A low-complexity, energy-efficient data securing model for wireless sensor network based on linearly complex voice encryption mechanism of GSM technology

Khan, Muhammad Suleman; Khan, Noor M.; Khan, Ahmad; Aadil, Farhan; Tahir, Muhammad; Sardaraz, Muhammad

doi:10.1177/15501477211018623

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…The initial DDoS intrusion was recognized by the Computer Incident Advisory Capability in the 20th century [9]. Microsoft stated that the DDoS attacks raised by about 25%…”

Section: Motivationmentioning

confidence: 99%

An Effective Classification of DDoS Attacks in a Distributed Network by Adopting Hierarchical Machine Learning and Hyperparameters Optimization Techniques

Dasari,

Kaluri

2024

IEEE Access

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Data privacy is essential in the financial sector to protect client's sensitive information, prevent financial fraud, ensure regulatory compliance, and safeguard intellectual property. It has become a challenging task due to the increase in usage of the internet and digital transactions. In this scenario, DDoS attack is one of the major attacks that makes clients' privacy questionable. It requires effective and robust attack detection and prevention techniques. Machine Learning (ML) has the most effective approaches for employing cyber attack detection systems. It paves the way for a new era where human and scientific communities will benefit. This paper presents a hierarchical ML-based hyperparameteroptimization approach for classifying intrusions in a network. CICIDS 2017 standard dataset was considered for this work, Initially, data was preprocessed with the min-max scaling and SMOTE methods. The LASSO approach was used for feature selection, given as input to the hierarchical ML algorithms: XGboost, LGBM, CatBoost, random forest, and decision tree. All these algorithms are pretrained with hyperparameters to enhance the effectiveness of algorithms. Models performance was assessed in terms of recall, precision, accuracy, and F1-score metrics. Evaluated approaches have shown that the LGBM algorithm gives a proven performance in classifying DDoS attacks with 99.77% of classification accuracy.

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“…The initial DDoS intrusion was recognized by the Computer Incident Advisory Capability in the 20th century [9]. Microsoft stated that the DDoS attacks raised by about 25%…”

Section: Motivationmentioning

confidence: 99%

An Effective Classification of DDoS Attacks in a Distributed Network by Adopting Hierarchical Machine Learning and Hyperparameters Optimization Techniques

Dasari,

Kaluri

2024

IEEE Access

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“…Data security is a widely studied field in computing domain. Many applications use security for different purposes, including access control [15], network security [16], data security [17], availability of services [4], etc. Both symmetric and asymmetric approaches are used, according to the targeted domains.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine-Learning-Based DDoS Attack Detection Using Mutual Information and Random Forest Feature Importance Method

et al. 2022

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Cloud computing facilitates the users with on-demand services over the Internet. The services are accessible from anywhere at any time. Despite the valuable services, the paradigm is, also, prone to security issues. A Distributed Denial of Service (DDoS) attack affects the availability of cloud services and causes security threats to cloud computing. Detection of DDoS attacks is necessary for the availability of services for legitimate users. The topic has been studied by many researchers, with better accuracy for different datasets. This article presents a method for DDoS attack detection in cloud computing. The primary objective of this article is to reduce misclassification error in DDoS detection. In the proposed work, we select the most relevant features, by applying two feature selection techniques, i.e., the Mutual Information (MI) and Random Forest Feature Importance (RFFI) methods. Random Forest (RF), Gradient Boosting (GB), Weighted Voting Ensemble (WVE), K Nearest Neighbor (KNN), and Logistic Regression (LR) are applied to selected features. The experimental results show that the accuracy of RF, GB, WVE, and KNN with 19 features is 0.99. To further study these methods, misclassifications of the methods are analyzed, which lead to more accurate measurements. Extensive experiments conclude that the RF performed well in DDoS attack detection and misclassified only one attack as normal. Comparative results are presented to validate the proposed method.

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A reinforcement learning based routing protocol for software-defined networking enabled wireless sensor network forest fire detection

Moussa

Nurellari

Azbeg

et al. 2023

Future Generation Computer Systems

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A low-complexity, energy-efficient data securing model for wireless sensor network based on linearly complex voice encryption mechanism of GSM technology

Cited by 4 publications

References 29 publications

An Effective Classification of DDoS Attacks in a Distributed Network by Adopting Hierarchical Machine Learning and Hyperparameters Optimization Techniques

An Effective Classification of DDoS Attacks in a Distributed Network by Adopting Hierarchical Machine Learning and Hyperparameters Optimization Techniques

Machine-Learning-Based DDoS Attack Detection Using Mutual Information and Random Forest Feature Importance Method

A reinforcement learning based routing protocol for software-defined networking enabled wireless sensor network forest fire detection

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