Comparative Review of the Intrusion Detection Systems Based on Federated Learning: Advantages and Open Challenges

Doynikova, Elena; Novikova, Evgenia; Shulepov, Anton

doi:10.3390/a15070247

Cited by 28 publications

(7 citation statements)

References 70 publications

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“…A blockchainenabled intrusion detection and prevention system is proposed which enhances ZTA onto endpoints. Fedorchenko et al [41] reviewed existing federated learning-based IDS solutions and studied their advantages as well as the open challenges they still face. The architecture of the proposed intrusion detection system and the method used to model data partitioning across clients are analyzed with emphasis.…”

Section: Related Workmentioning

confidence: 99%

Internet of Things Intrusion Detection System Based on Convolutional Neural Network

Yin¹,

Shi²,

Dong³

et al. 2023

Computers, Materials &Amp; Continua

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In recent years, the Internet of Things (IoT) technology has developed by leaps and bounds. However, the large and heterogeneous network structure of IoT brings high management costs. In particular, the low cost of IoT devices exposes them to more serious security concerns. First, a convolutional neural network intrusion detection system for IoT devices is proposed. After cleaning and preprocessing the NSL-KDD dataset, this paper uses feature engineering methods to select appropriate features. Then, based on the combination of DCNN and machine learning, this paper designs a cloud-based loss function, which adopts a regularization method to prevent overfitting. The model consists of one input layer, two convolutional layers, two pooling layers and three fully connected layers and one output layer. Finally, a framework that can fully consider the user's privacy protection is proposed. The framework can only exchange model parameters or intermediate results without exchanging local individuals or sample data. This paper further builds a global model based on virtual fusion data, so as to achieve a balance between data privacy protection and data sharing computing. The performance indicators such as accuracy, precision, recall, F1 score, and AUC of the model are verified by simulation. The results show that the model is helpful in solving the problem that the IoT intrusion detection system cannot achieve high precision and low cost at the same time.

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Section: Related Workmentioning

confidence: 99%

Internet of Things Intrusion Detection System Based on Convolutional Neural Network

Yin¹,

Shi²,

Dong³

et al. 2023

Computers, Materials &Amp; Continua

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“…After completing local training, the device transmits model changes-specifically weights and gradients-to a central server, rather than sending raw data [16]. This approach ensures that confidential information remains in its original location, effectively mitigating various privacy risks commonly associated with traditional data centralization [25]. The model updates from all participating devices are consolidated on the central server to create an enhanced global model that incorporates insights derived from all the decentralized data sources.…”

Section: Typical Federated Training Processmentioning

confidence: 99%

“…The analysis examined evaluation variables related to IoT, particularly concerning FL, and identified and dis-cussed prospects and unresolved issues pertaining to FL-based IoT. The authors of [25] also provided an overview and comparison of six studies that use FL to enhance IDS effectiveness for IoT. In the absence of specific datasets for assessing FL, the authors emphasized data partitioning modeling among clients.…”

Section: Related Surveysmentioning

confidence: 99%

Federated Learning for Intrusion Detection Systems in Internet of Vehicles: A General Taxonomy, Applications, and Future Directions

