Augmentation in performance and security of WSNs for IoT applications using feature selection and classification techniques

Yadav, Rajiv; Indu, S.; Gupta, Daya

doi:10.1016/j.aej.2022.10.033

Cited by 21 publications

(8 citation statements)

References 34 publications

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“…Te main types of attacks in WSN networks are black hole attacks, gray hole attacks, fooding attacks, replay routing attacks, and wormhole attacks [39]. Te following four types of attacks are included in the NSL-KDD dataset: denial of service attacks (DoS) [40], snifng attacks (probe) [41], unauthorized access from a remote machine to a local machine (R2L) [42], and unauthorized access to local superuser (root) privileges (U2R) [43].…”

Section: Experimental Evaluation Methodsmentioning

confidence: 99%

Feature-Weighted Naive Bayesian Classifier for Wireless Network Intrusion Detection

2024

Security and Communication Networks

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Objective. Wireless sensor networks, crucial for various applications, face growing security challenges due to the escalating complexity and diversity of attack behaviours. This paper presents an advanced intrusion detection algorithm, leveraging feature-weighted Naive Bayes (NB), to enhance network attack detection accuracy. Methodology. Initially, a feature weighting algorithm is introduced to assign context-based weights to different feature terms. Subsequently, the NB algorithm is enhanced by incorporating Jensen–Shannon (JS) divergence, feature weighting, and inverse category frequency (ICF). Eventually, the improved NB algorithm is integrated into the intrusion detection model, and network event classification results are derived through a series of data processing steps applied to corresponding network traffic data. Results. The effectiveness of the proposed intrusion detection algorithm is evaluated through a comprehensive comparative analysis using the NSL-KDD dataset. Results demonstrate a significant enhancement in the detection accuracy of various attack types, including normal, denial of service (DoS), probe, remote-to-local (R2L), and user-to-root (U2R). Moreover, the proposed algorithm exhibits a lower false alarm rate compared to other algorithms. Conclusion. This paper introduces a wireless network intrusion algorithm that not only ensures improved detection accuracy and rate but also reduces the incidence of false detections. Addressing the evolving threat landscape faced by wireless sensor networks, this contribution represents a valuable advancement in intrusion detection technology.

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Section: Experimental Evaluation Methodsmentioning

confidence: 99%

Feature-Weighted Naive Bayesian Classifier for Wireless Network Intrusion Detection

2024

Security and Communication Networks

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“…The WSN has an issue of attacked possibility by a hacker to make a false diagnosis against reality's condition. The research that proposed by Yadav et al [15], is tried to detect pre-hacking that attempted to WSN peripherals by using an artificial intelligence of deep learning algorithm. Other research to secure the physical devices likes computing, network devices was also conducted [16].…”

Section: Study Literaturementioning

confidence: 99%

Low-Cost Security Monitoring System for Indoor Areas Based on The IoT

Sidharta

et al. 2023

INDJCSE

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The growth of the Internet of Things (IoT) to be implemented for smart homes still increases. In the smart home area, the IoT has a task to control every electricity automatically. Not only that, the IoT gives the convenience for users to control manually all the electricity in their homes remotely. One of the important things that makes the IoT concept very needed in smart homes can be used to monitor the environment through sensors. The sensor can also be utilized for security systems, especially to monitor the presence of intruders. In the previous research, several ways to provide more protection in the room are by changing the door lock from mechanical conventional to automatic. A Keypad module for typing passwords and biometric recognition was also proposed to secure the door. The room's monitoring system based-on camera for object detection was also proposed. By using the camera to detect an intruder, it needs huge computational resources. The detection times were also considered. To provide the fastest and low-cost model to detect the presence of intruders this study proposes a security monitoring system based on IoT composed of edge devices based on ESP8266 that are equipped with an ultrasonic sensor of HC-SR04 as an input sensor, and for the user Interface the based-on Java application is included. The ultrasonic sensor is used to measure a distance. When the intruder is assumed to pass through the front of the ultrasonic sensor, the edge device will read the change in distance and send the warning signal to the monitoring system based on Java. With a satisfactory result, this model can detect the presence of intruders with a speed of only 274.798 milliseconds.

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“…These techniques can adapt to changing network conditions and reduce power consumption when used together. Sensor nodes provide environmental parameters, battery status, and network behavior data to the AI-driven framework [1], [2].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Driven Power Optimization in IOT-Enabled Wireless Sensor Networks

Balasaheb Balkhande

2024

jes

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The widespread use of Wireless Sensor Networks (WSN) in Internet of Things (IoT) causes energy efficiency issues. This paper proposes an AI-based solution to this problem. The propose an AI-Driven Power Optimization framework for IoT-enabled WSN using Deep Q-Network (DQN) and Dynamic Voltage and Frequency Scaling (DVFS). These techniques can adapt to changing network conditions and reduce power consumption when used together. Sensor nodes provide environmental parameters, battery status, and network behavior data to the AI-driven framework DQN is implemented after data preprocessing to learn and make power management decisions using reinforcement learning. Neural network-driven agent operates in a state and action space. It optimizes energy use with rewards. Real-time hardware power adjustment is done using DVFS. DVFS precise control and AI-driven decision-making create a comprehensive power optimization strategy. AI adapts to new challenges and optimizes network lifespan by improving its power management policies. Experimental implementations of the proposed framework show significant energy savings, network lifespan extension, and QoS improvements. AI-Driven Power Optimization in IoT-enabled WSN is proven effective and flexible. This study shows the potential of AI, specifically DQN and DVFS, in IoT-enabled WSN. This AI-Driven Power Optimization framework addresses energy efficiency and improves IoT sensor networks. AI improves power optimization in IoT-enabled WSN making IoT deployments more sustainable and resilient.

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Augmentation in performance and security of WSNs for IoT applications using feature selection and classification techniques

Cited by 21 publications

References 34 publications

Feature-Weighted Naive Bayesian Classifier for Wireless Network Intrusion Detection

Feature-Weighted Naive Bayesian Classifier for Wireless Network Intrusion Detection

Low-Cost Security Monitoring System for Indoor Areas Based on The IoT

Artificial Intelligence Driven Power Optimization in IOT-Enabled Wireless Sensor Networks

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