Attacks In Wireless Sensor Networks Research Articles

Analytical Solution of the Effect of Awareness Program by Media on the Spread of an Infectious Disease by Homotopy Perturbation Method

Wireless sensor networks (WSNs) have received wide-ranging considerationdue to their boundless potential in civil and military applications. Maliciousself-replicating codes, known as malware, pose substantial threat to the wireless computing infrastructure. The attacks of the malicious signals in the WSNare epidemic in nature. Biological epidemic models will be helpful to understand the dynamical behavior of the malware attack in WSN. In this paper,A (SEIRS-V) Susceptible - Exposed - Infected - Recovered - Susceptible witha Vaccination compartment, describing the undercurrents of worm propagation with respect to time in wireless sensor network (WSN) is considered. Theanalytical solution of WSN is obtained by Homotopy Perturbation Method.Numerical results are obtained and are graphically interpreted using Maple.The results assures that the dynamics of worm propagation in WSN by theproposed model exhibits rich dynamics.

Two-Hop Monitoring Mechanism Based on Relaxed Flow Conservation Constraints against Selective Routing Attacks in Wireless Sensor Networks.

In this paper, we investigate the problem of selective routing attack in wireless sensor networks by considering a novel threat, named the upstream-node effect, which limits the accuracy of the monitoring functions in deciding whether a monitored node is legitimate or malicious. To address this limitation, we propose a one-dimensional one-class classifier, named relaxed flow conservation constraint, as an intrusion detection scheme to counter the upstream node attack. Each node uses four types of relaxed flow conservation constraints to monitor all of its neighbors. Three constraints are applied by using one-hop knowledge, and the fourth one is calculated by monitoring two-hop information. The latter is obtained by proposing two-hop energy-efficient and secure reporting scheme. We theoretically analyze the security and performance of the proposed intrusion detection method. We also show the superiority of relaxed flow conservation constraint in defending against upstream node attack compared to other schemes. The simulation results show that the proposed intrusion detection system achieves good results in terms of detection effectiveness.

Empowered AODV Protocol in Wireless Sensor Network using Three Variable RSA Cryptosystem

Background: The Wireless Sensor Network (WSN) is a type of networks which primarily designed for the purpose of monitoring in remote areas. It consists of communicating nodes (called sensor's) which communicate each other to share their data and passing the information to the central node. In many applications like defence requires the secure communication of information. However, due to the numerous characteristics of WSN such as open shared communication channel, limited memory, and processing power of sensors, etc. these networks are vulnerable to various attacks such as black hole, gray hole, etc. Objective: The objective of the paper is to secure the AODV routing protocol in WSN using cryptography techniques. Methods: In this paper, the Ad hoc On-demand Distance Vector (AODV) routing protocol has been chosen for information routing because of their lightweight processing capability. To provide secure communication in WSN, the AODV routing protocol is secured by utilizing the RSA key generation algorithm. Here, RSA with three variables (three prime numbers) is employed instead of two variables. Results: The effectiveness of the proposed approach in handling black hole attack is being verified through the simulation results obtained from the experiments conducted using Network Simulator tool (NS2). The three popular performance metrics namely Average End-to-End Delay, Packet Delivery Ratio, and Average Throughput are used for evaluation purpose. These results are observed under different pose time and varying number of malicious nodes. Conclusion: In this paper, a new three variable RSA cryptosystem-based security model is proposed to protect the communication against the Black Hole (BH) attack in wireless sensor networks. The use of three variables instead of two variables allows our model to provide more security as compared to other methods. Simulation results obtained from the experiments carried out using NS2 tool evident the performance of the proposed model over original AODV and other previous models.

