Attacks In Wireless Sensor Networks Research Articles

Wormhole Attacks in Wireless Sensor Networks (Wsn) & Internet of Things (IoT): A Review

In the current world people are using the sensing networks called IoT and WSN as the subset of IoT in various applications. The employment of these sensor networks is rapidly increasing. Due to the longer usage of these sensor networks security issues are eventually happening and has the possibility of developing the attacks in the network. In this review, focuses on wormhole attacks in wireless sensor network (WSN) and Internet of Things (IoT) creating a tunnels i.e., wormhole link in between source and the destination node in the network. The classification of wormhole attack in both WSN and IoT are presented based on the mode of attacker. The detection mechanisms of wormhole attack are specified in both WSN and IoT. It hypothesizes the detection strength is more in IoT than the WSN based on the analysis, the parameters of the detection algorithm that the WSN is about 20% while in IoT is 70%.

DETECTING WORMHOLE ATTACKS IN WIRELESS SENSOR NETWORKS

Wireless sensor networks (WSN), consisting of wireless sensors and control devices and methods of self-organization using intelligent algorithms, demonstrate broad prospects. WSNs are subject to numerous types of threats and attacks. One of which is a wormhole attack. This article provides an overview of wormhole attack detection methods in WSN, as well as a new optimized method for detecting this attack. The detection method is based on the removal of the edges of the wormhole and causes significant changes in the length of the shortest paths between network nodes.The accuracy of the proposed algorithm is not affected by the number of wormholes

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.

Defense Countermeasures for DoS Attacks in WSNs Using Deep Radial Basis Networks

Wireless sensor networks (WSNs) are inclusive of tiny sized battery dependent autonomous gadgets which are deployed in the decentralized manner in order to monitor physical conditions. Deployed nodes are connected with routers and gateways to support much kind of real-time applications. In the prevailing open access situation, to secure authentication, nodes can be verified by securing authentication. To achieve secured communication, in real-time applications, many lightweight authentication mechanisms are introduced. But owing to lack of synchronization, WSNs become highly susceptible to DoS attacks. Deep learning based Radial basis networks have been proposed in this paper for detection and isolation of attacks. In this article DRBN algorithm is described for efficient detection of DoS attacks, such as exhaustion, jamming, flooding and so on. Extensive modeling tests are carried out in order to reliably isolate the malicious nodes, and they are discovered to be more resistant to DoS attacks. Apart from a decrease in energy consumption and FPR of 2.8%, the proposed simulation result reveals that 94.6% detection rate and 98% accuracy can be achieved.

Kriging Regressive Pseudo Randomized Lamport Certificateless Signcryption based Replication Attack Detection for Secure Routing in WSN

A Wireless Sensor Network (WSN) includes small, low-cost, and resource-constrained sensor nodes to perform monitoring. Each node is free to move and susceptible to various attacks. Therefore, WSNs have huge attention to communication security. Identifying and monitoring attack node is complex in distributed network and creates node replication attacks in WSN. In order to improve secure routing, a novel technique called Kriging Regressive Attack Detection based Pseudo Randomized Lamport Certificateless Signcryptive Secure Routing (KRAD-PRLCSSR) is proposed. KRAD-PRLCSSR enhances the secure routing by detecting the replication attack in WSN. Initially, the neighbor discovery is performed using the Kriging Regression function. Energy of every sensor node is calculated and validates with threshold value. Based on estimated results, normal node or replica node is identified. Then the normal node is chosen as neighboring node. After that, secure transmission is performed using Pseudo Randomized Lamport one-time Generative Certificateless Signcryption. By applying the signcryption technique, Pseudo Randomized private and public keys are generated for each sensor node. After the key generation, the source node performs the encryption as well as a digital signature. The encrypted data and signature are sent to receiver. Finally, signature is verified at the receiver side to decrypt the data. This helps to increase the security of routing in WSN. The simulation analysis of KRAD-PRLCSSR technique is performed with the different metrics.

