A non cryptographic method of sink hole attack detection in wireless sensor networks

Security in wireless sensor networks is critical due to its way of open communication. In this paper we have provided a solution to detect malicious nodes which perform radio transmission power control attack and sinkhole attack in wireless sensor networks. In the proposed approach, data transmission is divided into multiple rounds of equal time duration. Each node chooses the parent node in the beginning of the round for forwarding the packet towards sink. Each node adds its identity in the packet as a routing path marker and encrypts before forwarding to parent. Child node observes the parent, handles acknowledgement from 2-hop distance node and decides the trust on parent based on successful and unsuccessful transactions. Each node sends a trust value report via multiple paths to Sink at the end of the round. Sink identifies the malicious node by comparing trust value report received from each node with number of data packets received. Simulated the algorithm in NS-3 and performance analysis compared with other recently proposed approach. Simulation results show that proposed method detect the malicious nodes efficiently and early.

Objectives: The main aim of this paper is security by applying Intelligent Intrusion detection System to detect one of the critical attack know as sinkhole attack in Wireless Sensor network. Methods/Analysis: The major problem occurs while transmitting the data is security. The malicious node attracts the packets from the other normal sensor nodes and drops the sensitive packets which may leads to selective forwarding attack and black hole attack. Findings: In the existing system, Leader based intrusion detection system is used to monitor the surrounding node. Here the leader can monitor the nodes only region wise and does not have any clustering technique. By using the clustering technique we can monitor the surrounding nodes in efficient manner and better solution. In the proposed system, an Intelligent Intrusion Detection System (IIDS) mechanism to detect the intruder in the network, which uses a Hybrid Approach to detect and prevent sinkhole attack, with two approaches such as Zone-Based Leader Election Method and Leader Based Monitoring. In Zone-Based Leader Election method, a set of nodes is located in different regions and a Cluster Based Zone Leader (CBZL) is allocated to every region in the network. In the Leader Based Monitoring Approach, when a node gets detected as a compromised node, it communicates the status of that node to the other leader within the WSN. Application/Improvement: This technique can identify the intruder node and improve the efficiency of the network. As a simulation results, we are implementing in Network Simulator. In environment monitoring and military applications the proposed approach can be deployed.

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.

In this paper, we propose an Intrusion Detection System (IDS) against Sinkhole attack in wireless sensor networks with mobile sink. In the detection model, the network area is divided into a flat grid of cells, and we use the signature-based technique, which is represented by the detection rate of a cell, to distinguish between real and fake sink nodes. The proposed IDS considers two types of sink mobility: periodic and random. In addition, as the cell leaders do not activate their IDS agent simultaneously, the additional energy consumption incurred by the IDS is low. Simulation results show the efficiency of the proposed IDS in terms of detection rate, efficiency, and energy consumption.

Wireless sensor networks have become a vital technology that is extensively applied across multiple industries, including agriculture, industrial operations, and smart cities, as well as residential smart homes and environmental monitoring systems. Security threats emerge in these systems through hidden routing-level attacks such as Wormhole and Sinkhole attacks. The aim of this research was to develop a methodology for detecting security incidents in WSNs by conducting real-time analysis of Wormhole and Sinkhole attacks. Furthermore, the paper proposes a novel detection methodology combined with architectural enhancements to improve network robustness, measured by hop counts, delays, false data ratios, and route integrity. A real-time WSN infrastructure was developed using ZigBee and Global System for Mobile Communications/General Packet Radio Service (GSM/GPRS) technologies. To realistically simulate Wormhole and Sinkhole attack scenarios and conduct evaluations, we developed a modular cyber–physical architecture that supports real-time monitoring, repeatability, and integration of ZigBee- and GSM/GPRS-based attacker nodes. During the experimentation, Wormhole attacks caused the hop count to decrease from 4 to 3, while the average delay increased by 40%, and false sensor readings were introduced in over 30% of cases. Additionally, Sinkhole attacks led to a 27% increase in traffic concentration at the malicious node, disrupting load balancing and route integrity. The proposed multi-stage methodology includes data collection, preprocessing, anomaly detection using the 3-sigma rule, and risk-based decision making. Simulation results demonstrated that the methodology successfully detected route shortening, packet loss, and data manipulation in real time. Thus, the integration of anomaly-based detection with ZigBee and GSM/GPRS enables a timely response to security threats in critical WSN deployments.

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

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 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.

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.

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.

