Toward Intelligent Intrusion Prediction for Wireless Sensor Networks Using Three-Layer Brain-Like Learning

A scheme for WSNs (wireless sensor networks) security is given by dividing sensing tetrahedron into clusters and using the symmetric polynomials in this paper.The sensing tetrahedron is divided into a number of small grids.All those sensor nodes, both ordinary sensor nodes and heterogeneous sensor nodes are distributed in the sensing tetrahedron.In a grid, all ordinary sensor nodes and heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.All the heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.At last, all sensor nodes establish their keys directly or indirectly in the whole sensing tetrahedron.Analysis and comparison demonstrate this scheme enhances the WSN security, has good network connectivity, saves node storage, reduces network computing load, and extends the network lifetime.

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

Due to the prevalence of Wireless Sensor Networks (WSNs) in the many mission-critical applications such as military areas, security has been considered as one of the essential parameters in Quality of Service (QoS), and Intrusion Detection System (IDS) is considered as a fundamental requirement for security in these networks. This paper presents a lightweight Intrusion Detection System to protect the WSNs against the most important of routing attacks in network layer based on their extracted specifications. The proposed IDS, in contrast to related works that often focuses on a specific attack, covers almost all recognized important routing attacks in WSNs. With the full simulation of the routing attacks and the careful examination of their behavior, we extracted key specifications to identify them in the proposed system. Also, due to local operations provided to detect and significantly reduce communications, the proposed method is a lightweight approach. Another advantage of the proposed method is reducing false alarms rate by applying appropriate thresholds. We considered all performance criteria to evaluate and compare the proposed method. Simulation results show that the proposed system is an effective and lightweight IDS in WSNs due to high detection accuracy, low false alarms rate, and low power consumption.

Wireless sensor network is of prime importance because of its applicability in various domains ranging from healthcare applications to military applications. Security of such networks is important as these carry confidential information. Security of Wireless Sensor Network is divided in three phases, prevention, detection and mitigation. In Prevention phase, care is taken so that attack should not occur. But most of the times it is not possible to prevent attacks, so it is very important to detect them as early as possible so that these will not harm a lot to wireless sensor network and that phase is called as intrusion detection. Once Intrusion has been detected we have to take actions to cure from it and it is called as mitigation. So, Intrusion detection is most important phase as far as security of wireless sensor network is concerned. This paper discusses about various detection methodologies such as Anomaly based, Misuse based and Specification based IDS, various decision making schemes for intrusion. Major focus of this paper is to understand various Intrusion Detection Systems that are proposed for wireless sensor network. These are discussed with different issues with advantages and disadvantages. Finally paper gives future directions for selection of Intrusion Detection System.

Wireless sensor networks are nowadays widely popular and has become an integral part in the military applications for human monitoring, thermal detection etc. Security of Wireless sensor network (WSN) becomes a very important issue with the rapid developme nt of WSN that is vulnerable to a wide range of attacks such as sinkhole attacks due to deployment in the hostile environment and having limited resources. Intrusion detection system is one of the major and efficient defensive methods against attacks in WS N. One such detection technique is black listing technology.But using only Black listing technology is not suitable for a mobile intruder since it was designed considering only a static intruding node in a WSN. So it is necessary to build an energy efficient Intrusion detection system for sinkhole attack by a mobile intruder in WSN. We are intended to design an energy efficient system for detection of sinkhole and elimination of a mobile intruder from WSN nodes using a technology called greylisting. This t echnology uses pre alarm packets to warn the neighboring nodes about the intruder and the energy consumed by the pre alarm packets for making an alarm is much lesser than that of the packets used in black listing technology. Thus this method will serve asthe solution for the dilemma in providing the security for WSN in sinkhole attack.

