Sinkhole Attack Research Articles

Secure Cooperative Routing in Wireless Sensor Networks

In wireless sensor networks (WSNs), sensor nodes are randomly distributed to transmit sensed data packets to the base station periodically. These sensor nodes, because of constrained battery power and storage space, cannot utilize conventional security measures. The widely held challenging issues for the network layer of WSNs are the packet-dropping attacks, mainly sinkhole and wormhole attacks, which focus on the routing pattern of the protocol. This thesis presents an improved version of the second level of the guard to the system, intrusion detection systems (IDSs), to limit the hostile impact of these attacks in a Low Energy Adaptive Clustering Hierarchy (LEACH) environment. The proposed system named multipath intrusion detection system (MIDS) integrates an IDs with ad hoc on-demand Multipath Distance Vector (AOMDV) protocol. The IDS agent uses the number of packets transmitted and received to calculate intrusion ratio (IR), which helps to mitigate sinkhole attacks and from AOMDV protocol round trip time (RTT) is computed by taking the difference between route request and route reply time to mitigate wormhole attack. MATLAB simulation results show that this cooperative model is an effective technique due to the higher packet delivery ratio (PDR), throughput, and detection accuracy. The proposed MIDS algorithm is proven to be more efficient when compared with an existing LEACH-based IDS system and MS-LEACH in terms of overall energy consumption, lifetime, and throughput of the network.

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.

Trust based Routing – A Novel Approach for Data Security in WSN based Data Critical Applications

Wireless technology has changed the way entities communicate with one another. Wireless networks have created several opportunities in fields such as military, health care, and habitat monitoring, to name a few. However, only a few data-critical applications are built on wireless sensor networks, such as border reconnaissance, detecting infringement, and patient monitoring. These applications require the processing of a large amount of private data. Because most applications are data-sensitive, securing data transmission among wireless sensor networks is crucial. While incorporating data security, the most important requirement of wireless sensor nodes being energy optimized also need to be kept in consideration. There are various forms of assaults that are relevant in Wireless Sensor Networks (WSN). Attacks like Black Hole attacks, sink hole attacks, False data Injection attacks etc. are the most commonly seen attacks on WSNs. The common element in all these attacks is the concept of malicious / compromised node - a node which either drops / modifies the data content while forwarding it. Existing techniques for data security generally use cryptographic algorithms, but the use of cryptographic algorithms is in contrast with the energy optimization requirement of sensor nodes. An energy efficient data security scheme needs to be developed. The proposed system analyses several attacks and proposes a multi-layer data security approach to prevent change of data / dropping of data by the compromised nodes. The proposed system is a routing protocol referred as Trust Based Routing (TBR). A concept of trust value of a node is the core idea of TBR. Forwarding node is selected based on highest trust value and thus avoid malicious / compromised nodes from being involved in the routing process. The trust factor is calculated by considering the number of packets dropped, packets rejected, and the node's remaining energy. The idea of TBR is enhanced by incorporating the concept of past trust and trust of node towards a specific destination. This proposed scheme is referred as Extended Trust Based Routing (ETBR). This scheme is further enhanced by including Direct Trust, Indirect Trust and Energy Trust concepts. This scheme is referred as Consolidated Trust Estimation – Trust Based Routing (CTE-TBR). Network Simulator NS2 is used to simulate the proposed schemes. Various network factors are compared to classic Adhoc On-Demand Vector (AODV) and newly proposed schemes. The result indicates the effectiveness of the proposed data security scheme in terms of energy efficiency and Packet Delivery ratio (PDR).

