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A new approach for detecting and monitoring of selective forwarding attack in wireless sensor networks
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Wireless sensor networks (WSNs) are susceptible to most security attacks. There are some limitations such as reliability, energy efficiency, and scalability, which affect sensor nodes. These limitations mostly affect the security of wireless networks. Also, limited capacity of sensor nodes accounts for the security attacks on WSNs. Applications such as military surveillance, traffic surveillance, healthcare, and environmental monitoring are impacted by security attacks. Hence, researchers have created various types of detection approaches against such attacks. Selective forwarding attack is an example of an attack that is not easily detected particularly in the networks layer. In this type of attack, malicious nodes function in the same way as other nodes in the networks. However, it tries to drops the sensitive information prior to transferring the packet to other sensor node. In this paper, we proposed a new approach for detecting and monitoring selective forwarding attacks in wireless sensor networks. The new approach guaranteed to keep the data transferring between nodes safely.
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... These properties are implemented in the real world for energy emergency response information [2], and monitoring factory environments [3]. The networks are vulnerable to various kinds of security threats from intruders at the network layer [4]. The main B Deepak C. Mehetre dcmehetre14@gmail.com 1 Computer Science Department, Sathyabama University, Chennai, India 2 Information Technology Department, Govt. ...
... There is no requirement to fix the positions of the malicious nodes because the sensor nodes are applicable in the field of high risk. Therefore, there is no security for most of the WSN, which averts the simple intrusions on the sensor nodes [3,4]. The Principle service in WSNs is the routing of data packets. ...
... The collision of these issues is employed to evaluate various types of methods [10]. The issues include fault tolerance, scalability, power consumption, network topology, hardware constraints, production cost, transmission media, and environment [4]. But, the non-forbearance of possible security obstacles in the area of routing is dangerous because, in almost all application areas, in which WSNs are used, sensor nodes are deployed in unfavorable environments, providing the opportunity for the attacker to launch certain attacks [11] against the sensor nodes. ...
In the recent era, security is the major problem in sensor networks. Wireless sensor networks (WSNs) are mostly used for various real-world applications. However, WSNs face a lot of insider and outsider attacks, and it is complex to identify and protect towards insider attacks. Generally, an insider attack, in which the intruders choose several received data packets to drop, threatens the clustered WSNs. This situation has occurred because of the unattended clustered environments in the network. To overcome this problem, this paper proposes a trustable and secure routing scheme using two-stage security mechanism, and dual assurance scheme, for selecting the node and securing the data packet for WSNs. Both schemes are based on Active Trust to protect several kinds of attacks, such as black hole attack, and selective forwarding attack, during routing. Therefore, this paper identifies the trusted path and provides the secure routing paths using trust and Cuckoo search algorithm. Energy is the performance parameter utilized in the proposed scheme. The experimental result proves that proposed system provides the assurance to prolong the network lifespan and the probability of secure routing path in the network.
... In [54] author discussed the selective forwarding attack in military applications. Sensor nodes can detect activities in battlefield such as tank movement but malicious node can destroy transmission and stop the packet from being transmitted. ...
Internet of Things (IoT) has revolutionized the world in the last decade. Today millions of devices are connected to each other utilizing IoT technology in one way or the other. With the significant growth in IoT devices, the provision of IoT security is imperative. Routing protocol for low power and lossy networks (RPL) is a network layer protocol, specially designed for routing in IoT devices. RPL protocol faces many attacks such as selective forwarding attacks, blackhole attacks, sybil attacks, wormhole attacks, and sinkhole attacks. All these attacks pose great threats to IoT networks and can substantially affect the performance of the network. In this work, a comprehensive review of internal attacks on the network layer is presented. Specifically, we focus on the literature that considers presenting solutions for the detection and prevention of sinkhole attacks. We reviewed the state-of-the-art works and different performance parameters like energy consumption, scalability, threshold value, packet delivery ratio, and throughput. Moreover, we also present a detailed analysis of machine learning-based algorithms and techniques proposed for the security of RPL protocol against internal attacks.
... The digest message technique was created, the methods for data digestion are broadcasted using the trusted route to detect sinkhole attacks, and the protocol followed differs from the system's actual design. As the given method considers the active trust mechanism, different forms of the attacks, such as blackhole and the selective forwarding attacks, are observed.Alajmi et al. [26] introduced a discovery strategy for selective forwarding attacks and explored various network monitoring techniques. To discover and observe the many attacks in the WSN like selective forwarding attack. ...