Fuzzy-Based Reliability Prediction Model for Secure Routing Protocol Using GA and TLBO for Implementation of Black Hole Attacks in WSN

This study manifests a fuzzy-based trust prediction model for detecting malicious nodes in wireless sensor networks (WSN) by preventing black hole attack. Besides, a new routing protocol based on the shortest path and trust to the path nodes is presented with a fuzzy estimator, which uses the data mining methods to detect the malicious nodes (black holes). In a black hole attack, a malicious node selects the RREP (Route Replay) message as the shortest path from the source node to the destination node. After that, the packet sent to the malicious node is not received by the network. Eventually, the malicious node releases the entire data packet instead of sending it to the destination node. Optimal path selection is performed according to different algorithms on the objective function and its results are observed for route selection. After defining the objective function, different algorithms such as Genetic Algorithm (GA) and teaching–learning-based optimization (TLBO) are utilized for routing. For each algorithm, the objective function for the most secure node is evaluated and simulated based on the parameters defined in fuzzy logic. Based on the simulation results under MATLAB software, TLBO algorithm has obtained the best response for path selection with the least cost for the target performance. Significantly, the proposed method is simple and based on the exchange of control packets between the sensor node and the base station. In accession, the results show that the proposed algorithms are effective in detecting and preventing black hole attacks.

DLDM: Deep learning-based defense mechanism for denial of service attacks in wireless sensor networks

Wireless Sensor Networks (WSNs) include small battery-based self-governing devices that are deployed in a distributed manner to supervise the environmental or physical circumstances. The routers and gateways are connected to the deployed nodes to support many real-time applications. Due to open access, the security issue arises in WSN. In this circumstance, the external users can be verified by securing authentication is necessary one. In real-time applications, to achieve secured communication they have made many lightweight authentication mechanisms. But WSNs are highly susceptible to DoS attacks as it lacks the synchronization between nodes during data routing. In this paper, a new lightweight DoS detection scheme Deep Learning-based Defense Mechanism (DLDM) has proposed to detect and isolate the attacks in Data Forwarding Phase (DFP). This paper describes the new algorithm for the successful detection of DoS attacks, such as exhaustion, jamming, homing, and flooding. We conduct extensive simulation experiments that can accurately isolate the adversaries and it is more resilient to DoS attacks. Our proposed simulation result shows that it can achieve a high detection rate, throughput, packet delivery ratio, and accuracy. This also reduces the energy consumption and the false alarm rate.

Lightweight and Energy-Efficient Implementation of an Unclonable WSN Nodes on PSoC

An adversary can easily capture a wireless sensor network (WSN) node and extract the security information stored in the node, including keys, shared secrets and so on. Due the resources limitation, the traditional encryption and authentication protocols are impractical on WSN nodes. In this letter, we proposed a lightweight and energy-efficient anti-clone design for the WSN nodes, in which physical unclonable function (PUF) is adopted to enhance the security of the WSN nodes. The new structure aims at achieving a low cost and low energy method to prevent the node to be captured and cloned by the adversary. Unclonable WSN nodes are implemented on cypress PSOC 4, in which a configurable PUF structure is accomplished by use the universal digital block. Experimental results show that the proposed system can prevent the capture and clone attack in wireless sensor network, which is unavailable in previous WSN nodes.

Multi-stage secure clusterhead selection using discrete rule-set against unknown attacks in wireless sensor network

Security is the rising concern of the wireless network as there are various forms of reonfigurable network that is arised from it. Wireless sensor network (WSN) is one such example that is found to be an integral part of cyber-physical system in upcoming times. After reviewing the existing system, it can be seen that there are less dominant and robust solutions towards mitigating the threats of upcoming applications of WSN. Therefore, this paper introduces a simple and cost-effective modelling of a security system that offers security by ensuring secure selection of clusterhead during the data aggregation process in WSN. The proposed system also makes construct a rule-set in order to learn the nature of the communication iin order to have a discrete knowledge about the intensity of adversaries. With an aid of simulation-based approach over MEMSIC nodes, the proposed system was proven to offer reduced energy consumption with good data delivery performance in contrast to existing approach.