Nowadays, Wireless Sensor Network (WSN) have created tremendous prominence because this evident benefits while comparing to the conventional cable network are simple in distribution, users mobility over the area of network coverage and ease in new user’s connection. With sudden increase of communications in digital technologies are depressed through traffic of network data. The usage demand in an internet has grown daily use for the present cyber world have influences the requirement of network security. The WSN security can be supported through the strategies of attack and defense using Machine Learning (ML) which perform automated in learning from and adapt to the characteristics of wireless communication which are firm in capturing using the features and model of hand-crafted. However, several security attacks have classified as cyber ones and the attack types are interested in the system tentative as well as spoils the data instead of data stealing. Hence, the paper focuses on Intrusion Detection System (IDS) has done through Stacked Autoencoder (SAE) with Artificial Neural Network (ANN) in sequential patterns method is produced improved security in DoS attacks. Moreover, the dataset of security type for DoS in WSN has mainly focused in identifying attacks, malicious node elimination and transmission data security

Wireless Sensor Networks (WSNs) are used for a wide range of things these days, from watching the environment to improving healthcare systems. But because these networks are used everywhere, they are also open to security risks like spying, data corruption, and node capture. It is very important to address these issues in order to protect the accuracy, privacy, and access of data being sent. Information-theoretic security looks like a good way to deal with these problems because it uses mathematical models to make sure that transmission in WSNs is safe and uses little energy. This essay goes into detail about information-theoretic security in WSNs. It gives a full rundown of the mathematical basics and real-world implications for getting strong security in places with limited resources. The idea of "secrecy capacity" is at the heart of this method. It measures the highest rate at which authorized nodes can communicate privately while preventing spying foes. Utilizing the chance that exists in wireless channels, encrypted keys can be generated without using shared secrets, making them resistant to attempts that try to steal them. Because sensor nodes only have a short power life, energy economy is very important in WSNs. Information-theoretic security methods, like physical layer security and joint communication, are designed to use as little energy as possible while still providing strong security. Nodes can work together to improve signal strength and fight channel fading using cooperative methods. This makes the network last longer without compromising security. This article also talks about the trade-offs between security, energy savings, and communication performance in WSNs. This helps us figure out how to make the best transfer schemes.

Wireless sensor networks (WSNs) have wide applications due to these sensor nodes ease of deployment.But the security of WSNs is still an important issue. Some existed approaches mainly rely on cryptography to ensure data authentication and integrity. These approaches only address part of the problem of security in WSNs. However, these approaches are not sufficient for the unique characteristics and novel misbehaviors or abnormal behaviors encountered in WSNs. In this paper we introduce an implicit security authentication scheme technique ,which can authenticate the sensor nodes based on actions they would carry out anyway. From our preliminary findings support that this is a meaningful approach, whether used to increase usability or increase security in WSNs.

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

In this paper, wireless sensor network (WSN) security issue in the presence of an eavesdropper is analysed. The sensor-sink as well as sensor-eavesdropper channels are assumed to be subjected to generalized-K fading. According to the physical layer security framework, we employ round-robin, optimal sensor and cumulative distribution function-based scheduling scheme to enhance secure connection between sensor nodes and to attain low probability of intercept. Novel analytical results are given in a form of easy-to-compute analytical expressions for the intercept probability. Additionally, asymptotic closed-form expressions are derived and diversity orders of scheduling schemes are determined. Numerical results demonstrate important impacts of fading and shadowing parameters of main and wiretap links, network size and selected scheme on WSN security. Presented results are validated by independent Monte Carlo simulations.

Secure routing of information is one of the major concerns of specialist working in the field of wireless sensor networks (WSNs) as sensor nodes gather information from the physical world and transmit to base stations for authorized users to access via the internet. In a WSN, sensor nodes are deployed most of the times in unattended locations where there is the possibility of authorized or unauthorized access. This and others have brought the need to secure transmitted information from intruders. Though there are many ways to provide security to a network, cryptography has been identified as the best way for these main three security requirements which include: data confidentiality, authentication, and integrity (CIA). In this paper, we present an overview of the present challenges in a WSN and outline a number of countermeasures to solve these challenges. To tackle the challenge of security encountered in existing WSN systems, we propose an efficient and robust encryption and decryption algorithms for secure communication. The major key feature of this algorithm is that it combines both substitution and transposition cipher techniques in order to achieve the encrypted text. The algorithm is the outcome of the inspection of various existing algorithms in this field.