The Internet of Things (IoT) is gradually spreading across the globe, offering a wide variety of opportunities across every part of our lives. Unfortunately, the Internet of Things comes with several information technology flaws and exploits. As the number of security vulnerabilities continues to rise, cybersecurity remains a serious problem for every business in cyberspace. In recent times thousands of zero-day attacks are known to exist. As a result, the addition of new attacks emerges regularly several protocols, mostly from the IoT. There are minor modifications to previously reported cyberattacks. This suggests that even advanced mechanisms like basic machine learning systems have a hard time identifying anomalies. IoT applications pose various threats to businesses, according to security experts. Because of the widespread acceptance of IoT devices, their variety, standardization challenges, and inherent versatility, businesses need an intelligent mechanism capable of the detecting unauthorized IoT devices automatically and connected to their networks. In this study, a machine learning approach is used to identify unauthorized IoT devices by detection of sybil and buffer overflow attack among IoT devices infrastructure. The CNN technique focuses on detecting the attack or activity of any malicious node, as well as attempting to resolve the problem. In this research, CNN, a deep learning technique, was applied for extracting features from the network traffic data to accurately identify malicious IoT devices.
... Gray hole attack [21] also known as the selective forwarding attack. Gray hole attack is a certain or special type of the black hole attack as in this some part of the data packets is being dropped and pass on the data packets after it creates an illusion that it has the shortest path to reach the recipient node [22]. ...
Wireless Sensor Network comprises of nodes that are randomly distributed over the network. This type of network operates over radio wave frequency band. The nodes are free to move in any direction as they do not require any fixed infrastructure. The characteristic features of the network are dynamic topology, no infrastructure, fast in operation, etc. But due to its increase in the network, these types of network are prone to many attacks. As security is considered as one of the main constraints in any type of network, it becomes very important to take into consideration the key elements of security which are availability, integrity and confidentiality. Confidentiality means that the data transfer in the network must be kept secret in any transmission process. Integrity implies that the sent data must be preserved during transmission process. Availability means the data must be made available in the network for transmission. Wireless network follows layered approach where different layers perform different functionalities. In this paper we present different layer attacks along with security mechanisms to avoid the effect of attack in the network.
... In order to find and monitor the various attacks in WSN like selective forwarding attacks, Alaimi et al. [31] presented a detection technique for the selective forwarding attacks and also have considered different techniques for network monitoring. The defined technique is capable to detect the attacks over the network layer without considering much of the efforts. ...
Selective forwarding attacks in WSN can damage many mission-critical applications, like military surveillance and forest fire censoring. In such attacks, malicious nodes most of the time functions like regular nodes, but sometimes drop sensitive packets selectively, like a packet recording the dissimilar power' activity, making it more difficult to identify their malicious intent. The current selective forwarding attack detection schemes, randomly select checkpoint nodes, available in-between nodes within a forwarding route, which are responsible for producing acknowledgments for each received packet. In this paper, the complete sets of nodes are differentiated into three different types based on their functionality as Inspector Node (IN), Cluster Head (CH), and Member Nodes (MN). The newly considered node as IN is considered to overhear all of the activities of the Cluster head, as CH is the most compromising node in the complete cluster, and in the case, if the CH is attacked then the complete cluster stops working in the network. The IN is trained based on certain rules and predefined parameters which analyses if the CH or MN is malicious or not and considers the required action. NS2 is considered for the simulation of the proposed methodology and also for the validation of the proposed work. In the proposed methodology, two different stages are considered as detection and correction, which works to tackle the attacks and also considering the system efficiency almost. As in the proposed methodology, the effect of the attack is minimized which increases the QOS and also better data transmission.
... (4) Gray Hole Attack Gray hole attack [32], [33] is also called selective forwarding attack, as shown in Fig. 4. It is a specific form of black hole attack as it only drops a part of packets. For example, it drops a packet every t seconds, or the packets with some sensitive information (e.g., sending to a specific destination). ...
Wireless Sensor Network (WSN) is an indispensible part of IoT that has been applied in many fields to monitor environments and collect data from surroundings. However, WSNs are highly susceptible to attacks due to its unique characteristics: large-scale, self-organization, dynamic topology and constrained resources. A number of systems have been proposed to effectively detect varieties of attacks in WSNs. However, most previous surveys on WSN attacks focus on detection methods for only one or two types of attacks and lack detailed performance analysis. Additionally, the literature lacks comprehensive studies on security-related data (in short security data) collection in WSNs. In this paper, we first provide an overview of WSNs and classify the attacks in WSNs based on protocol stack layers. For the purpose of WSN security measurement, we then research attack detection methods of eleven mainstream attacks. We extract security data that play an important role for detecting security anomaly towards security measurement. We further elaborate the advantages and disadvantages of the existing detection methods based on a number of evaluation criteria. Finally, we highlight a number of open problems in this research field based on our thorough survey and conclude this paper with possible future research directions.
