A Performance Comparison of Machine Learning Approaches on Intrusion Detection Dataset

Manvith, Vaddeboyina Sri; Saraswathi, R. Vijaya; Vasavi, R.

doi:10.1109/icicv50876.2021.9388502

Cited by 18 publications

(3 citation statements)

References 22 publications

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“…Manvith et al [16] demonstrated the ability of ML techniques to identify various attacks to ensure network security. They analyzed and compared SVM, LR, and RF techniques to determine which one can be used to identify network attacks.…”

Section: Comparative Study Based On ML Classifiersmentioning

confidence: 99%

A Comparative Study of Machine Learning Algorithms for Intrusion Detection in IoT Networks

Benamor,

Seghir,

Djezzar

et al. 2023

RIA

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The pervasive threat of cyberattacks jeopardizes the security and privacy of the Internet of Things (IoT) landscape, spanning devices to networks. To counter these attacks, research has been directed towards the development of effective and appropriate countermeasures. Intrusion Detection Systems (IDSs), particularly those leveraging Machine Learning (ML) techniques for expedited attack detection, are currently recognized as some of the most potent solutions for preserving the integrity of the IoT environment. This study was conducted with the objective of evaluating the efficacy of supervised Machine Learning techniques, specifically, Random Forest (RF), Decision Trees (DT), and XGBoost classifiers, in detecting attacks within the IoT network. Chi-square (Chi2) and Mutual Information served as the employed Feature Selection Techniques. The research utilized two recent datasets for model evaluation. In pursuit of an optimal solution and high IDS model accuracy, a comparison of different techniques was undertaken across each stage of the ML workflow. The performance of the algorithms was assessed using the Edge-IIoT and BoTNeTIoT datasets, and the results from the two were compared. The impact of each workflow step on the model's accuracy was also examined. According to the performance metrics, the best results were achieved with the Mutual Information and XGBoost combination, outperforming both the Random Forest and Decision Tree classifiers. This study thus contributes to the ongoing efforts to strengthen IoT security through enhanced intrusion detection techniques.

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Section: Comparative Study Based On ML Classifiersmentioning

confidence: 99%

A Comparative Study of Machine Learning Algorithms for Intrusion Detection in IoT Networks

Benamor,

Seghir,

Djezzar

et al. 2023

RIA

View full text Add to dashboard Cite

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“…The three fundamental security considerations identified in Ref. [11] are confidentiality, integrity, and availability. As identified in Refs.…”

Section: Introductionmentioning

confidence: 99%

An End-Process Blockchain-Based Secure Aggregation Mechanism Using Federated Machine Learning

Mbonu,

Maple,

Epiphaniou

2023

Electronics

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Federated Learning (FL) is a distributed Deep Learning (DL) technique that creates a global model through the local training of multiple edge devices. It uses a central server for model communication and the aggregation of post-trained models. The central server orchestrates the training process by sending each participating device an initial or pre-trained model for training. To achieve the learning objective, focused updates from edge devices are sent back to the central server for aggregation. While such an architecture and information flows can support the preservation of the privacy of participating device data, the strong dependence on the central server is a significant drawback of this framework. Having a central server could potentially lead to a single point of failure. Further, a malicious server may be able to successfully reconstruct the original data, which could impact on trust, transparency, fairness, privacy, and security. Decentralizing the FL process can successfully address these issues. Integrating a decentralized protocol such as Blockchain technology into Federated Learning techniques will help to address these issues and ensure secure aggregation. This paper proposes a Blockchain-based secure aggregation strategy for FL. Blockchain is implemented as a channel of communication between the central server and edge devices. It provides a mechanism of masking device local data for secure aggregation to prevent compromise and reconstruction of the training data by a malicious server. It enhances the scalability of the system, eliminates the threat of a single point of failure of the central server, reduces vulnerability in the system, ensures security, and transparent communication. Furthermore, our framework utilizes a fault-tolerant server to assist in handling dropouts and stragglers which can occur in federated environments. To reduce the training time, we synchronously implemented a callback or end-process mechanism once sufficient post-trained models have been returned for aggregation (threshold accuracy achieved). This mechanism resynchronizes clients with a stale and outdated model, minimizes the wastage of resources, and increases the rate of convergence of the global model.

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“…Therefore, machine learning is devised for optimal resource allocations. 16,17 On the other hand, fewer resources are needed as it minimizes the cost of engaged resources. Thus, the allocation of resources is done using cloud-based software services, which needs to make a tradeoff between QoS and occupied resources cost.…”

Section: Introductionmentioning

confidence: 99%

Exponentially‐spider monkey optimization based allocation of resource in cloud

Kalpana

Prabhu²,

Polepally

et al. 2021

Int J of Intelligent Sys

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The increasing desire for distributed computing systems has attracted huge interest in memory and computing resources. The cloud provides on-demand access to provide a flexible allocation of resources for reliable services. Therefore, there should be a provision in which resources are accessible to request users to satisfy user needs. In classical techniques, the allocation of resources by satisfying power and Quality-of-Services is a challenging aspect. This paper devises a novel technique for optimal resource allocation, namely, Exponentially Spider Monkey Optimization (E-SMO). Here, the proposed E-SMO is devised by combining Exponential Weighted Moving Average and Spider Monkey Optimization (SMO). Besides, the fitness function is newly devised considering resource utilization and resource cost. After that, the cloud resources employ a switching strategy to reduce power consumption to prevent the switching of redundant servers. For optimal switching, the proposed E-SMO is utilized with other fitness factors that compute the number of applications assigned in the physical machine using the switching state is in OFF condition. Thus, the server switching model is incorporated to activate or deactivate the server when not in use for effective resources utilization. The proposed E-SMO algorithm outperformed other methods with the maximal resource

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A Performance Comparison of Machine Learning Approaches on Intrusion Detection Dataset

Cited by 18 publications

References 22 publications

A Comparative Study of Machine Learning Algorithms for Intrusion Detection in IoT Networks

A Comparative Study of Machine Learning Algorithms for Intrusion Detection in IoT Networks

An End-Process Blockchain-Based Secure Aggregation Mechanism Using Federated Machine Learning

Exponentially‐spider monkey optimization based allocation of resource in cloud

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