Detection of Wormhole Attack in Wireless Sensor Networks: A Survey

Detecting Sybil attacks in wireless sensor networks using UWB ranging-based information

A Wireless Sensor Network (WSN) is a wireless network consisting of small sensor nodes. Sensors are used to monitor physical or environmental conditions. WSNs are particularly used in military and civilian applications. As WSNs are generally deployed in an unattended area, they are prone to various types of attacks. One of the harmful attacks is Sybil attack in which a node illegitimately claims multiple identities. In this scenario, legitimate node shares data to the malicious node and the data are lost. So that it becomes necessary to secure the network from this type of attack. This paper aims to study, discuss and analyze the various techniques for detection of Sybil attack in Wireless Sensor Networks. Also various protocols affected by Sybil attack are studied and analyzed.

Routing protocols in wireless sensor network are vulnerable to various malicious security attacks that can degrade network performance and lifetime. This becomes more important in cluster routing protocols that is composed of multiple node and cluster head, such as low energy adaptive clustering hierarchy (LEACH) protocol. Namely, if an attack succeeds in failing the cluster head, then the entire set of nodes fail. Therefore, it is necessary to develop robust recovery schemes to overcome security attacks and recover packets at short times. Hence this paper proposes a detection and recovery scheme for selective forwarding attacks in wireless sensor networks using LEACH protocol. The proposed solution features near-instantaneous recovery times, without the requirement for feedback or retransmissions once an attack occurs.

Wireless sensor network routing protocols are basically used to provide the function of data routing towards the sink and are vulnerable to various attacks. LEACH is a one of the routing protocol used for clustered implementation of wireless sensor network with Received Signal Strength based dynamic selection of Cluster Heads. But, as with other routing protocols, the LEACH is also exposed to attacks when the malicious sensor node becomes the Cluster Head by launching Sybil attack. Cluster heads are vulnerable to various malicious attacks and this greatly affects the performance of the wireless sensor network. Cryptographic and non-cryptographic approaches to detect the presence of attack also exist but they lack efficiency in some way. Cryptographic approaches to prevent this attack are not so helpful though some non-cryptographic methods to detect the attack also exist but they are not too efficient as they result in large test packet overhead. In this paper, we propose SRSRP (Sybil Resistant Secure Routing Protocol) extension to LEACH protocol so as to protect the cluster head against Sybil attack. SRSRP is base on encryption using Armstrong number and decryption using AES algorithm to verify the identity of cluster head. The proposed technique is implemented in NS2, the experimental results clearly indicate the proposed technique has significant capability for the detection of Sybil attack launched for making the malicious node as the cluster head.

Advance in technology leads to increasing interest in the use of Wireless Sensor Networks (WSNs). In WSNs, the sensor nodes are deployed in open and unprotected region. So, the sensor networks are vulnerable to a wide class of attacks such as sinkhole, wormhole and Sybil attack. In many-to-one communication the adversary attracts its surrounding neighbor nodes with false routing information and fake identities. The existing researches have focused on single layer attacks. In their work they target only one specific layer and detect the attack, without considering other layers into account. Our proposed detection technique maximizes the detection accuracy by using cross layer features and Mobile agent based approach in two phases. In first phase, attacks are detected by correlating the cross layer features such as MAC and Network layers. During second phase, if the attacks are detected, mobile agent based technique is applied to prevent the attack. Here the data is forwarded through the mobile agent. The mobile agent solves the security problem by using three step negotiations. Through this mobile agent based approach, the false positive rate can be reduced and improving the energy efficiency than the existing approach.

Wireless Sensor Network (WSN) technology is progressing at a rapid pace and its demand is also increasing massively due to its cost-effective nature and less-challenging installation demands. However, due to its vulnerable wireless environment, WSNs are prone to various types of severely damaging attacks like wormhole, sinkhole, Denial of Service (DoS) etcetera. Therefore, it requires to be more secure and confidential to maintain the productive flow of communication between the nodes. One of the greatest threats which if placed strategically can seriously impair the network is the wormhole attack. In this paper, we proposed a cluster based technique to detect and protect WSN from wormhole attacks. The entire network is divided into clusters eventually at the end and each cluster is governed by a cluster head (CH). Based on Round Trip Time and hop count the security of the network has enhanced and nullified the wormhole attack in the network. A significant improvement in throughput, PDR, Delay has been observed and reported with proposed scheme.