Detection and prevention of sinkhole attacks in MANETS based routing protocol using hybrid AdaBoost-Random forest algorithm

WSN (Wireless Sensor Network) is considered as one of the promising technologies which are utilized in various fields for various applications. Along with it, MANET (Mobile Ad Hoc Network) has attracted a distinct attention, as they serve communication means across innumerable domain. In recent years, due to the development of both wired and wireless technologies, attacks are becoming more frequent and these attacks can be in various form such as worm hole, black hole, grey-hole, sinkhole and others. These attacks can cause loss of security, increase the number of drop packets, decrease packet delivery ratio which result in poor routing performance. In order to overcome these issues, the proposed study employs PBCS (Particle Bee Colony Swarm) algorithm for finding the shortest path between the nodes, which helps in reducing the routing cost and makes the model more efficient and effective. In addition to this, Hybrid AdaBoost-Random forest algorithm is utilized in proposed model. This model helps in reducing the training time which makes the model more reliable and efficient than existing models. It can be implemented in huge dataset and in Hybrid AdaBoost-Random Forest algorithm, estimator of random forest is used, which result in high accuracy and low loss. AODV (Ad-Hoc on Demand Vector) protocol helps to identify the prevention of attack as this routing protocol perform better for longer and extensive duration of traffic than other protocols. The performance of the proposed model is evaluated using different metrics which includes accuracy, recall, precision, sensitivity and specificity. Besides, energy consumption, throughput, network lifetime, delay of the proposed model is also evaluated. The proposed model is then compared with various existing models in order to determine the efficiency and effectiveness of the proposed framework.

A fuzzy based Q – learning approach to prevent sinkhole attacks in MANET (FQ – SPM)

Mobile Ad-Hoc Networks (MANET) are considered one of the significant and growing areas in today’s scenario of technological advancement. It is an infrastructure-less and dynamic ad-hoc network that requires a connection between nodes to deliver packets and data. However, its design adopts a connection-less approach, at the helm of which no monitoring node exists. Hence, the threat of maintaining the network’s security remains an uphill task. Many attacks have been attempted to breach the protection of the MANET. This paper discusses one of the most potent attacks in a MANET infrastructure, the Sinkhole Attack. We try to minimize the possibility of a sinkhole attack using a Fuzzy Q-learning-based approach, a reinforcement learning technique. The results are encouraging, suggesting that sinkhole attacks can be minimized to a great extent after the adaption of the proposed approach.

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.

A Security Scheme for Statistical Anomaly Detection and the Mitigation of Rank Attacks in RPL Networks (IoT Environment)

A Routing Protocol for Low-power-lossy (RPL) networks builds a Destination Oriented Directed Acyclic Graph (DODAG) to provide IPv6 connectivity for resource-constrained devices over a large variety of low-power-lossy link layer technologies. Each RPL node maintains a rank value, which quantizes its relative topological distance from the DODAG root and is calculated based on the rank of its preferred parents and the objective function being employed. The RPL routing process does not impose any check to monitor the action and conduct of the parent nodes. A malicious attacking node can exploit this weakness by faking its rank value to be much lower than the original to attract more traffic to traverse through it from its neighboring and underlying child nodes. An attacking node can choose to perform selective forwarding or a sinkhole attack (Rank Attack type 1 – RA1) or exacerbate network performance parameters by causing topological instability (Rank Attack type 2 - RA2). This paper presents the Statistically-based Anomaly Detection Scheme (SARPL) to detect RA1 and RA2 and attempts to mitigate their effects. The simulations and performance evaluations show that SARPL can successfully detect RA1 attacks in all scenarios whereas it has a positive detection rate of approximately 93% for RA2 type attacks. SARPL also significantly improves network performance parameters, such as packet delivery rate and end-to-end delay, while mitigating the effects of RA1 and RA2.

