite various useful characteristics WSN’s is being considered vulnerable and unprotected. There is a large class of various security attacks that may affect the performance of the system among which sinkhole an adversary attack puts dreadful threats to the security of such networks. Out of various attacks, a sinkhole attack is one of the detrimental types of attacks that brings a compromised node or fabricated node in the network which keeps trying to lures network traffic by advertising its wrong and fake routing update. Sinkhole attacks may have some other serious harmful impacts to exploit the network by launching few other attacks. Some of these attacks are forwarding attacks, selective acknowledge spoofing attacks, and they may drop or modify routing information too. It can also be used to send fake or false information to the base station. This study is analyzing the challenges with sinkhole attacks and exploring the existing available solutions by surveying comparatively which used to detect and mitigate sinkhole attacks in the wireless sensor network.

Challenges in Sinkhole Attack Detection in Wireless Sensor Network

Wireless sensor networks (WSN’s) comprise limited energy small sensor nodes having the ability to monitor the physical conditions and communicate information among the various nodes without requiring any physical medium. Over the last few years, with the rapid advancements in information technology, there has been an increasing interest of various organizations in making the use of wireless sensor networks (WSN’s). The sensor nodes in WSN having limited energy detects an event, collect data and forward this collected data to the base node, called sink node, for further processing and assessment. Few attributes of WSN’s like the energy consumption and lifetime can be impacted by the design and placement of the Sink node. Despite various useful characteristics WSN’s is being considered vulnerable and unprotected. There is a large class of various security attacks that may affect the performance of the system among which sinkhole an adversary attack puts dreadful threats to the security of such networks. Out of various attacks, a sinkhole attack is one of the detrimental types of attacks that brings a compromised node or fabricated node in the network which keeps trying to lures network traffic by advertising its wrong and fake routing update. Sinkhole attacks may have some other serious harmful impacts to exploit the network by launching few other attacks. Some of these attacks are forwarding attacks, selective acknowledge spoofing attacks, and they may drop or modify routing information too. It can also be used to send fake or false information to the base station. This study is analyzing the challenges with sinkhole attacks and exploring the existing available solutions by surveying comparatively which used to detect and mitigate sinkhole attacks in the wireless sensor network.

Survey on Approaches to Detect Sinkhole Attacks in Wireless Sensor Networks

Due to the wide range of applications in public and military domains, wireless sensor network (WSN) is evolving as a popular technology. This WSN consists of a large number of sensors that are spread across a geographical area thatare self-configuring in nature. These nodes are of low cost and resource-constrained nodes. Because these reasons the network becomes vulnerable to many network attacks. There are many types of attacks among which one of the serious attacks is the sinkhole attack, which is one of the destructive routing attacks. It causes the adversary node to attract all or most of the traffic from the neighbors by broadcasting false routing updates of having the shortest path to the central station. This paper is a survey on various methods implemented to overcome sinkhole attacks like Hop Count Monitoring scheme, Key Management Approach, Message-Digest Algorithm.

Sinkhole Attack Detection and Prevention using Agent Based Algorithm

This study presents sinkhole attack detection and prevention using agent-based algorithm. In this algorithm, agents are used to provide information to all node from its reliable neighbors by negotiation in three steps, thus nodes may not be able to pay the attention to the traffic made by sinkhole attacker. In this work, network scale of 500×500 m2 square areas have been considered. Series of simulation are carried in each experiment. Every simulation run is being organized to work for 10mins. Network performance is evaluated in terms of throughput, packet delivery ratio, jitter, delay in packets delivery, data packets received, data packets drop using network simulations software. Network simulation results depicts that in proposed algorithm, throughput increases by 15 to 20 percent, packet delivery ratio increases by 30 to 40%, decrease in the jitter by 10 to 15 %, delay in packets delivery is decreased by 15 to 20 %, data packets received are increased by 15 to 20 % and number of the data packets drop are decreased by 5 to 15 %. Based on simulation results throughput, packet delivery ratio and data packets received increased in proposed agent-based algorithm. However, it is observed that, jitter, delay in packets delivery and data packets drop were decreased.

