Game Theoretic Framework for Reputation-Based Distributed Intrusion Detection

Bradai, Amira; Afifi, Hossam

doi:10.1109/socialcom.2013.84

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…For a single data source, we can use a collective measure of trustworthiness (in the sense of reliability) based on the referrals or ratings from members in a community to evaluate the reputation [59]. PageRank is another method for reputation measurement [60]. If we generate the dataset by data fusion of multiple data sources, an "opinion" (a metric of the degree of belief) can be generated to represent the uncertainty in the aggregated result.…”

Section: Dataset Quality Principles and Dimensionsmentioning

confidence: 99%

Data Curation and Quality Assurance for Machine Learning-based Cyber Intrusion Detection

Chen,

Tran,

Thumati

et al. 2021

Preprint

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Intrusion detection is an essential task in the cyber threat environment. Machine learning and deep learning techniques have been applied for intrusion detection. However, most of the existing research focuses on the model work but ignores the fact that poor data quality has a direct impact on the performance of a machine learning system. More attention should be paid to the data work when building a machine learning-based intrusion detection system. This article first summarizes existing machine learning-based intrusion detection systems and the datasets used for building these systems. Then the data preparation workflow and quality requirements for intrusion detection are discussed. To figure out how data and models affect the machine learning performance, we conducted experiments on 11 HIDS datasets using seven machine learning models and three deep learning models. The experimental results show that BERT and GPT were the best algorithms for HIDS on all of the datasets. However, the performance on different datasets vary, indicating the differences between the data quality of these datasets. We then evaluate the data quality of the 11 datasets based on quality dimensions proposed in this paper to determine the best characteristics that a HIDS dataset should possess in order to yield the best possible result. This research initiates a data quality perspective for researchers and practitioners to improve the performance of machine learning-based intrusion detection.

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Section: Dataset Quality Principles and Dimensionsmentioning

confidence: 99%

Data Curation and Quality Assurance for Machine Learning-based Cyber Intrusion Detection

Chen,

Tran,

Thumati

et al. 2021

Preprint

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“…For self-organizing ad hoc networks, some nodes may be malicious and how to detect the malicious actions is an important work. Some strategic games are presented to stimulate the cooperation among distinct regular nodes, based on which the hidden malicious nodes can be detected [16][17][18][19][20][21]. In [22], a two-player Stackelberg stochastic game is analyzed for achieving the best response against the intrusion.…”

Section: Introductionmentioning

confidence: 99%

Game Theoretical Method for Anomaly-Based Intrusion Detection

Wang

et al. 2020

Security and Communication Networks

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In this paper, the game theoretical analysis method is presented to provide optimal strategies for anomaly-based intrusion detection systems (A-IDS). A two-stage game model is established to represent the interactions between the attackers and defenders. In the first stage, the players decide to do actions or keep silence, and in the second stage, attack intensity and detection threshold are considered as two important strategic variables for the attackers and defenders, respectively. The existence, uniqueness, and explicit computation of the Nash equilibrium are analyzed and obtained by considering six different scenarios, from which the optimal detection and attack actions are provided. Numerical examples are provided to validate our theoretical results.

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