Analytical modeling of attacks in wireless sensor networks for attack detection

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.

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

A Wireless Sensor network (WSN) consists of large number of low cost low power sensor nodes. The nature of wireless sensor networks makes them very attractive to attackers. One of the most popular and serious attacks in wireless sensor networks is sink hole attack and most existing protocols to defend against this attack used cryptographic methods with keys. In sink hole attack a sensor node will have a lot of false neighbors. Wireless Sensor Network has a dynamic topology, intermittent connectivity, and resource constrained device nodes. Researchers over the past years have encouraged the use of mobile agent to overcome these challenges. The proposed scheme is to defend against sink hole attacks using mobile agents. Mobile agent is a program segment which is self controlling. They navigate from node to node not only transmitting data but also doing computation. They are an effective paradigm for distributed applications, and especially attractive in a dynamic network environment. A routing algorithm with multiple constraints is proposed based on mobile agents. It uses mobile agents to collect information of all mobile sensor nodes to make every node aware of the entire network so that a valid node will not listen the cheating information from malicious or compromised node which leads to sink hole attack. The significant feature of the proposed mechanism is that it does not need any encryption or decryption mechanism to detect the sink hole attack. This mechanism does not require more energy than normal routing protocols like AODV. Here we implement a simulation-based model of our solution to recover from a Sinkhole attack in a Wireless Sensor Network. The mobile agents were developed using the Aglet.

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.

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

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.

In recent years, with the development of the Internet of things industry, wireless sensor networks, as one of the most important technologies in its sensing layer, have been further studied and applied. However, the security threats it faces are becoming more and more diverse, and the security needs are becoming more and more urgent. However, wireless sensor network nodes are mutually exclusive, which leads to low accuracy of attack path prediction when malicious attacks occur. In order to ensure the security of wireless sensor networks, a hidden Markov based malicious attack path prediction method for wireless sensor networks is designed. The method of authentication coding is introduced to realize data integrity authentication. Extract the malicious software gene sequence, define the constraint conditions of the state transition probability matrix, build the attack means deduction model based on Hidden Markov, and realize the malicious attack path prediction. Experimental results: the average accuracy of the proposed method in four malicious attack scenarios is 73.583%, 55.674%, 32.923% and 85.014%, respectively. The results also verify that the proposed method improves the accuracy of malicious attack path prediction in wireless sensor networks based on Hidden Markov model and has good feasibility. It provides an important basis for wireless sensor network administrators to implement defense strategies in time.

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 (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.

The protection of wireless sensor networks (WSN) against malware attacks is crucial. The paper discusses the issue of malware attacks in WSN, which are commonly used for monitoring and surveillance in various applications. Due to resource constraints, sensor nodes in WSN are vulnerable to malware attacks, which can spread rapidly and paralyze the network. The development of new technologies such as IoT, Industry 4.0 has increased the importance of WSN, and it has become essential to address the challenges posed by the resource‐constrained nature of sensor nodes and security concerns. In this paper, a model is considered with two exposed states to investigate the behaviour of malware spreading in WSN, and a SE 1 E 2 IR (Susceptible—Exposed State 1 ‐ Exposed State 2 ‐ Infectious—Recovered) model is proposed. The model is formulated as a system of differential equations, and its equilibrium and stability are examined. The basic reproduction number (R 0 ) is also calculated as a key parameter that characterizes the spread of malware in the network. This parameter helps to identify the conditions under which the network will remain malware‐free or when it will experience an outbreak of malware. The proposed model provides a mechanism for the earlier detection of malware occurrences in WSN, and also discusses the effect of connectivity and coverages on the propagation of malware in the network. The paper also includes a comparative study of the proposed model with existing models; extensive theoretical study and computation analysis are performed to validate the proposed model.

One of the most common attacks against Wireless Sensor Networks is the wormhole attack. In this attack, the enemy deploys two malicious nodes in two different areas of the network and establishes a high-speed dedicated channel between these two. This will cause the normal nodes in two different areas wrongly think that they are two-hop neighbors. Therefore, this attack will greatly affect the routing algorithms. In this paper, a new distributed algorithm is provided to deal with the wormhole attack. The main idea of the proposed algorithm is to discover separate routes between pairs of two-hop neighboring nodes. The proposed algorithm was implemented and evaluated in terms of true and false detection rate by performing a series of experiments and the results were compared with the base algorithm. The test results showed that the proposed algorithm has desirable efficacy.

Nowadays, the Internet of Things (IoT) is growing rapidly. IoT needs to handle efficient information to handle intelligent applications. There are many applications for Wireless Sensor Networks (WSNs), which act as an important interface between the physical world and the IoT. The continuous object boundary detection is one of the most widely used WSN applications in industry. Traditional WSNs are utilized to track leakage from continuous objects require costly long-term maintenance. To address this issue, sparse WSNs are employed. However, the inaccuracy of sparse networks poses a significant problem when using information about continuous objects to arrange retreat paths for individuals. In this paper, a Robust Sensing-Hole Detection Integrating with Rotation-Invariant Coordinate Convolutional Neural Networks in Sparse Wireless Sensor Network (RSHD-RICCNN-SWSN) is proposed. Initially, fill sensing holes by identifying their virtual points and calculating their variation rates. Then Rotation-Invariant Coordinate Convolutional Neural Networks(RICCNN) is used for detecting the boundary of continuous objects. Finally, Giza Pyramid Construction Optimization Algorithm (GPCOA) is employed for optimal path selection. The proposed RSHD-RICCNN-SWSN method is implemented in MATLAB. The performance measures including Space Significance, Data Significance, Network Lifetime, Energy Consumption, Success Rate, Time Complexity, Detection Accuracy, Network Throughput, and Scalability is evaluated. The proposed RSHD-RICCNN-SWSN method attains 20.58%, 26.73% and 24.62% higher Network Lifetime, 20.98%, 26.66% and 16.32% higher Data Significance, 10.78%, 20.47%, and 12.86% lower Energy Consumption when compared with existing models: Localization and detection of multiple attacks in wireless sensor networks using artificial neural network (LDMA-WSN-ANN), Genetic algorithm for cross-layer-based energy hole minimization in wireless sensor networks (GACL-EHM-WSN), Q-learning dependent vegetation evolution for numerical optimization and wireless sensor network coverage optimization (QLVE-WSN-NOC)respectively.

Information attacks and security in wireless sensor networks of industrial SCADA systems

Over the past few years, the devices in Wireless Sensor Networks (WSN) are growing exponentially due to the emergence of many sophisticated applications. This tremendous growth leads to serious security challenges, and the devices of WSN should be protected from various attacks. WSN can be configured dynamically without fixed infrastructure and the devices can be talked with one another in an ad-hoc manner. Due to the dynamic nature of WSN, routing is considered as the challenging task that should be performed efficiently with robust routing mechanism. Even though many routing schemes have been emerged for WSN, they are not well scalable in very large-scale environment. This work introduces multi path routing strategy, and the routing will be selected based on trusted nodes. First, the trusted nodes are identified using trusted metrics of each node in the network. These metrics are calculated based on the threshold value of nodes. Then, secure routing is established by isolating node capturing attacks from the path. The performance of the work is analyzed in terms of packet loss, computational time and throughput. The paper compares the performance with the state-of-the-art routing schemes such as EMBTR (Enhanced Multi Attribute Based Attack Resistance), TSRM (Trust based secure routing model), and TARF (Trust-aware routing framework for WSNs). The outcome of the simulation shows that the proposed scheme outperforms the other state-of-the-work in terms of computational cost, throughput, and delay.

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.