Secure Localization Algorithms Against Localization Attacks in Wireless Sensor Networks

The accuracy of the location estimation of sensors in wireless sensor networks (WSNs) is important to many applications such as environment surveillance applications. In large-scale and distributed WSNs, locations of the sensors may not be recorded during the deployment process, resulting in the need of localization techniques to help collect the sensor location information. WSNs are highly vulnerable to localization attacks since the localization techniques totally rely on the neighboring relations to determine the location of sensors. In other words, some of malicious sensor nodes can take the advantage of the neighboring relations to manipulate the localization process. In this paper, we propose two localization algorithms to overcome both common types of localization attacks, independent attacks and collusion attacks. In the first algorithm, we propose the Improved Randomized Consistency Position Algorithm to determine the location of unknown nodes using the location estimation information provided by the three random anchor nodes and using the PSO algorithm paradigm to identify the coordinates of the unknown nodes’ location. In addition, the second algorithm, which is a combination of voting method, location optimization, and the PSO algorithm paradigm to identify the location of unknown nodes, is referred to as the Enhanced Attack-Resistant Secure Localization Algorithm. Through rigorous theoretical analysis and experimentation, we demonstrate that our proposed localization algorithms yield better performance comparing to the existing methods.

Secure and Shortest Path Routing Bypassing Attackers in Wireless Sensor Networks

In wireless sensor networks (WSNs), energy constraint of node is the major issue, as the sensor may be deployed in the area where energy backup or quick replacements may not be available. In such cases, preserving the node energy and prolonging the network life time play crucial role in wireless sensor networks. Similarly, sensor nodes are highly vulnerable to attacks, attackers can easily tamper the sensor node and compromise it. Thus to overcome above stated two problems, the proposed work ensures shortest path routing, which ensures network life time of sensor nodes and the trust based routing, which avoids node compromise attacks. The proposed shortest path routing algorithms takes route through multi-hop nodes to corresponding sink. The shortest path based on the geographical routing strategy chooses the nodes nearest to the routing node and sink node. The novel routing framework proposed in this work considered shortest path with trust based routes. The node's energy is considered to taking reliable node on the routing path, which ensure the packet delivery and avoids any node failure due to less energy. The node's trust value is evaluated with three type, which ensure that the paths created are more reliable

PERFORMANCE EVALUATION OF MACHINE LEARNING TECHNIQUES FOR DOS DETECTION IN WIRELESS SENSOR NETWORK

The nature of Wireless Sensor Networks (WSN) and the widespread of using WSN introduce many security threats and attacks. An effective Intrusion Detection System (IDS) should be used to detect attacks. Detecting such an attack is challenging, especially the detection of Denial of Service (DoS) attacks. Machine learning classification techniques have been used as an approach for DoS detection. This paper conducted an experiment using Waikato Environment for Knowledge Analysis (WEKA)to evaluate the efficiency of five machine learning algorithms for detecting flooding, grayhole, blackhole, and scheduling at DoS attacks in WSNs. The evaluation is based on a dataset, called WSN-DS. The results showed that the random forest classifier outperforms the other classifiers with an accuracy of 99.72%.

Performance Evaluation of Machine Learning Techniques for DOS Detection in Wireless Sensor Network

The nature of Wireless Sensor Networks (WSN) and the widespread of using WSN introduce many security threats and attacks. An effective Intrusion Detection System (IDS) should be used to detect attacks. Detecting such an attack is challenging, especially the detection of Denial of Service (DoS) attacks. Machine learning classification techniques have been used as an approach for DoS detection. This paper conducted an experiment using Waikato Environment for Knowledge Analysis (WEKA)to evaluate the efficiency of five machine learning algorithms for detecting flooding, grayhole, blackhole, and scheduling at DoS attacks in WSNs. The evaluation is based on a dataset, called WSN-DS. The results showed that the random forest classifier outperforms the other classifiers with an accuracy of 99.72%.