WIRELESS SENSOR NETWORK PERFORMANCE ANALYSIS UNDER SINKHOLE ATTACKS

WSN is one of the modern networks that use technologies and applications in public places. It consists of hundreds and thousands of tiny sensor nodes scattered in the network and has limited scope and range resources connected to the base stations. The specifications of these nodes are low cost and low energy and used for monitoring purposes. Since the sensors are small and many, it is easy to attack these networks. Therefore, there will be many potential attacks on the network of sensors, and among these attacks are jamming, sinkhole, eavesdropping, and other attacks. The sinkhole attack is the most attack that works to destroy paths by announcing the update of the fake routing related to it; the attack occurs through the compromised node (the malicious node) So that it announces a file containing the routing information and works to attract the rest of the nodes to do this routing the data towards it and then operating the sphere of influence. One of the effects of this attack is to reduce the overall network performance, and it can also be used to make another attack, such as a selective redirection attack and a spoofing attack. This paper aims to analyze and detect sinkhole attacks in wireless sensor networks.

Sinkhole Attack Detection and Avoidance Mechanism for RPL in Wireless Sensor Networks

The security issue is one of the main problems in Wireless Sensor Network (WSN) and Internet of Things (IoTs). RPL (Routing protocol for low power and lossy networks) is a standard routing protocol for WSN, is not to be missed from being attacks. The performance of RPL is reduced significantly after being attacked. Sinkhole attack is one of the most common attacks to WSN and RPL, threatening the network capability by discarding packets and disrupting routing paths. Therefore, this paper proposes a new Secured-RPL routing protocol to detect and avoid sinkhole attacks in the network, which is called Cross Layers Secured RPL (CLS-RPL). This routing protocol is enhanced of the existing RPL routing protocol. CLS-RPL is a cross-layer routing protocol that uses information from the data link layer in its security mechanism. CLS-RPL uses a new technique and concept in detecting a sinkhole attack that is based on eave-listening (overhearing) that allows a child node to eave-listening its parent transmission. If the child node does not hear any transmission from its parent node after sending several packets, this means its parent node is a sinkhole attacker. Otherwise, if the node hears transmission from its parent node, this means that its parent node is legitimate and continues to send more packets. CLS-RPL implements a simple security mechanism that provides a high packet delivery ratio. The finding shows that CLS-RPL provides 52% improvement in terms of packet delivery ratio when compared to RPL protocol.

Black Hole and Sink Hole Attack Detection in Wireless Body Area Networks

Black Hole and Sink Hole Attack Detection in Wireless Body Area Networks

Route Inference Based Sinkhole Attack Detection System f or Mobile Ad Hoc Networks

Mobile Ad Hoc Network (MANET) is a highly popular wireless network technology due to the proliferation of wireless devices. The characteristic of mobile ad hoc network is described as wireless links and open medium, centralized decision making, dynamic topology, limited power supply, bandwidth constraint and no predefined boundary. Due to its unique characteristics, this technology has been used to support communications in situations where it may be impossible to deploy infrastructure networks, such as military battlefields, disaster recovery sites and medical emergency situations. However, they appear to be susceptible to a variety of attacks than any other networks. The nodes of a MANET communicate with each other with the help of intermediate nodes. Each node of a network act as a host as well as a router. Efficient routing protocols have been developed to support the functionality of each node. These protocols trust that all the nodes are cooperative and well behaved. But some nodes act as a malicious node and launch various attacks on routing protocols. Mainly sinkhole attack affects the routing functionality of the network. Therefore, route inference-based attack detection has been proposed to handle the sinkhole attack in the networks. The proposed system computes the weight value by considering route factor, flow factor and sink factor. Based on the weight value, a malicious node is identified. The experiment results indicate that, the proposed system achieved a packet delivery ratio of 99.6% and throughput of 2700 kbps.