Wireless sensor networks are being used more and more. Sensor nodes can be distributed in hazardous environments to collect data. As these environments are usually unsecured, provide security in wireless sensor networks is vital. Detecting attacks in sensor networks is inevitable to provide security. Various attacks threaten sensor networks one of which is Sybil attack, which can be a big threat for geographical routing protocols and multi-path routing. In this paper, a distributed method has been presented using mobile agents and local information of each sensor to detect Sybil attack. The results of simulation show the efficacy of this method in comparison with previous methods.

Wireless sensor networks have been widely applied in many areas due to their unique characteristics. These have exposed them to different types of active and passive attacks. In the literature, several solutions have been proposed to mitigate these attacks. Most of the proposed solutions are too complex to be implemented in wireless sensor networks considering the resource-constraint of sensor nodes. In this work, we proposed a hierarchical trust mechanism based on clustering approach to detect and prevent denial of service attacks in wireless sensor networks. The approach was validated through simulation using Network Simulator (NS2). The following metrics were used to evaluation the proposed scheme: packet delivery ratio, network lifetime, routing delay, overhead, and number of nodes. The proposed approach is capable of detecting compromised sensor nodes vulnerable to a denial of service attacks. Moreover, it is able to detect all sensed data that have been compromised during transmission to the base station. The results show that our method can effectively detect and defend against denial of service attacks in sensor wireless sensor networks.

With the properties of openness and dynamic in topology, wireless sensor networks are challenged with great security threats. In this paper we focus on the wormhole attack which is proved to be particularly dangerous to the resource-limited sensor nodes. We present the SA-TC algorithm for detecting and thus defending against this serious threat. It is based on the on-demand multi-path routings and uses statistical analysis and time constraint to identify the suspected links. It needs neither time synchronization among the sensors nor extra hardwares such as directional antenna and GPS. The simulative experiment shows that SA-TC can detect and locate wormhole attacks successfully and efficiently.

In today’s world, one of the most severe attacks that wireless sensor networks (WSNs) face is a Black-Hole (BH) attack which is a type of Denial of Service (DoS) attack. This attack blocks data and injects infected programs into a set of sensors in a group to capture packets before reached to the target. Therefore, raw data in the BH region is thwarted and is unable to reach its destination. The network is susceptible to various types of attacks as it is accessible to all types of users and minimizing the energy depletion without compromising the network lifetime is an NP-hard problem. Even though numerous protocols came into effect to overcome the BH attack and to enhance the security of packet delivery in WSNs, Simulated Annealing Black-hole attack Detection (SABD) based Enhanced Gravitational Search Algorithm (EGSA) is yet another implemented strategy to reduce the BH attacks. EGSA-SABD detects and isolates the BH infectors in WSNs. Initially, sensor nodes are hierarchically clustered using similar residual energy to reduce energy consumption. Then, the BH attack possibility in a deployed node is evaluated to find the existence of BH nodes in the region. In the end, EGSA-SABD is employed to detect and quarantine BH attackers in WSNs. The performance of EGSA-SABD is evaluated with certain metrics such as BH attack detection probability rate (BHatt_Prate), energy consumption (Ec), Duration of BH attack detection (Attduration), Packet delivery ratio (Pdr). Based on the experimental observations, the EGSA-SABD outperforms the BHatt_Prate by 13% and also reduces the energy consumption by 21%.

Wireless sensor network detection is compromised by a wormhole. In this study, we propose a technique to identify wormhole invasions in wireless sensor networks. Wormhole attacks are particularly challenging to defend against since the attacker doesn't have to compromise any nodes and can send packets on channels with low latency utilizing wireless gadgets like laptops and cellphones. In recent years, wireless sensor networks (WSNs) have become one of the most significant research areas in computer science, in part due to the challenging issues that these networks and their diverse variety of applications have raised. For the same reason, security on these networks, however, becomes a major concern. Due to limitations in their computing capabilities, memory capacity, battery life, processing power, and tamper-resistant packaging, wireless sensor networks are susceptible to a number of internal and external attacks. Comparisons with a number of related techniques from the literature, such as Wormhole Detection Neighborhood Information, Authentication of Nodes Scheme (ANS), Received Signal Strength (RSS), and Wormhole Detection Using Hound Packet (WHOP) using the NS-2 simulator, are used to validate the proposed detection method. Based on simulation results, regional routing techniques have a low False Detection Rate (FDR).