Node Density Based Security Level Determining to Prolong the Lifetime of WSN through Network Conditions

A wireless sensor network (WSN) consists of sensor nodes with low cost and limited performance. The energy needed for node management is important because it is difficult to manage the deployed nodes. Several traditional security protocols prevent several known attacks in WSNs, such as s false report injection attack. However, the traditional protocols do not consider the false traffic ratio and geographical environment factors. Therefore, to extend the network lifetime, a new design is needed that maintains security and considers environmental factors. This paper introduces a method to increase the energy efficiency and increase the detection rate of false reports by adjusting the security strength of the report generated at the node according to the node density and geographical location. The proposed method has the advantage that it can be applied to various security protocols based on Message Authentication Code (MAC).

Competence computation of attacks in wireless sensor network

Wireless sensor network holds a very important space in daily routine. These sensors are connected to each other through Wireless Sensor Network and these networks collect data for various aspects. As this data is very important, it is prone to many attacks. Hence, we need to prevent the network from the attacks to maintain the integrity of data. In this paper, we have presented a framework to analyze various attacks in WSN. The framework is applied on black hole, flooding and gray hole attacks. The simulation of framework is done in ns2. A detailed analysis of packet delivery ratio, drop rate, throughput, density of packet drop, packet received, packet sent and success rate is done. We also conclude that our framework is best suited for analysis of attacks in WSN.

An intelligent based healthcare security monitoring schemes for detection of node replication attack in wireless sensor networks

Due to the increased growth of elderly people in recent years, healthcare systems face many challenges on the money spent for those people. Both quality and affordability has to be provided by the new technology which is the today’s need. When applying WSN technologies, the advantages such as continuous monitoring with alert mechanisms and relative information are to be satisfied. Among the other challenges, due to the deployed environment, security is a key challenge. As gateway connects to the wireless networks, it is the target area for many adversaries to launch various attacks. Initially, attacker launches node compromise attack which leads to node replication attack. The introduced security methods for intelligent healthcare monitoring system effectively detect replication attack and provide protection to the system. The potential application of proposed methods namely Exponential Moving Average based Replica Detection (EMABRD), Secured Ant Colony Optimization (SACOP) and Fingerprint based Zero Knowledge Authentication (FZKA) is applied to a real time environment. While comparing three algorithms, SACOP has higher detection probability of malicious nodes at the expense of increased storage and communication overheads over EMABRD and FZKA. FZKA performs better compared to EMABRD in terms of detection probability but at the cost of increased overheads. So, among the three algorithms, EMABRD is better in terms of overheads and SACOP is better in terms of detection probability.

RETRACTED ARTICLE: Novel Sybil attack detection using RSSI and neighbour information to ensure secure communication in WSN

Wireless sensor networks (WSN) are generally employed in unattended hostile areas such as forest monitoring, agriculture fields, military battlefields. An adversary can physically capture WSN deployed in a hostile environment. Once an adversary captures a node, the cryptographic information and the software program can be easily extracted. Also, the adversary can reprogram the software inside the node. After reprogramming, the adversary can replicate and deploy the node with multiple identities to do malicious activities. This kind of identity theft attack can be classified as Clone attack or Sybil attack. These identity theft attacks are addressed by many distributed, centralised and localised solutions. Most of these solutions utilise private/public key, and symmetric key algorithms are energy and memory demanding, while the WSN are energy and memory constrained. This paper proposes a novel Sybil attack detection protocol (NoSad) to identify and isolate the Sybil attack in WSN. This protocol is a localised method using intra-cluster communication and RSSI value to identify the Sybil node. The proposed protocol is simulated extensively with various topologies, and obtained results prove that the protocol is highly efficient in detection ratio, energy utilisation, memory usage, computation and communication requirement. This protocol may be used in any resource-constrained WSN to obtain a satisfactory result. This work provides a solution for different scenarios of Sybil node position to counter the Sybil attack in WSN.