Securing smart home against sinkhole attack using weight‐based IDS placement strategy

AbstractExtensive use of the Internet of Things (IoT) in smart homes makes users' lives easy and comfortable. Yet, these resource‐constrained devices are prone to manifold security attacks. The sinkhole attack is one of the most destructive attacks that disrupt smart home operations, causing user dissatisfaction. Existing intrusion detection systems (IDS) cannot handle sinkhole attacks competently as they (i) do not consider the node capacity for being an IDS agent, leading to a low attack detection ratio, (ii) do not examine the sinkhole node's role when mitigating attacks, causing remaining network disconnection with the root node and (iii) do not consider replacing energy‐exhausted IDS nodes, causing connectivity loss of partial network with the root. This paper addresses these shortcomings and adequately presents a mechanism to handle sinkhole attacks. A formulation for assigning weights to network nodes based on their resources is proposed here. An IDS placement strategy is introduced to place IDS agents on particular resourceful nodes that extend network lifetime and enhance attack detection capability. We present a novel attack detection and mitigation strategy by ensuring network connectivity. The proposed mechanism achieves 95% attack detection accuracy and reduces false negative rates by 25% and energy consumption reasonably compared to the state‐of‐the‐art.

Comprehensive Trust based Routing Protocol to Mitigate Black-hole attack in Wireless Sensor Networks

Wireless Sensor Network (WSN) is a collection of sensor nodes, that sense environmental data and send it to the administrator for further processing. Sensor nodes are wireless devices with limited battery and are vulnerable to security attacks such as black hole attack, gray hole attack, sink hole attack etc. Researchers have proposed many security mechanisms to mitigate security attacks. Trust based approaches have gained tremendous interest among researchers to embed security in WSNs. A Trust Based Secure Routing Protocol to mitigate black hole attack is presented in this paper. The protocol computes comprehensive trust value for each node and routes the packets only through the nodes with trust value > 0.5. The results of the proposed protocol are compared with TBSEER [10] protocol. The results show an improvement in Packet Delivery Ratio (PDR), throughput, End to End (EED), routing overhead and energy consumption.

A hybrid IDS for detection and mitigation of sinkhole attack in 6LoWPAN networks

A hybrid IDS for detection and mitigation of sinkhole attack in 6LoWPAN networks

Mitigation of Sinkhole Attack in Dynamic Directional Routing for Mobile Wireless Sensor Networks

Mitigation of Sinkhole Attack in Dynamic Directional Routing for Mobile Wireless Sensor Networks

An efficient intrusion detection framework for mitigating blackhole and sinkhole attacks in healthcare wireless sensor networks

A Wireless Sensor Network (WSN) is made up of physically dispersed autonomous sensors which monitor the network and gather data about its surroundings. A sensor effectively captures information securely with the goal to detect potentially dangerous user conduct in the network. However, identifying an attack from ordinary nodes of sensors remains a difficult task. As a result, the suggested efforts concentrate on building effective attack discovery methods for secured data packet broadcasting from source to destination in the WSN. Proportional Overlapping Score-Based Minkowski K-Means Clustering (POS-MKC) is proposed to improve attack detection accuracy with lesser computational complexity in WSN healthcare applications. The simulation results illustrate that the proposed MK-Means framework widely generates an optimized performance with the reduction of delay, computational complexity and improvements in packet delivery ratio with high attack detection accuracy as compared to the state-of-the-art works.

Sinkhole detection and prediction using watermarking (SNDW)

Since there is no central controller, preserving the security and energy efficiency of wireless sensor networks (WSN) is challenging. They also have a flexible configuration. A network of this type is vulnerable to several attacks. The main goal of this paper is to focus on a well-known attack known as the sinkhole attack. Sensors are installed and positioned equally in a WSN to communicate sensed data to a centralized station regularly. So, the sinkhole attack is a big danger to the WSN network layer, and it is still a difficult issue on sensor networks, where even the malicious node collects packets from other regular sensor nodes and dumps them. To maintain the integrity and authentication of data during its travel in wireless sensor networks overcoming sinkhole attacks we propose a novel approach. In our approach besides overcoming sinkhole attack using a threshold-based method, authentication, and data integrity is maintained using a watermarking-based technique.

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.