An efficient voting based method to detect sink hole in wireless acoustic sensor networks

Rapid growth in the development of wireless sensor networks (WSN) in the field of distributed audio signal processing is one of the critical research areas. Intruders can easily track the unprotected audio signals through sinkhole in WSN. To identify the source of the unauthorized audio signals can be retrieve using wireless acoustic sensor networks (WASN). Detection of sinkhole attacks in WASN is a challenging task as there exists centralized level of control only for the collecting the data. Since it is distributed in computation detection of a compromised node and alerting the other nodes in the network can be considered as a challenging issue in this research domain. In this paper, the detection of sinkhole attacks using voting-based method is implemented along with the optimization perspective. The main contribution of this paper is to adapt Artificial Bee Colony algorithm for effective generation of minimized Boolean key for sharing the suspect list among the alerted nodes. The experimental results of our proposed work has been evaluated and compared with the recent algorithms. The metrics used for evaluation are energy value of bee, overall network lifetime and authentication overhead. The performance shows the impact of the proposed algorithm on WSN scenario when compared to other existing algorithms.

Designing Efficient Sinkhole Attack Detection Mechanism in Edge-Based IoT Deployment.

The sinkhole attack in an edge-based Internet of Things (IoT) environment (EIoT) can devastate and ruin the whole functioning of the communication. The sinkhole attacker nodes (s) have some properties (for example, they first attract the other normal nodes for the shortest path to the destination and when normal nodes initiate the process of sending their packets through that path (i.e., via ), the attacker nodes start disrupting the traffic flow of the network). In the presence of s, the destination (for example, sink node i.e., gateway/base station) does not receive the required information or it may receive partial or modified information. This results in reduction of the network performance and degradation in efficiency and reliability of the communication. In the presence of such an attack, the throughput decreases, end-to-end delay increases and packet delivery ratio decreases. Moreover, it may harm other network performance parameters. Hence, it becomes extremely essential to provide an effective and competent scheme to mitigate this attack in EIoT. In this paper, an intrusion detection scheme to protect EIoT environment against sinkhole attack is proposed, which is named as SAD-EIoT. In SAD-EIoT, the resource rich edge nodes (edge servers) perform the detection of different types of sinkhole attacker nodes with the help of exchanging messages. The practical demonstration of SAD-EIoT is also provided using the well known NS2 simulator to compute the various performance parameters. Additionally, the security analysis of SAD-EIoT is conducted to prove its resiliency against various types of s. SAD-EIoT achieves around detection rate and false positive rate, which are considerably better than other related existing schemes. Apart from those, SAD-EIoT is proficient with respect to computation and communication costs. Eventually, SAD-EIoT will be a suitable match for those applications which can be used in critical and sensitive operations (for example, surveillance, security and monitoring systems).

Artificial bee colony based sinkhole detection in wireless sensor networks

Sinkhole attack in wireless sensor networks (WSN) is most vulnerable attack in WSN that prevents the base station from gathering complete and unmodified data from its origin. A simple authentication mechanism is not adequate to prevent WSN from sinkhole attacks as signed routing can also be easily done by compromised nodes. Hence in this paper we addressed the problem sinkhole attack detection in WSN using swarm-based algorithm namely artificial bee colony algorithm. This algorithm finds the compromised node by comparing the node ID’s defined in the rule set. ABC reduces the overall time complexity taken to find the compromised node which turns to reduces the packet loss percentile and increases the packet delivery ratio. The performance of the proposed algorithm is evaluated and compared with the existing methodologies. The results show that the proposed algorithm outperforms the existing methodologies in terms of packet loss, packet delivery ratio and energy consumption.

Detection of Sink Hole Attack Using Decision Tree in Manet

Detection of Sink Hole Attack Using Decision Tree in Manet