Wireless Sensor Network (WSN) is usually prolonged into huge geographical regions through inexpensive sensor devices that can tolerate themselves with very less power usage. The networking ability enables the sensor nodes for integrating, coordinating, and collaborating with each other, which is a basic alternation in network area and it distinguishes sensor network nodes from other networks, namely Ad-Hoc, Internet (IP) datagram and etc. At present, the routing protocol in a wireless sensor network experiences various issues, including coverage packet loss, video real time and real-time audio streaming, new scalability, energy restraints, severe weather environments and so on. Moreover, network routing is termed as the incorporation of a routing approach and routing protocol. The intent of the routing system is to afford consistent assessment of network nodes topology when the routing model offers intelligence with regards to optimal path computation. Thus, in this paper, a study is made with several routing systems in WSN and the performance is evaluated. Here, the performance evaluation is done by the existing routing methods along with the Taylor SailFish Optimizer (Taylor SFO) approach. Additionally, the performance of the routing technique is analyzed with throughput, distance, energy, end to end delay as well as Packet Delivery ratio (PDR). The Taylor SFO routing model achieved minimal distance and delay of 195.44 m, and 15.25 ms as well as maximum throughput, PDR, and residual energy of 119.98 kbps, 94.87%, and 221.65 J.
This paper introduces the design, implementation, and performance analysis of the scalable and mobility-aware hybrid protocol named boarder node medium access control (BN-MAC) for wireless sensor networks (WSNs), which leverages the characteristics of scheduled and contention-based MAC protocols. Like contention-based MAC protocols, BN-MAC achieves high channel utilization, network adaptability under heavy traffic and mobility, and low latency and overhead. Like schedule-based MAC protocols, BN-MAC reduces idle listening time, emissions, and collision handling at low cost at one-hop neighbor nodes and achieves high channel utilization under heavy network loads. BN-MAC is particularly designed for region-wise WSNs. Each region is controlled by a boarder node (BN), which is of paramount importance. The BN coordinates with the remaining nodes within and beyond the region. Unlike other hybrid MAC protocols, BN-MAC incorporates three promising models that further reduce the energy consumption, idle listening time, overhearing, and congestion to improve the throughput and reduce the latency. One of the models used with BN-MAC is automatic active and sleep (AAS), which reduces the ideal listening time. When nodes finish their monitoring process, AAS lets them automatically go into the sleep state to avoid the idle listening state. Another model used in BN-MAC is the intelligent decision-making (IDM) model, which helps the nodes sense the nature of the environment. Based on the nature of the environment, the nodes decide whether to use the active or passive mode. This decision power of the nodes further reduces energy consumption because the nodes turn off the radio of the transceiver in the passive mode. The third model is the least-distance smart neighboring search (LDSNS), which determines the shortest efficient path to the one-hop neighbor and also provides cross-layering support to handle the mobility of the nodes. The BN-MAC also incorporates a semi-synchronous feature with a low duty cycle, which is advantageous for reducing the latency and energy consumption for several WSN application areas to improve the throughput. BN-MAC uses a unique window slot size to enhance the contention resolution issue for improved throughput. BN-MAC also prefers to communicate within a one-hop destination using Anycast, which maintains load balancing to maintain network reliability. BN-MAC is introduced with the goal of supporting four major application areas: monitoring and behavioral areas, controlling natural disasters, human-centric applications, and tracking mobility and static home automation devices from remote places. These application areas require a congestion-free mobility-supported MAC protocol to guarantee reliable data delivery. BN-MAC was evaluated using network simulator-2 (ns2) and compared with other hybrid MAC protocols, such as Zebra medium access control (Z-MAC), advertisement-based MAC (A-MAC), Speck-MAC, adaptive duty cycle SMAC (ADC-SMAC), and low-power real-time medium access control (LPR-MAC). The simulation results indicate that BN-MAC is a robust and energy-efficient protocol that outperforms other hybrid MAC protocols in the context of quality of service (QoS) parameters, such as energy consumption, latency, throughput, channel access time, successful delivery rate, coverage efficiency, and average duty cycle.
Wireless sensor networks are specific adhoc networks. They are characterized by their limited computing power and energy constraints. This paper proposes a study of security in this kind of network. We show what are the specificities and vulnerabilities of wireless sensor networks. We present a list of attacks, which can be found in these particular networks, and how they use their vulnerabilities. Finally we discuss about different solutions made by the scientific community to secure wireless sensor networks.