Alsamiri,

Alsubhi

2023

Future Internet

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In recent years, the Internet of Vehicles (IoV) has garnered significant attention from researchers and automotive industry professionals due to its expanding range of applications and services aimed at enhancing road safety and driver/passenger comfort. However, the massive amount of data spread across this network makes securing it challenging. The IoV network generates, collects, and processes vast amounts of valuable and sensitive data that intruders can manipulate. An intrusion detection system (IDS) is the most typical method to protect such networks. An IDS monitors activity on the road to detect any sign of a security threat and generates an alert if a security anomaly is detected. Applying machine learning methods to large datasets helps detect anomalies, which can be utilized to discover potential intrusions. However, traditional centralized learning algorithms require gathering data from end devices and centralizing it for training on a single device. Vehicle makers and owners may not readily share the sensitive data necessary for training the models. Granting a single device access to enormous volumes of personal information raises significant privacy concerns, as any system-related problems could result in massive data leaks. To alleviate these problems, more secure options, such as Federated Learning (FL), must be explored. A decentralized machine learning technique, FL allows model training on client devices while maintaining user data privacy. Although FL for IDS has made significant progress, to our knowledge, there has been no comprehensive survey specifically dedicated to exploring the applications of FL for IDS in the IoV environment, similar to successful systems research in deep learning. To address this gap, we undertake a well-organized literature review on IDSs based on FL in an IoV environment. We introduce a general taxonomy to describe the FL systems to ensure a coherent structure and guide future research. Additionally, we identify the relevant state of the art in FL-based intrusion detection within the IoV domain, covering the years from FL’s inception in 2016 through 2023. Finally, we identify challenges and future research directions based on the existing literature.

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“…The test statistic H follows a chi-square distribution with degrees of freedom equal to the number of groups minus 1(df = k-1), where k is the number of groups being compared. The significance of the test can be determined by comparing the obtained test statistic with the crit ical value fro m the chi-square distribution with the appropriate degrees of freedom [24].…”

Section: Kruskal -Wallis Testmentioning

confidence: 99%

A Novel Paradigm for IoT Security: ResNet-GRU Model Revolutionizes Botnet Attack Detection

2023

IJACSA

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The rapid proliferation of the Internet of Things (IoT) has engendered substantial security apprehensions, chiefly due to the emergence of botnet attacks. This research study delves into the realm of Intrusion Detection Systems (IDS ) by leveraging the IoT23 dataset, with a specific emphasis on the intricate domain of IoT at the network's edge. The evolution of edge computing underscores the exigency for tailored security solutions. An array of statistical methodologies, encompassing ANOVA, Kruskal-Wallis, and Friedman tests, is systematically employed to illuminate the evolving trends across multiple facets of the study. Given the intricacies entailed in feature selection within edge environments, Chi-square analyses, Recursive Feature Elimination (RFE), and Lasso-based techniques are strategically harnessed to unearth meaningful feature subsets. A meticulous evaluation encompassing 19 classifiers, meticulously selected from both machine learning (ML) and deep learning (DL) paradigms, is rigorously conducted. Initial findings underscore the potential of the Gated Recurrent Unit (GRU) model, especially when coupled with intrinsic lasso-based feature selection. This promising outcome catalyzes the formulation of an ensemble approach that harnesses multiple LassoCV models, aimed at amplifying feature selection proficiency. Furthermore, an optimized ResNet-GRU model emerges from the fusion of the GRU and ResNet architectures, with the objective of augmenting classification performance. In response to mounting concerns regarding data privacy at the edge, a resilient federated learning ecosystem is meticulously crafted. The seamless integration of the optimized ResNet-GRU model into this framework facilitates the employment of FedAvg, a widely acclaimed federated learning methodology, to adeptly navigate the intricacies associated with data sharing challenges. A comprehensive performance evaluation is undertaken, wherein the ResNet-GRU model is benchmarked against FedAvg and a diverse array of other federated learning algorithms, including FedProx and Fed+. This extensive comparati ve analysis encompasses a spectrum of performance metrics and processing time benchmarks, shedding comprehensive light on the capabilities of the model.

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Comparative Review of the Intrusion Detection Systems Based on Federated Learning: Advantages and Open Challenges

Cited by 28 publications

References 70 publications

Internet of Things Intrusion Detection System Based on Convolutional Neural Network

Internet of Things Intrusion Detection System Based on Convolutional Neural Network

Federated Learning for Intrusion Detection Systems in Internet of Vehicles: A General Taxonomy, Applications, and Future Directions

A Novel Paradigm for IoT Security: ResNet-GRU Model Revolutionizes Botnet Attack Detection

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