AI-Based Techniques for DDoS Attack Detection in WSN: A Systematic Literature Review

Wireless Sensor Networks (WSNs) are currently being used in various industries such as healthcare, engineering, the environment and so on. Security is a significant issue for WSN due to its infrastructure and limited physical security. Distributed Denial of Service (DDoS) is one of the most vulnerable attacks that can be defined as attacks launched from multiple ends into a set of legitimate sensor nodes in the WSN to drain their inadequate energy resources. Nowadays, Artificial intelligence techniques are performing better accuracy than the traditional methods to detect intrusion for the various attack. This Systematic Literature Review (SLR) attempts to investigate the current status of DDoS detection techniques and to identify the most capable and effective detection system using artificial intelligence to detect distributed DoS attack. Preferred Reporting Item for Systematic Review and Meta-Analysis (PRISMA) statement is used to conduct this review. Based on 15 out of 983 that met inclusion criteria, Support Vector Machine (SVM) and Artificial Neural Network (ANN) is the most used AI-based techniques to detect distributed denial of service attack in the wireless sensor network. The performance of AI techniques-based detection system for DDoS attack in WSN is remarkable.

Multilevel Intrusion Detection System to Mitigate Denial-of-Service Attack in Wireless Sensor Networks

Malicious activities over WSN is quite hard to detect as sensors operate in an open network environment. Researchers have offered several solutions but still intrusion detection/prevention is an open issue. In this paper, a scheme is introduced that can analyze the malicious behavior of the nodes over multiple layers. It uses AES algorithm for data encryption and its integrity is insured using SHA512 method. Simulation results show that it outperforms as compared to traditional WatchDog method under QoS constraints. Simulation result show that it outperforms as compared to traditional watchdog scheme.

Optimal defense resource allocation for attacks in wireless sensor networks based on risk assessment model

Abstract To solve the unreasonable allocation problem of network security defense resource, this paper presents a framework for optimal defense resource allocation for minimizing the network risk. The vulnerability power function model of network nodes is built based on nonlinear theory between defense resource and network vulnerability. The network risk assessment model is described by vulnerability model combining with local and global information of node. Numerical studies demonstrate that the strategies are optimum in terms of protection and redundancy allocation of network nodes. Simulation results show that our algorithm achieves the optimum allocation strategies of defense resource. Furthermore, network risk can quickly converge to a lower value.

Design of event-driven control strategy for spoofing attacks in wireless sensor networks

In this study, an event-driven control strategy is developed to counter the spoofing attacks in wireless sensor networks which is considered as a control strategy in stochastic system. We have also devised the corresponding spoofing-proof event-driven transmission mechanism. For this, first, an event-driven state estimator for the underlying stochastic system is designed. Then, using the stochastic stability theory, the event-triggered state estimator gain is derived by means of the stochastic Laypunov function, so that the corresponding state estimation mean square error is compensated. The estimator’s output error is used to suppress the related spoofing attack. The devised event-driven transmission strategy applies some approximate quadratic performance indicators to ensure a measured balance between estimation error, data communication rate and sensor battery life time. Finally, numerical examples are presented to verify the validity of the theoretical results.

The Relative performance of Security protocols and attacks in Wireless Sensor Network

Wireless Sensor Network is organized by arranging a huge number of sensor nodes in a region to the investigation of normally remote locations. A Wireless sensor network contains various applications such as landslide detec­tion, automotive application, health, noise level, environmental, vehicular movement, and battlefield. A Sensor node has different components to complete the work for sensing the data, transmitting, and processing the data into the network. Security is a very challenging issue in the WSNs. Various security threats and attacks may affect or damage the functioning (activity) of these networks. Attack divided the main two types active and passive. This paper focuses on several security attacks like Blackhole, fabrication, Sybil, DoS, sinkhole, wormhole, flood­ing. We have also explained security protocols are 3 categories. Data-Centric, Geographic, and hierarchical pro­tocol. Other are SPIN, LiSP, LEDs, LLSP, LEACH protocols. We explain the term attack threat, vulnerabilities, and security requirements.