Wireless sensor networks (WSNs), including Software-Defined Wireless Sensors, are particularly vulnerable to Denial-of-Service (DoS) attacks. Trust models are widely acknowledged as an effective strategy to mitigate the threat of successful DoS attacks in WSNs. However, existing trust models commonly rely on threshold configurations that are based on the network administrator’s experience and leave the challenging task of weight allocation for various trust metrics to network users. This limits the widespread application of trust models as a WSN defence mechanism. To address that issue, this study proposes and theoretically analyses an Adaptive, Threshold-Free, and Automatically Weighted Trust Model (ATAW-TM) for SDWSNs. The model architecture is aligned with the layered centralized management architecture of SDWSNs, which makes it flexible and enhances its responsiveness. The proposed model does not require manual threshold configuration and weight allocation and allows for rapid trust system recovery. It has significant advantages compared to existing trust models and is potentially more feasible to implement on a large scale.

Wireless sensor networks (WSNs) are increasingly being used for data monitoring and collection purposes. Typically, they consist of a large number of sensor nodes that are used remotely to collect data about the activities and conditions of a particular area, for example, temperature, pressure, motion. Each sensor node is usually small, inexpensive, and relatively easy to deploy compared to other sensing methods. For this reason, WSNs are used in a wide range of applications and industries. However, WSNs are vulnerable to different kinds of security threats and attacks. This is primarily because they are very limited in resources like power, storage, bandwidth, and processing power that could have been used in developing their defense. To ensure their security, an effective Intrusion detection system (IDS) need to be in place to detect these attacks even under these constraints. Today, traditional IDS are less effective as these malicious attacks are becoming more intelligent, frequent, and complex. Denial of service (DOS) attack is one of the main types of attacks that threaten WSNs. For this reason, we review related works that focus on detecting DoS attacks in WSN. In addition, we developed and implemented several Deep learning (DL) based IDS. These systems were trained on a specialized dataset for WSNs called WSN-DS in detecting four types of DoS attacks that affects WSNs. They include the Blackhole, Grayhole, Flooding, and Scheduling attacks. Finally, we evaluated and compared the results and we discuss possible future works.

Wireless sensor networks (WSNs) have many limitations in terms of their computational capability, memory, and battery power. In addition, the sensor nodes use insecure wireless communication channels. Thus, WSNs are vulnerable to many types of attacks such as jamming, sinkhole, wormhole, Sybil, and selective forwarding. Among all these attacks, detecting a wormhole attack is very difficult in mobility-based WSNs. In these WSNs, two genuine nodes that are located far apart can become one-hop neighbours for valid reasons. However, the same scenario might indicate the presence of a wormhole attack instead. Differentiating genuine nodes from malicious nodes is a highly challenging task. Our detection method is based on the concept of the rate of change of neighbouring nodes and the length of an alternative path between two nodes. The proposed method does not require any additional hardware such as synchronised clocks or timing information, GPS, or cryptographic methods that require large amounts of computational power. The simulation results indicate that our method has good detection accuracy.

Wormhole attack is a challenging security threat to wireless sensor networks which results in disrupting most of the routing protocols as this attack can be triggered in different modes. In this paper, WRHT, a wormhole resistant hybrid technique, is proposed, which can detect the presence of wormhole attack in a more optimistic manner than earlier techniques. WRHT is based on the concept of watchdog and Delphi schemes and ensures that the wormhole will not be left untreated in the sensor network. WRHT makes use of the dual wormhole detection mechanism of calculating probability factor time delay probability and packet loss probability of the established path in order to find the value of wormhole presence probability. The nodes in the path are given different ranking and subsequently colors according to their behavior. The most striking feature of WRHT consists of its capacity to defend against almost all categories of wormhole attacks without depending on any required additional hardware such as global positioning system, timing information or synchronized clocks, and traditional cryptographic schemes demanding high computational needs. The experimental results clearly indicate that the proposed technique has significant improvement over the existing wormhole attack detection techniques.