A Review on Network Layer Attacks in Wireless Sensor Networks

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

An Approach for Stego-Insider Detection Based on a Hybrid NoSQL Database

One of the reasons for the implementation of information security threats in organizations is the insider activity of its employees. There is a big challenge to detect stego-insiders-employees who create stego-channels to secretly receive malicious information and transfer confidential information across the organization’s perimeter. Especially presently, with great popularity of wireless sensor networks (WSNs) and Internet of Things (IoT) devices, there is a big variety of information that could be gathered and processed by stego-insiders. Consequently, the problem arises of identifying such intruders and their transmission channels. The paper proposes an approach to solving this problem. The paper provides a review of the related works in terms of insider models and methods of their identification, including techniques for handling insider attacks in WSN, as well methods of embedding and detection of stego-embeddings. This allows singling out the basic features of stego-insiders, which could be determined by their behavior in the network. In the interests of storing these attributes of user behavior, as well as storing such attributes from large-scale WSN, a hybrid NoSQL database is created based on graph and document-oriented approaches. The algorithms for determining each of the features using the NoSQL database are specified. The general scheme of stego-insider detection is also provided. To confirm the efficiency of the approach, an experiment was carried out on a real network. During the experiment, a database of user behavior was collected. Then, user behavior features were retrieved from the database using special SQL queries. The analysis of the results of SQL queries is carried out, and their applicability for determining the attribute is justified. Weak points of the approach and ways to improve them are indicated.

Data tampering attacks diagnosis in dynamic wireless sensor networks

Real-world wireless sensor networks (WSNs) usually have dynamic topologies and are vulnerable to data tampering attacks, which may possibly lead to serious consequences. This paper addresses the issue of defending against data tampering attacks to dynamic WSNs. First, a hybrid diagnosis algorithm is proposed, which consists of three phases: data comparison phase, distributed diagnosis phase, and global diagnosis phase. In the distributed diagnosis phase, a majority voting mechanism and a multi-round diagnosis mechanism are introduced. As the phase may lead to inconsistent diagnosis results among different sensor nodes, we introduce a global diagnosis phase to overcome the shortcoming. Next, we evaluate the performance of the diagnosis algorithm, accompanied with a few examples. Finally, to achieve near perfect performance, some experiments on how to choose the parameters of the algorithm are provided. This work provides guidance for dealing with data tampering attacks in dynamic WSNs.

Lightweight models for detection of denial‐of‐service attack in wireless sensor networks

Wireless sensor networks (WSNs) are becoming more popular in recent times due to the availability of low cost, the short transmission range of sensor nodes and their easy deployment in different application areas; however, security is crucial for WSNs due to the nature of data they transmit in most cases in their locations. Sensor nodes transmit through an open medium and due to the short communication range of each sensor node, a node may need to transmit through many hops to a final destination. Denial-of-service (DoS) is one of the attacks that can be launched against WSNs to prevent legitimate nodes from transmitting/receiving data packets. Developing an attack detection and countermeasure system to mitigate the effect of DoS is not an easy task. This work proposes lightweight models for DoS attack detection in WSNs. We developed different lightweight models and algorithms for the detection and elimination of DoS in WSNs and experimented the same. The performance of data delivery ratio, delay, detection rate, energy consumption, and false-positive was carried out. The experimental results show that the proposed scheme achieved a high true-positive detection rate, low false-positive detection rate, energy-efficient, and high data reliability. Numerical examples are presented to further validate the effectiveness of the proposed scheme.

Survey of wormhole attack in wireless sensor networks

From the last decade, a wireless sensor network (WSN) has a very important role over the networks. The primary features of WSN include satellite communication, broadcast channel, hostile environment, medical system and data gathering. There are a lot of attacks available in WSN.In wormhole attack scenario is brutal from other attacks, which is smoothly resolved in networks but tough to observe. This survey paper is an experiment to observing threats and also focuses on some different method to identify the wormhole attacks.

Survey of wormhole attack in wireless sensor networks

From the last decade, a wireless sensor network (WSN) has a very important role over the networks. The primary features of WSN include satellite communication, broadcast channel, hostile environment, medical system and data gathering. There are a lot of attacks available in WSN. In wormhole attack scenario is brutal from other attacks, which is smoothly resolved in networks but tough to observe. This survey paper is an experiment to observing threats and also focuses on some different method to identify the wormhole attacks.