How to Detect and Mitigate Sinkhole Attack in Wireless Sensor Network (WSN)

How to Detect and Mitigate Sinkhole Attack in Wireless Sensor Network (WSN)

An Anonymous Channel Categorization Scheme of Edge Nodes to Detect Jamming Attacks in Wireless Sensor Networks.

Wireless Sensor Networks (WSNs) are vulnerable to various security threats. One of the most common types of vulnerability threat is the jamming attack, where the attacker uses the same frequency signals to jam the network transmission. In this paper, an edge node scheme is proposed to address the issue of jamming attack in WSNs. Three edge nodes are used in the deployed area of WSN, which have different transmission frequencies in the same bandwidth. The different transmission frequencies and Round Trip Time (RTT) of transmitting signal makes it possible to identify the jamming attack channel in WSNs transmission media. If an attacker jams one of the transmission channels, then the other two edge nodes verify the media serviceability by means of transmitting information from the same deployed WSNs. Furthermore, the RTT of the adjacent channel is also disturbed from its defined interval of time, due to high frequency interference in the adjacent channels, which is the indication of a jamming attack in the network. The simulation result was found to be quite consistent during analysis by jamming the frequency channel of each edge node in a step-wise process. The detection rate of jamming attacks was about 94% for our proposed model, which was far better than existing schemes. Moreover, statistical analyses were undertaken for field-proven schemes, and were found to be quite convincing compared with the existing schemes, with an average of 6% improvement.

A Modular IoT Hardware Platform for Distributed and Secured Extreme Edge Computing

The hardware of networked embedded sensor nodes is in continuous evolution, from those 8-bit MCUs-based platforms such as Mica, up to powerful Edge nodes that even include custom hardware devices, such as FPGAs in the Cookies platform. This evolution process comes up with issues related to the deployment of the Internet of Things, particularly in terms of performance and communication bottlenecks. Moreover, the associated integration process from the Edge up to the Cloud layer opens new security concerns that are key to assure the end-to-end trustability and interoperability. This work tackles these questions by proposing a novel embedded Edge platform based on an EFR32 SoC from Silicon Labs with Contiki-NG OS that includes an ARM Cortex M4 MCU and an IEEE 802.15.4 transceiver, used for resource-constrained low-power communication capabilities. This IoT Edge node integrates security by hardware, adding support for confidentiality, integrity and availability, making this Edge node ultra-secure for most of the common attacks in wireless sensor networks. Part of this security relies on an energy-efficient hardware accelerator that handles identity authentication, session key creation and management. Furthermore, the modular hardware platform aims at providing reliability and robustness in low-power distributed sensing application contexts on what is called the Extreme Edge, and for that purpose a lightweight multi-hop routing strategy for supporting dynamic discovery and interaction among participant devices is fully presented. This embedded algorithm has served as the baseline end-to-end communication capability to validate the IoT hardware platform through intensive experimental tests in a real deployment scenario.

DSLA: Defending against Selective Forwarding Attack in Wireless Sensor Networks using Learning Automaton

Selective forwarding attacks (SFAs) can harm the mission of critical applications such as military surveillance and forest fire monitoring. In these attacks, malicious nodes behave like normal nodes most of the time, but selectively drop sensitive packets, such as a packet that reports on the movement of opposing forces, and therefore, detection of this kind of attack is hard. In this paper, a fully distributed, dynamic, intelligent, lightweight algorithm based on learning automata is proposed in order to defend against the selective forwarding attack. In this algorithm, an overhearing mechanism, along with the learning automata model, is used to select secure routes for forwarding packets in a multi-hop routing algorithm. Each node is equipped with a learning automaton, which helps the node select the next hop for forwarding its data towards the base station. The proposed algorithm is simulated using J-SIM. Simulation results show the superiority of the proposed algorithm over existing algorithms, such as the single path forwarding algorithm, the multi-hop acknowledge–based algorithm, the multi-data–flow algorithm, the multi-path algorithm, and the neighbor watch system–based algorithm, in packet delivery rate, packet drop rate by malicious nodes, communications overhead, and energy consumption.