Securing Wireless Sensor Networks, Types of Attacks, and Detection/Prevention Techniques, An Educational Perspective

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.

The increasing complexity and low-power constraints of current Wireless Sensor Networks (WSN) require efficient methodologies for network simulation and embedded software performance analysis of nodes. In addition, security is also a very important feature that has to be addressed in most WSNs, since they may work with sensitive data and operate in hostile unattended environments. In this paper, a methodology for security analysis of Wireless Sensor Networks is presented. The methodology allows designing attack-aware embedded software/firmware or attack countermeasures to provide security in WSNs. The proposed methodology includes attacker modeling and attack simulation with performance analysis (node’s software execution time and power consumption estimation). After an analysis of different WSN attack types, an attacker model is proposed. This model defines three different types of attackers that can emulate most WSN attacks. In addition, this paper presents a virtual platform that is able to model the node hardware, embedded software and basic wireless channel features. This virtual simulation analyzes the embedded software behavior and node power consumption while it takes into account the network deployment and topology. Additionally, this simulator integrates the previously mentioned attacker model. Thus, the impact of attacks on power consumption and software behavior/execution-time can be analyzed. This provides developers with essential information about the effects that one or multiple attacks could have on the network, helping them to develop more secure WSN systems. This WSN attack simulator is an essential element of the attack-aware embedded software development methodology that is also introduced in this work.

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

Wireless sensor networks (WSNs) are becoming more prevalent in a wide range of uses, including tracking the environment, medical care, and military monitoring. Yet, security, energy efficiency, and overall efficiency are important concerns for these networks. Ensuring the security of data transmission while maintaining energy efficiency is critical for the longevity and reliability of WSNs. This paper proposes a novel method to enhance both security and efficiency in WSNs done the integration of metaheuristic optimization algorithms. The proposed method leverages advanced optimization techniques such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) to dynamically optimize key parameters, including node deployment, routing protocols, and encryption schemes. By optimizing the network topology, routing paths, and energy consumption strategies, the algorithm significantly improves data security, minimizes energy usage, and enhances network lifespan. The simulation results show that the suggested methodology beats standard methods in terms of throughput, security resilience and energy efficiency. The findings highlight the potential of metaheuristic optimization algorithms as a robust tool for improving WSN security and operational efficiency in real-world applications

A new hierarchical belief-rule-based method for reliability evaluation of wireless sensor network

Enhancing cyber security in WSN using optimized self-attention-based provisional variational auto-encoder generative adversarial network

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.

Wireless sensor networks (WSNs) have been widely applied in many areas for real‐time event detection. They are designed using both mobile and static sensor nodes (SNs) for different applications such as smart parking, environmental monitoring, health care systems, automotive industries, sports, open space surveillance, and so on. WSNs communicate through wireless mediums and are accessible to anyone, which make SNs susceptible to different types of attacks. Distributed denial of service (DDoS) is one such attack. It wastes the limited energy of SNs and causes loss of data packets within a network. A DDoS attack launches a coordinated attack by flooding the target nodes with bogus requests, thus exhausting their resources, and forcing them to deny service to legitimate member nodes. In this study, the authors propose a message analyser scheme for WSNs. The method is capable of detecting compromised SNs vulnerable to a DDoS attack. In addition, it is able to detect all compromised messages transmitted by the attackers to the base station through the sender nodes. The proposed method is compared with other related protocols. The results show that their method can effectively detect and defend against DDoS attacks in WSNs.

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.

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.

Wireless Sensor Networks (WSNs) are turning into an essential portion of our lives. Devoid of assuring WSNs security (WS), there remain no broad implementations of WSNs. Because of sensor nodes’ constrained capacities concerning computation, communication, and energy, giving protection to WSNs remains competitive. Indeed, the procedure of applying WS remains adaptable and vibrant that develops consistently. Attack-defend’s crux in WS could be conveyed by collaborative schemes of interdependency when Game Theory (GT) could be employed to consider communications amidst schemes of logical decision-makers. Hence, learning WS alongside GT possesses greater logic. This study proffers a Multiplayer Reputation-based Coalition Game Model (MRCGM) for seeking the false data injection amidst the nodes. An attacker attempts to sustain an ideal level of belief by correlating the identifications within the network (NW) to initiate a victorious transfer. Concurrently, the attacker should acquire a few charges for sustaining the trustability of its identifications. This proffered procedure employs this notion and turns the attack expensive by fixing a global threshold for nodes or identifications to be trustable and functional within the NW. Additionally, the beneficial function of the attacker and defender is as well described. The MRCGM has been correlated with 2 advanced methodologies like security-aware routing scheme employing repeated game (SARSRGM) paradigm and a Game-based Fuzzy Q-learning (GBFQL) scheme concerning diverse criteria. Consequently, the proffered MRCGM attains 124 kbps of Packet Drop Rate, 76.14% of energy efficiency, 96.1% of detection accuracy, 25.45% of energy consumption, and 102.45 ms of routing latency.

A comparative performance evaluation of intrusion detection techniques for hierarchical wireless sensor networks

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.

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