A comparative study of different security issues in MANET

In a MANET (Mobile Ad-Hoc Network), an intruder can attempt to gain unlawful access to the network to obtain sensitive information. These attacks can occur at various network layers, and different attacks can be carried out. To mitigate the risks of such attacks, several solutions have been proposed. It can be characterized by dynamic topology, meaning that the network is formed by a group of nodes communicating wirelessly and without centralized control. This feature makes MANETs highly vulnerable to attacks, especially when malicious nodes are introduced into the network. These malicious nodes can engage in malicious activity that severely damages the network's performance and credibility. Among the major attacks that can be carried out in a MANET are Sinkhole attacks, Black hole attacks, and Wormhole attacks. Sinkhole attack, a malicious node intercepts a data packet, alters its contents, and then forwards it to its neighbors. This can cause other nodes to send their data packet to the malicious nodes, compromising the safety and privacy of the network. In a BHA, malicious nodes drop the data packet it receives, preventing them from accomplishing their intended destinations. This can result in a DoS attack, where legitimate users cannot access the network. A WHA involves two malicious nodes colluding to drop data packets from the network. They create a virtual tunnel between them, and any data that passes through this tunnel is dropped, making it impossible for legitimate nodes to communicate with each other. All these attacks can cause significant damage to the network, and researchers have proposed various solutions to protect the network from them. These solutions include using IDS, deploying secure routing protocols, and developing secure algorithms for data transmission. By implementing these solutions, it is possible to improve the Safety and trustworthiness of the MANET and prevent malicious nodes from causing harm to the network.

Cooperative IDS for Detecting Collaborative Attacks in RPL-AODV Protocol in Internet of Everything

Internet of everything (IoET) is one of the key integrators in Industry 4.0, which contributes to large-scale deployment of low-power and lossy (LLN) networks to connecting people, processes, data, and things. The RPL is one of the unique standardized routing protocols that enable efficient use of smart devices energy, compute resources to address the properties and constraints of LLN networks. The authors investigate the RPL-AODV routing protocol's performance in combining the advantages of both RPL and AODV routing protocol, which works together in a low power resource-constrained network. The main challenging issue is collaborating the AODV and RPL routing protocol in the LLN network. This paper also models the collaborative attacks such as wormhole, blackhole attack for AODV, and rank and sinkhole attacks to exploit the vulnerability of RPL protocol. Finally, the cooperative IDS combining specification-based and signature-based IDS is proposed to detect the collaborative attacks against the RPL-AODV routing protocol that effectively monitors and provides security to the LLN networks.

A multipath routing protocol for secure energy efficient communication in Wireless Sensor Networks

A Wireless Sensor Network (WSN) is comprised of a number of sensor nodes (SNs) that are randomly placed in an open, harsh environment for many applications. Due to the resource-constrained nature of SNs and hostile deployment environments, energy efficiency and security are considered two key factors in designing WSN routing protocols. This paper proposes an Energy Efficient Secure Multipath (EESM) routing protocol to securely construct efficient routes and transmit data packets between SNs and the base station (BS). EESM achieves energy efficiency through minimal task allocation among SNs whereas all computation-intensive tasks such as network information collection, routing table generation, and network maintenance are performed by the BS. The proposed protocol incorporates lightweight security mechanisms including a one-way hash chain, message authentication code, encryption, and clique-based coordinator selection and monitoring schemes to defend against numerous security attacks. Simulation results show that EESM can successfully detect and protect the network against various security attacks such as replay attacks, sybil attacks, sinkhole attacks, spoofing attacks, compromised node attacks, and so on. In terms of energy efficiency, the proposed protocol achieves an up to 37% increase in network lifetime and a 6% increase in throughput over Secure and Energy Efficient Multipath (SEEM) routing, Secure and Reliable Multipath Routing (SRMR), and Reliable and Multipath Encounter Routing (RMER) protocols. The paper implements the protocol in a real environment using Arduino motes to analyze security overheads and network setup time.

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.