Wireless Sensor Networks (WSNs) are used in many applications in military, environmental, and health-related areas. These applications often include the monitoring of sensitive information such as enemy movement on the battlefield or the location of personnel in a building. Security is important in WSNs. However, WSNs suffer from many constraints, including low computation capability, small memory, limited energy resources, susceptibility to physical capture, and the use of insecure wireless communication channels. These constraints make security in WSNs a challenge. In this paper, we try to explore security issue in WSN. First, the constraints, security requirements and attacks with their corresponding countermeasures in WSNs are explained. Individual sensor nodes are subject to compromised security. An adversary can inject false reports into the networks via compromised nodes. Furthermore, an adversary can create a Gray hole by compromised nodes. If these two kinds of attacks occur simultaneously in a network, some of the existing methods fail to defend against those attacks. The Ad-hoc On Demand Distance (AODV) Vector scheme for detecting Gray-Hole attack and Statistical En-Route Filtering is used for detecting false report. For increasing security level, the Elliptic Curve Cryptography (ECC) algorithm is used. Simulations results obtain so far reduces energy consumption and also provide greater network security to some extent.

Wireless Sensor Network (WSN) technology has developed substantially over the past decade or so and now numerous solutions exist across a diverse range of innovative applications. The expanding Internet of Things (IoT) sector is becoming an ever more important aspect of modern technology and a key motivator for improving security and privacy in WSNs. Typically, WSN protocols form an integral part of the overall IoT infrastructure by enabling the sensor to access point communication links. These wireless links inherently encompass security challenges, frequently due to external interference and intrusions. As IoT applications incorporate WSNs in their architecture, the incentive to attack and compromise these WSNs escalates. Often, commercial off the shelf devices and standardized open-access protocols combine to achieve specific WSN deployments. Numerous WSN vulnerabilities exist, whilst attack approaches are abundant and change frequently. Thus, to ensure acceptable performance, safety and privacy in many IoT applications, the adopted WSN must be secure. This paper discusses IoT security and privacy, by evaluating a machine learning approach for interference detection focused entirely on analyzing received In-phase (I) and Quadrature-phase (Q) samples. Significantly, once an intrusion is detected, mitigation strategies can be implemented, thus emphasizing the requirement for interference detection. Random Forest is chosen as the machine learning classifier as it consists of a large number of individual decision trees operating as an ensemble. An intrusion detection system (IDS) is developed based on Matlab simulated ZigBee data as an initial insight into whether a real wireless data approach may be viable.

Now days, wireless technology is one of the center of attention for users and researchers. Wireless network is a network having large number of sensor nodes and hence called as “Wireless Sensor Network (WSN)”. WSN monitors and senses the environment of targeted area. The sensor nodes in WSN transmit data to the base station depending on the application. These sensor nodes communicate with each other and routing is selected on the basis of routing protocols which are application specific. Based on network structure, routing protocols in WSN can be divided into two categories: flat routing, hierarchical or cluster based routing, location based routing. Out of these, hierarchical or cluster based routing is becoming an active branch of routing technology in WSN. To allow base station to receive unaltered or original data, routing protocol should be energy-efficient and secure. To fulfill this, Hierarchical or Cluster base routing protocol for WSN is the most energy-efficient among other routing protocols. Hence, in this paper, we present a survey on different hierarchical clustered routing techniques for WSN. We also present the key management schemes to provide security in WSN. Further we study and compare secure hierarchical routing protocols based on various criteria.

In this article, we propose an intelligent hybrid model that combines deep learning with optimization techniques to enhance the security of wireless sensor networks (WSN) by detecting and preventing cyberattacks. In the rapidly expanding Internet landscape, security has become crucial due to the surge in Internet users. Various intrusion detection systems have been developed over time to detect and identify intruders using data processing methods. However, existing systems often fall short in achieving high detection accuracy. To address this, the article introduces an Enhanced Black Widow Optimization (EBWO) algorithm integrated with a Bidirectional Gated Recurrent Unit (BiGRU) and Attention Mechanism (ATTN) model for detecting malicious activities in IoT‐based WSNs. The EBWO algorithm optimizes the parameters of the BiGRU‐ATTN network, enhancing its performance. The proposed model's effectiveness is evaluated using various metrics and benchmarked against other popular deep learning algorithms. An experimental study is conducted using the WSN‐DS dataset to demonstrate that the proposed approach detects cyber threats in WSNs better than any other approach.