Wormhole attack is a harmful attack that disrupts the normal functioning of the network by manipulating routing protocols and exhausting network resources. The paper presents a technique that detects the presence of wormhole attack in wireless sensor network (WSN) using artificial neural network (ANN). The proposed technique uses connectivity information between any two sensor nodes as the detection feature. The proposed technique has been implemented considering the deployment of sensor nodes in the wireless sensor network area under uniform, Poisson, Gaussian, exponential, gamma & beta probability distributions. The proposed technique does not require any additional hardware resources and gives a comparatively high percentage of detection accuracy. KeywordsWireless sensor networkProbability distributionWormhole attackANN

The article comprises issues of analytical modeling of attacks in wireless sensor networks. A proposed attack model specifies possible types of unauthorized influences at the network level aimed at compromising network nodes, data transmitted over the network, and information services provided by the wireless sensor network. A developed graph-based analytical model of a wireless sensor network aims at detecting network layer attacks by processing actual events received from network nodes and deciding whether the network is running under an attack. The correctness of the models is confirmed by their testing by using an example of a scenario for detecting a wormhole attack aimed at disrupting routing processes in a wireless sensor network.

In Wireless sensor Network, the sensor nodes transmit critical information over the network; therefore, security services, such as, authentication and pair wise key establishment between sensor nodes and mobile sinks, are important. However, the problem of authentication and pair wise key establishment in sensor networks with MSs is still not solved in the face of mobile sink replication attacks. For the basic probabilistic and q-composite key pre distribution schemes, an attacker can easily obtain a large number of keys by capturing a small fraction of the network sensor nodes, making it possible for the attacker to take control of the entire network by deploying a replicated mobile sink, preloaded with some compromised keys to authenticate and then initiate data communication with any sensor node. To solve this problem, we have developed a general framework that permits the use of any pair wise key pre distribution scheme as its basic component, to provide authentication and pair wise key establishment between sensor nodes and MSs. The new framework requires two separate key pools, one for the mobile sink to access the network, and one for pair wise key establishment between the sensors.

The rapid growth of the Internet and information technologies and also the diminution of the price of hardware components like wireless sensors leads to the fast growth in Wireless Sensor Network (WSN). The WSN is a bunch/group of sensors that are located across the area, in order to monitor and determine environmental conditions such as temperature, humidity, vibrations, wind, water levels, pollution levels. WSNs are susceptible to major attacks like Denial of Service (DOS), Wormhole attack, Sinkhole attack, etc. The main threat to the WSN is because of the broadcast nature of the nodes in the network. Therefore, the security of WSNs is the essential part that must be done. Hence, to overcome these problems or threats, we are trying to detect it using AI technology. With the expanding fields of Machine Learning and Deep Learning, we can apply various algorithms in order to classify different types of attacks. Once we detect the attack properly, we can prevent it accordingly. We are using WSN-DS. It has 4 classes of attacks which are Grayhole, Blackhole, TDMA(Scheduling), and Flooding which comes under the category of DOS attacks. In this chapter, we have analysed and compared the accuracies of 5 main machine learning classification algorithms. Also, we have analysed the 1 deep learning algorithm. The ANN (Artificial Neural Network) and 5 machine learning algorithms have been trained on the dataset. Furthermore, we have used K-fold cross-validation to get even more accurate predictions. After analysis of these algorithms, we came to the point that, the Machine Learning algorithms like Random Forest, Support Vector Machines and Deep Learning algorithm, namely, Artificial Neural Network can help us for detecting intrusions in the system or network. This will help researchers in designing their own machine learning model on top of our suggested model.

Wireless sensor networks (WSNs) are novel large-scale wireless networks that consist of distributed, self organizing, low-power, low-cost, tiny sensor devices to cooperatively collect information through infrastructure less wireless networks. These networks are envisioned to play a crucial role in variety of applications like critical military surveillance applications, forest fire monitoring, commercial applications such as building security monitoring, traffic surveillance, habitat monitoring and smart homes and many more scenarios. Node capture attack is one of the most dreadful security attack exist in wireless sensor networks. An adversary steals cryptographic key or other confidential information like node’s id etc from a captured node to compromise entire network. So, Security of wireless sensor network is an important issue for maintaining confidentiality and integrity of wireless links. Now-a-days, researchers are paying attention towards developing security schemes against Node capture attack. Our survey provides deep insights of existing techniques that enhance the attacking efficiency of the node capture attack in wireless sensor network. It also analyzes various detection and key predistribution schemes for inventing a new scheme to improve resilience against node capture attack.