Sequential Pattern Artificial Neural Network (ANN) Method for Predicting Attacker Transaction in Wireless Sensor Network (WSN)

The rapid growth of the Internet and information technologies and also the diminution of the price of hardware components like wireless sensors leads to the fast growth in Wireless Sensor Network (WSN). The WSN is a bunch/group of sensors that are located across the area, in order to monitor and determine environmental conditions such as temperature, humidity, vibrations, wind, water levels, pollution levels. WSNs are susceptible to major attacks like Denial of Service (DOS), Wormhole attack, Sinkhole attack, etc. The main threat to the WSN is because of the broadcast nature of the nodes in the network. Therefore, the security of WSNs is the essential part that must be done. Hence, to overcome these problems or threats, we are trying to detect it using AI technology. With the expanding fields of Machine Learning and Deep Learning, we can apply various algorithms in order to classify different types of attacks. Once we detect the attack properly, we can prevent it accordingly. We are using WSN-DS. It has 4 classes of attacks which are Grayhole, Blackhole, TDMA(Scheduling), and Flooding which comes under the category of DOS attacks. In this chapter, we have analysed and compared the accuracies of 5 main machine learning classification algorithms. Also, we have analysed the 1 deep learning algorithm. The ANN (Artificial Neural Network) and 5 machine learning algorithms have been trained on the dataset. Furthermore, we have used K-fold cross-validation to get even more accurate predictions. After analysis of these algorithms, we came to the point that, the Machine Learning algorithms like Random Forest, Support Vector Machines and Deep Learning algorithm, namely, Artificial Neural Network can help us for detecting intrusions in the system or network. This will help researchers in designing their own machine learning model on top of our suggested model.

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.

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

Wireless sensor networks (WSNs) are gaining popularity in a variety of applications, including smart homes, industrial facilities, and environmental monitoring systems. However, using WSNs exposes them to a variety of attacks that might have major effects, such as data loss, privacy breaches, and safety issues. As a result, understanding the sorts of threats and detection/prevention strategies in WSNs is critical to ensuring their security, especially from an educational perspective. We present an overview of the sorts of attacks that can occur in WSNs, including denial of service attacks, spoofing attacks, jamming attacks, physical assaults, and node compromise attacks. We also examine encryption, authentication and authorization procedures, intrusion detection systems, and physical security measures as methods for detecting and blocking attacks in WSNs. We examine the benefits and drawbacks of each strategy and give case studies to show their efficacy. Finally, we propose future research directions, such as the development of more effective intrusion detection systems and the enhancement of physical security measures. We can assure the security and reliability of WSNs in diverse applications by knowing the types of attacks and detection/prevention mechanisms.

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.

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.

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.

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.

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 (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.

The intrusion prediction for wireless sensor networks (WSNs) is an unresolved problem. Hence, the current intrusion detection schemes cannot provide enough security for WSNs, which poses a number of security challenges in WSNs. In many mission-critical applications, such as battle field, even though the intrusion detection systems (IDSs) without prediction capability could detect the malicious activities afterwards, the damages to the WSNs have been generated and could hardly be restored. In addition, sensor nodes usually are resource constrained, which limits the direct adoption of expensive intrusion prediction algorithm. To address the above challenges, we propose an intelligent intrusion prediction scheme that is able to enforce accurate intrusion prediction. The proposed scheme exploits a novel three-layer brain-like hierarchical learning framework, tailors, and adapts it for WSNs with both performance and security requirements. The implementation system of the proposed scheme is designed based on agent technology. Moreover, an attack experiment is done for getting training and test data set. Experiment results show that the proposed scheme has several advantages in terms of efficiency of implementation and high prediction rate. To our best knowledge, this paper is the first to realize intrusion prediction for WSNs.

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.

Wireless sensor networks (WSNs) are gaining more and more interest in the research community due to their unique characteristics and have a lots of interesting applications. Besides energy consumption security of WSN is being a critical issue nowadays. This is because WSNs are easily effected by various types of attacks and node compromises so they require security mechanisms to defend against them. An intrusion detection system (IDS) is one such solution to the problem. There are many research works focused in the area of signature based anomaly based IDS. Based on these malicious activities there is a need of addressing a new or modified versions of IDS algorithm. The proposed algorithm, abbreviated as AD(anomaly detection) algorithm has dedicated procedures for secure cluster formation, periodic re-clustering, and efficient cluster member monitoring and then the detection of different attacks. The performance of AD algorithm in identifying and detecting intrusions using a rule-based anomaly detection scheme is studied via simulations.

Wireless sensor networks have become integral components of the monitoring systems for critical infrastructures such as the power grid or residential microgrids. Therefore, implementation of robust Intrusion Detection Systems (IDS) at the sensory data aggregation stage has become of paramount importance. Key performance targets for IDS in these environments involve accuracy, precision, and the receiver operating characteristics which is a function of the sensitivity and the ratio of false alarms. Furthermore, the interplay between machine learning and networked systems has led to promising opportunities, particularly for the system level security of wireless sensor networks. Pursuant to these, in this paper, we propose Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS) for wirelessly connected sensor clusters that monitor critical infrastructures. The proposed ASCH-IDS mechanism is built on a hybrid IDS framework, and transforms the previous work by continuously monitoring the behavior of the receiver operating characteristics, and adaptively directing the incoming packets at a sensor cluster towards either misuse detection or anomaly detection module. We evaluate the proposed mechanism by introducing real attack data sets into simulations, and show that our proposal performs at 98.9% detection rate and approximately 99.80% overall accuracy to detect known and unknown malicious behavior in the sensor network.

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