PAutoBotCatcher: A blockchain-based privacy-preserving botnet detector for Internet of Things

Lekssays, Ahmed; Landa, Luca; Carminati, Barbara; Ferrari, Elena

doi:10.1016/j.comnet.2021.108512

Cited by 12 publications

(13 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Secure SVM, a privacy-preserving SVM training scheme over blockchain-based encrypted IoT data, was used in this study [24]. Blockchain technology creates a secure and dependable data-sharing platform for numerous data sources, in which IoT data is encrypted and then stored on a distributed ledger [25]. The study [26] offers a unique Decentralized Blockchain-based Security (DeBlock-Sec) solution to address the security challenge in resource-constrained IoT environments.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Data Security in IoT Networks with Blockchain-Based Management and Adaptive Clustering Techniques

Kiran¹,

Mathivanan

Mahdal

et al. 2023

Mathematics

View full text Add to dashboard Cite

The rapid proliferation of smart devices in Internet of Things (IoT) networks has amplified the security challenges associated with device communications. To address these challenges in 5G-enabled IoT networks, this paper proposes a multi-level blockchain security architecture that simplifies implementation while bolstering network security. The architecture leverages an adaptive clustering approach based on Evolutionary Adaptive Swarm Intelligent Sparrow Search (EASISS) for efficient organization of heterogeneous IoT networks. Cluster heads (CH) are selected to manage local authentication and permissions, reducing overhead and latency by minimizing communication distances between CHs and IoT devices. To implement network changes such as node addition, relocation, and deletion, the Network Efficient Whale Optimization (NEWO) algorithm is employed. A localized private blockchain structure facilitates communication between CHs and base stations, providing an authentication mechanism that enhances security and trustworthiness. Simulation results demonstrate the effectiveness of the proposed clustering algorithm compared to existing methodologies. Overall, the lightweight blockchain approach presented in this study strikes a superior balance between network latency and throughput when compared to conventional global blockchain systems. Further analysis of system under test (SUT) behavior was accomplished by running many benchmark rounds at varying transaction sending speeds. Maximum, median, and lowest transaction delays and throughput were measured by generating 1000 transactions for each benchmark. Transactions per second (TPS) rates varied between 20 and 500. Maximum delay rose when throughput reached 100 TPS, while minimum latency maintained a value below 1 s.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Data Security in IoT Networks with Blockchain-Based Management and Adaptive Clustering Techniques

Kiran¹,

Mathivanan

Mahdal

et al. 2023

Mathematics

View full text Add to dashboard Cite

show abstract

“…When the traffic packets sent by this end device are received by the edge nodes, they can be downscaled using principal component analysis, after which a DDoS anomaly traffic detection model can be constructed. 18 Random forest (RF) is a classifier that trains and predicts samples through multiple trees. In machine learning, a random forest is a classifier that contains multiple decision trees, and its classification results depend on the classification method output by each tree.…”

Section: Random Forest-based Ddos Attack Detection Modelmentioning

confidence: 99%

Section: Blockchain‐based Joint Defense Mechanism For Ddos Attacksmentioning

confidence: 99%

AI‐enabled blockchain and SDN‐integrated IoT security architecture for cyber‐physical systems

Wang¹,

Zhang²,

Zhang³

2023

Adv Control Appl

View full text Add to dashboard Cite

To address the IoT security problem, in this paper we propose and evaluate the DDoS attack mitigation method based on blockchain, and construct a DDoS abnormal information detection and sharing model. The obtained experimental results show that when the number of decision trees increases, the training time of the DDoS attack detection model based on the RF model grows with a minimum trend of 14 s. The testing time is finally maintained at 1 s, and the recognition accuracy of DDoS attacks keeps improving, ultimately reaching over 99.8%. If the amount of DDoS abnormal traffic information exceeds 100 pieces and 2000 pieces, it only takes 0.1 and 5 s to sign the DDoS abnormal traffic information using ECDSA algorithm digitally. The signature verification only takes 0.1 and 9 s, respectively. And compared to conventional network physical system IoT security architecture, a network physical system IoT security architecture that integrates AI empowerment, blockchain, and SDN integration has a higher joint defense success rate. It can be explained that this scheme will be conducive to promoting joint defense against DDoS attacks and ensuring the security of the Internet of Things.

show abstract

“…Alharbi et al [20] suggested a Local-Global best Bat Algorithm for NNs (LGBA-NN) for choosing both hyperparameters and feature subsets for effectively identifying botnet attacks, acquired from 9 viable IoT devices attacked by 2 botnets: Gafgyt and Mirai. Lekssays et al [21] developed PAutoBotCatcher, a dynamic botnet identification model dependent upon community behavior analysis between peers controlled by various parameters. PAutoBotCatcher leverages BC to ensure transparency and immutability among all parameters.…”

Section: Related Workmentioning

confidence: 99%

Modeling of Botnet Detection Using Chaotic Binary Pelican Optimization Algorithm With Deep Learning on Internet of Things Environment

Alrowais,

Eltahir,

Aljameel

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Nowadays, there are ample amounts of Internet of Things (IoT) devices interconnected to the networks, and with technological improvement, cyberattacks and security threads, for example, botnets, are rapidly evolving and emerging with high-risk attacks. A botnet is a network of compromised devices that are controlled by cyber attackers, frequently employed to perform different cyberattacks. Such attack disrupts IoT evolution by disrupting services and networks for IoT devices. Detecting botnets in an IoT environment includes finding abnormal patterns or behaviors that might indicate the existence of these malicious networks. Several researchers have proposed deep learning (DL) and machine learning (ML) approaches for identifying and categorizing botnet attacks in the IoT platform. Therefore, this study introduces a Botnet Detection using the Chaotic Binary Pelican Optimization Algorithm with Deep Learning (BNT-CBPOADL) technique in the IoT environment. The main aim of the BNT-CBPOADL method lies in the correct detection and categorization of botnet attacks in the IoT environment. In the BNT-CBPOADL method, Z-score normalization is applied for pre-processing. Besides, the CBPOA technique is derived for feature selection. The convolutional variational autoencoder (CVAE) method is applied for botnet detection. At last, the arithmetical optimization algorithm (AOA) is employed for the optimal hyperparameter tuning of the CVAE algorithm. The experimental valuation of the BNT-CBPOADL technique is tested on a Bot-IoT database. The experimentation outcomes inferred the supremacy of the BNT-CBPOADL method over other existing techniques with maximum accuracy of 99.50%.

show abstract

PAutoBotCatcher: A blockchain-based privacy-preserving botnet detector for Internet of Things

Cited by 12 publications

References 35 publications

Enhancing Data Security in IoT Networks with Blockchain-Based Management and Adaptive Clustering Techniques

Enhancing Data Security in IoT Networks with Blockchain-Based Management and Adaptive Clustering Techniques

AI‐enabled blockchain and SDN‐integrated IoT security architecture for cyber‐physical systems

Modeling of Botnet Detection Using Chaotic Binary Pelican Optimization Algorithm With Deep Learning on Internet of Things Environment

Contact Info

Product

Resources

About