Stochastic Gradient Descent Intrusions Detection for Wireless Sensor Network Attack Detection System Using Machine Learning

Saleh, Hadeel M.; Marouane, Hend; Fakhfakh, Ahmed

doi:10.1109/access.2023.3349248

Cited by 11 publications

(3 citation statements)

References 38 publications

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“…The initialization of each

in spider monkey optimization is as follows:

where

and

are upper and lower bounds in

direction for

and S (0, 1) indicates a random amount between the range [0, 1]. Initialization stage: the Bernoulli procedure is employed in the first phase of the SMO method to randomly initialize a population of N spider monkeys (SM) [ 36 ].

where

is the

dimension of

spider monkey, a random number distributed uniformly within the interval [0, 1], and prob , a probability with a value of 0.5.…”

Section: Proposed Modelmentioning

confidence: 99%

See 1 more Smart Citation

A Hybrid Approach for Energy Consumption and Improvement in Sensor Network Lifespan in Wireless Sensor Networks

Ullah,

Khan,

Mohy-ud-din

et al. 2024

Sensors

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In this paper, we propose an improved clustering algorithm for wireless sensor networks (WSNs) that aims to increase network lifetime and efficiency. We introduce an enhanced fuzzy spider monkey optimization technique and a hidden Markov model-based clustering algorithm for selecting cluster heads. Our approach considers factors such as network cluster head energy, cluster head density, and cluster head position. We also enhance the energy-efficient routing strategy for connecting cluster heads to the base station. Additionally, we introduce a polling control method to improve network performance while maintaining energy efficiency during steady transmission periods. Simulation results demonstrate a 1.2% improvement in network performance using our proposed model.

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“…The initialization of each

in spider monkey optimization is as follows:

where

and

are upper and lower bounds in

direction for

where

is the

dimension of

spider monkey, a random number distributed uniformly within the interval [0, 1], and prob , a probability with a value of 0.5.…”

Section: Proposed Modelmentioning

confidence: 99%

“…where Q minb and Q maxb are upper and lower bounds in bth direction for Q a and S (0, 1) indicates a random amount between the range [0, 1]. Initialization stage: the Bernoulli procedure is employed in the first phase of the SMO method to randomly initialize a population of N spider monkeys (SM) [36].…”

Section: Proposed Modelmentioning

confidence: 99%

A Hybrid Approach for Energy Consumption and Improvement in Sensor Network Lifespan in Wireless Sensor Networks

Ullah,

Khan,

Mohy-ud-din

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Another group of researchers worked on intrusion detection of IoT sensors. They mainly focused on the Gaussian Nave Bayes (GNB) and Stochastic Gradient Descent (SGD) algorithms to tackle the intrusions [ 18 ]. S. Salmi et al [ 19 ] explained the reason for the usage of a wide range of sensors in different fields and why these are vulnerable to security issues.…”

Section: Related Workmentioning

confidence: 99%

Automated Sensor Node Malicious Activity Detection with Explainability Analysis

Zubair,

Janicke,

Mohsin

et al. 2024

Sensors

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Cybersecurity has become a major concern in the modern world due to our heavy reliance on cyber systems. Advanced automated systems utilize many sensors for intelligent decision-making, and any malicious activity of these sensors could potentially lead to a system-wide collapse. To ensure safety and security, it is essential to have a reliable system that can automatically detect and prevent any malicious activity, and modern detection systems are created based on machine learning (ML) models. Most often, the dataset generated from the sensor node for detecting malicious activity is highly imbalanced because the Malicious class is significantly fewer than the Non-Malicious class. To address these issues, we proposed a hybrid data balancing technique in combination with a Cluster-based Under Sampling and Synthetic Minority Oversampling Technique (SMOTE). We have also proposed an ensemble machine learning model that outperforms other standard ML models, achieving 99.7% accuracy. Additionally, we have identified the critical features that pose security risks to the sensor nodes with extensive explainability analysis of our proposed machine learning model. In brief, we have explored a hybrid data balancing method, developed a robust ensemble machine learning model for detecting malicious sensor nodes, and conducted a thorough analysis of the model’s explainability.

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Enhancing agricultural wireless sensor network security through integrated machine learning approaches

Sharma,

Bhardwaj,

Kaushik

2024

Security and Privacy

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Wireless sensor network (WSN) works with a collection of multiple sensor nodes to fetch the data from the deployed environment to fulfill the application whether it is agricultural monitoring, industrial monitoring, etc. The agricultural region can be monitored by deploying sensor nodes to multiple verticals where continuous human presence is not feasible. These devices are equipped with limited resources and are easily vulnerable to various cyber‐attacks. The attacker can hack the sensor nodes to steal critical information from WSN devices. The cluster heads in the WSN play a vital role in the process of routing data packets and attackers launch malicious codes through sender nodes to hack or damage the cluster heads to shut down the entire deployed network of agricultural regions. This research paper proposes a framework to improve the security of WSNs by providing a shield to the cluster heads of the network using machine learning techniques. The experimental study of the paper includes the comparative analysis of three machine learning techniques decision tree classifier, Gaussian Naïve Bayes, and random forest classifier for predicting WSN attacks like flooding, gray hole, blackhole, and TDMA that are deployed to support the proposed WSN security framework on the attack dataset. The random forest classifier achieves an accuracy of 98%, Precision of 97.6%, Recall of 97.6%, and F1 score of 97.8% which is the maximum among the deployed machine learning techniques.

show abstract

Stochastic Gradient Descent Intrusions Detection for Wireless Sensor Network Attack Detection System Using Machine Learning

Cited by 11 publications

References 38 publications

A Hybrid Approach for Energy Consumption and Improvement in Sensor Network Lifespan in Wireless Sensor Networks

A Hybrid Approach for Energy Consumption and Improvement in Sensor Network Lifespan in Wireless Sensor Networks

Automated Sensor Node Malicious Activity Detection with Explainability Analysis

Enhancing agricultural wireless sensor network security through integrated machine learning approaches

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