Security in Wireless Sensor Networks (WSNs) is especially challenging and quite different from traditional network security mechanisms. There are two major reasons. Firstly, there are severe constraints on these devices namely their minimal energy, computational and communicational capabilities. Secondly, there is an additional risk of physical attacks such as node capture and tampering. Moreover, cryptography based techniques alone are insufficient to secure WSNs [1]. Hence, intrusion detection techniques must be designed to detect the attacks. Further, these techniques should be lightweight because of resource-constrained nature of WSNs [2]. In this paper, we present a new approach of robust and lightweight solution for detecting the Sinkhole attack and the Selective Forwarding attack based on Received Signal Strength Indicator (RSSI) readings of messages. The proposed solution needs collaboration of some Extra Monitor (EM) node apart from the ordinary nodes. We use RSSI value from four EM nodes to determine the position of all sensor nodes which the Base Station (BS) is origin position (0,0). Later, we use this information as weight from the BS. Another functions of EM nodes are eavesdropper and monitor all traffics, in order to detect the Selective Forwarding attack in the network. Our solution is lightweight in the sense that monitor nodes were not loaded any ordinary nodes or BS and not cause a communication overhead.
To ensure sustainable operations of wireless sensor systems, environmental energy harvesting has been regarded as one of the most fundamental solutions for long-term applications. In energy-dynamic environments, energy conservation is no longer considered necessarily beneficial, because energy storage units (e.g., batteries or capacitors) are limited in capacity and leakage-prone. In contrast to legacy energy conservation approaches, we aim at energy-synchronized computing for wireless sensor devices. The starting point of this work is TwinStar, which uses ultra-capacitor as the only energy storage unit. To efficiently use the harvested energy, we design and implement leakage-aware feedback control techniques to match the activities of sensor nodes with dynamic energy supply from environments. We conduct system evaluation under both indoor and outdoor typical real-world settings. Results indicate our leakage-aware energy-synchronized control can effectively utilize energy that could otherwise leak away.
With the widely use of wireless sensor network (WSN), data forwarding security has become more and more important to the whole network. In order to avoid the selective forwarding attack, we proposed a scheme of secure data transmission which can forward the data safely, and detect the selective forwarding attack. In this paper, we judge the trust value of each node to select a secure path for message forwarding and then use the watermark technology to detect the malicious nodes which are suspected to launch selective forwarding attack. Different from the multi-path routing which only defends the selective forwarding attack, our method may find the malicious nodes. Extensive simulation proves that even when the channel error rate is 10%, the detection accuracy of the proposed scheme is over 95%.
Wireless sensor networks (WSNs) can be used by the military for a number of purposes such as monitoring or tracking the enemies and force protection. Unlike commercial WSNs, a tactical military sensor network has different priority requirements for military usage. Especially in the remote large-scale network, topology, self-configuration, network connectivity, maintenance, and energy consumption are the challenges. In this paper, we present an overview of application scenarios in remote large-scale WSNs focusing on the primary requirements for tactical environments. We propose a sensor network architecture based on the cluster-tree based multi-hop model with optimized cluster head election and the corresponding node design method to meet the tactical requirements. With the proposed WSN architecture, one can easily design the sensor network for military usage in remote large scale environments.
We consider routing security in wireless sensor networks. Many sensor network routing protocols have been proposed, but none of them have been designed with security as a goal. We propose security goals for routing in sensor networks, show how attacks against ad-hoc and peer-to-peer networks can be adapted into powerful attacks against sensor networks, introduce two classes of novel attacks against sensor networks––sinkholes and HELLO floods, and analyze the security of all the major sensor network routing protocols. We describe crippling attacks against all of them and suggest countermeasures and design considerations. This is the first such analysis of secure routing in sensor networks.
Selective forwarding attacks may corrupt some mission- critical applications such as military surveillance and for- est fire monitoring. In these attacks, malicious nodes be- have like normal nodes in most time but selectively drop sensitive packets, such as a packet reporting the movement of the opposing forces. Such selective dropping is hard to detect. In this paper, we propose a lightweight secu- rity scheme for detecting selective forwarding attacks. The detection scheme uses a multi-hop acknowledgement tech- nique to launch alarms by obtaining responses from inter- mediate nodes. This scheme is efficient and reliable in the sense that an intermediate node will report any abnormal packet loss and suspect nodes to both the base station and the source node. To the best of our knowledge, this is the first paper that presents a detailed scheme for detecting selective forwarding attacks in the environment of sensor networks. The simulation results show that even when the channel error rate is 15%, simulating very harsh radio con- ditions, the detection accuracy of the proposed scheme is over 95%.