A Framework for Formal Analysis of Anonymous Communication Protocols

Yang, Ke; Xiao, Meihua

doi:10.1155/2022/4659951

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…Yang et al [21] presented a labeled transition framework to offer operational semantics for security protocols; the transition relation was specified by the transition rules, including the create, transmit, and receive rules. A formal explanation of the invader model in this framework is also provided.…”

Section: Related Workmentioning

confidence: 99%

“…Table 2 presents the comparison of the references from the literature survey for their research features. X X Al-Garadi et al [15] X X X Zhou et al [17] X X X Verma et al [14] X X Wang et al [20] X X Bharany et al [23] X X X Yahuza et al [27] X X Paredes et al [29] X X X Yang et al [21] X X X Yin et al [22] Kaur et al [24] X X Bharany et al [26] X X Bharany et al [28] X X X Bharany et al [30] X X X Shuaib et al [31] X X X…”

Section: Related Workmentioning

confidence: 99%

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Comparison of IoT Communication Protocols Using Anomaly Detection with Security Assessments of Smart Devices

et al. 2022

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The authors implemented an attack scenario that involved simulating attacks to compromise node and sensor data. This research proposes a framework with algorithms that generates automated malicious commands which conform to device protocol standards and bypass compromise detection. The authors performed attack-detection testing with three different home setup simulations and referred to Accuracy of Detection, Ease of Precision, and Attack Recall, with the F1-Score as the parameter. The results obtained for anomaly detection of IoT logs and messages used K-Nearest Neighbor, Multilayer Perceptron, Logistic Regression, Random Forest, and linear Support Vector Classifier models. The attack results presented false-positive responses with and without the proposed framework and false-negative responses for different models. This research calculated Precision, Accuracy, F1-Score, and Recall as attack-detection performance models. Finally, the authors evaluated the performance of the proposed IoT communication protocol attack framework by evaluating a range of anomalies and compared them with the maliciously generated log messages. IoT Home #1 results in which the model involving an IP Camera and NAS device traffic displayed 97.7% Accuracy, 96.54% Precision, 97.29% Recall, and 96.88% F1-Score. This demonstrated that the model classified the Home #1 dataset consistently.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Comparison of IoT Communication Protocols Using Anomaly Detection with Security Assessments of Smart Devices

et al. 2022

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Tor network anonymity evaluation based on node anonymity

Cui,

Huang,

Meng

et al. 2023

Cybersecurity

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In order to address the shortcomings of traditional anonymity network anonymity evaluation methods, which only analyze from the perspective of the overall network and ignore the attributes of individual nodes, we proposes a dynamic anonymity model based on a self-built anonymous system that combines node attributes, network behavior, and program security monitoring. The anonymity of evaluation nodes is assessed based on stable intervals and behavior baselines defined according to their normal operating status. The anonymity of the network is evaluated using an improved normalized information entropy method that refines anonymity evaluation to the anonymity of each node and expands the dimensionality of evaluation features. This paper compares the effectiveness of our proposed method with static framework information entropy and single indicator methods by evaluating the degree of anonymity provided by a self-built Tor anonymous network under multiple operating scenarios including normal and under attack. Our approach utilizes dynamically changing network anonymity based on multiple anonymous attributes and better reflects the degree of anonymity in anonymous systems.

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Comparison of IoT Communication Protocols Using Anomaly Detection with Security Assessments of Smart Devices

Bhardwaj

2024

Smart Home and Industrial IoT Devices: Critical Perspectives on Cyberthreats, Frameworks and Protocols

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The authors implemented an attack scenario simulating attacks to compromise node and sensor data. This research proposes a framework with algorithms that generate automated malicious commands, which conform to device protocol standards and bypass compromise detection. The authors performed attack detection testing with three different home setup simulations and referred to accuracy of detection, ease of precision, and attack recall, with F1-score as the parameters. The results obtained for anomaly detection of IoT logs and messages used K-nearest neighbor, multi-layer perceptron, logistic regression, random forest, and linear support vector classifier models. The attack results presented false-positive responses with and without the proposed framework and false-negative responses for different models. This research calculated precision, accuracy, F1-score, and recall as attack detection performance models. Finally, the authors evaluated the performance of the proposed IoT communication protocol attack framework by evaluating a range of anomalies and compared them with the maliciously generated log messages. IoT Home #1 in which the model involved IP Camera and NAS device traffic displayed 97.7% Accuracy, 96.54% Precision, 97.29% Recall, and 96.88% F-1 Score. This demonstrated the model classified the Home #1 dataset consistently.

show abstract

A Framework for Formal Analysis of Anonymous Communication Protocols

Cited by 3 publications

References 26 publications

Comparison of IoT Communication Protocols Using Anomaly Detection with Security Assessments of Smart Devices

Comparison of IoT Communication Protocols Using Anomaly Detection with Security Assessments of Smart Devices

Tor network anonymity evaluation based on node anonymity

Comparison of IoT Communication Protocols Using Anomaly Detection with Security Assessments of Smart Devices

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