Study of Denial of Service DoS Attack in Wireless Sensor Networks with Power Constraints

Wireless sensor networks (WSN) are highly vulnerable to attacks and constraints on resources like power, processing, and radio signal. In high constraint and vulnerable environments adapting to detection and defence mechanisms is a challenge. In this paper analysis of Distributed Denial of Service detection mechanisms of Sybil, Sinkhole, and Wormhole attack for power optimization is carried out based on the power consumption parameter. Power consumption attributes affects the efficiency of nodes during the time of attacks. Energy utilization and conservation also depend upon certain parameters comprising usage of network, changes in topology, climatic changes, no of sensors connected, memory utilization on sensors, and security issues. The analysis takes various attacks detection mechanisms into consideration and a comparative study is projected upon power optimization parameters. This study could contribute to the aspect of extending the activity of the sensor in various applications such as whether monitoring and recording of wildlife movement.

Real Time Spatial Transmission Analysis Based Improved Black Hole Attack Detection In WSN For Improved QoS


 
 
 
 ABSTRACT
 Despite the extensive utilisation of Wireless Sensor Networks (WSNs), they remain vulnerable to various types of security breaches, including black hole attacks. The detection and prevention of black hole attacks are crucial for safeguarding the security and reliability of Wireless Sensor Networks (WSNs). The present study introduces a technique for real-time detection of black hole attacks in Wireless Sensor Networks (WSNs) by means of spatial transmission analysis, with the aim of enhancing Quality of Service (QoS). In order to enhance the identification of black hole attacks, we propose a methodology that leverages real-time spatial transmission analysis to quantify the Euclidean distance separating the source and destination nodes of every packet. An algorithm with a high detection rate and a low false positive rate was developed by us. In order to evaluate the efficacy of the proposed mechanism and establish a comparative analysis with existing methodologies, we conducted extensive experimentation. The results indicate that the proposed mechanism exhibits superior performance compared to existing techniques in terms of both detection rate and false positive rate. Our proposed mechanism aims to safeguard the quality of service of Wireless Sensor Networks (WSNs) against black hole attacks in real-time.
 
 
 

Machine Learning Based Detection and a Novel EC-BRTT Algorithm Based Prevention of DoS Attacks in Wireless Sensor Networks

Wireless sensor network allows nodes to communicate with another using wireless channels so that it is necessary for packet delivery to the nodes. Black hole attack occurs when a malicious node receives and misleads a set of nodes in the wireless network to drop the data they received. Wormhole attack occurs when malicious nodes create data delivery latency between two nodes in the wireless network. In this paper, we propose a Machine Learning based Naive Bayes Classifier to detect and Enhanced Code-based Round Trip Time based method prevent those two vital attacks. The black hole attack can be analyzed using this technique with the help of authorize code. Similarly, the wormhole attack and fake destination attack can be prevented by trip time to find the data delay time to reach the destination. In the case of black hole attack, a node which does not have authorized code will be considered as malicious node and discarded. If the packet does not arrive at the destination within the specific time, it will automatically detect the occurrence of wormhole in the wireless network. The proposed method is experimentally validated on network simulator (NS2). The main advantage of the proposed method is to reduce the communication overhead.

Non-cryptography countermeasure against controllable event triggering attack in WSNs

Compressive sensing-based data collecting is a promising technique, which can significantly reduce the communication costs in wireless sensor networks (WSNs). However, a recent study shows that it is vulnerable to controllable event triggering attack (CETA): after compromising a sensor and carefully manipulating the environmental elements around a target in the subtree rooted at the sensor, an attacker is able to infer sensitive parameters of the target. The existing countermeasure is purely based on cryptography and requires central control. In this paper, we propose a lightweight non-cryptography-based approach by termly modifying the structure of the data gathering tree. However, how to efficiently and effectively construct the tree is an open problem. To solve the problem, we create a novel topology-based coding scheme and a set of distributed algorithms to mitigate the CETA attack. By adopting this approach, the time for successfully launching a CETA attack is significantly increased. Extensive simulations and experiments show that our solution can efficiently and securely build different data gathering trees for consecutive sensing tasks.

Attack Resilient Trust and Signature-based Intrusion Detection Systems

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.